BR112018074917B1 - IMAGE GENERATION SYSTEM AND METHOD TO GENERATE THE IMAGE OF AN OBJECT AND GENERATE A MEASUREMENT OF AUTHENTICITY OF THE OBJECT - Google Patents

Abstract

The invention relates to an imaging system (200) for imaging and generating a measure of authenticity of an object (10) comprising a dispersive imaging arrangement (30) and an image sensor arrangement (60). These are positioned so that when electromagnetic radiation (20) from the object (10) illuminates the dispersive imaging array (30), the radiation splits in different directions to at least one non-scattered part (40 ) and a dispersed part (50), and these have their images generated by the image sensor arrangement (60). The image generation system (200) is configured to then generate a measure of authenticity of the object (10) depending on at least one relationship between the imaged dispersed part, the non-scattered imaged part, and the spectral information of reference. The invention also relates to imaging methods, computer programs, computer program products and storage media.

Description

Technical Field

[0001] The present invention relates to imaging systems for generating an image of an object and for generating a measure of authenticity of the object. The invention also relates to methods, computer programs, computer program products and storage media for the same purposes.

Fundamentals

[0002] The supply of counterfeit goods in a particular market causes a loss of revenue for the manufacturers of the corresponding authentic goods, as well as for governments when these goods are subject to taxation. End users are adversely affected by counterfeit goods because they are supplied with inferior quality products, which can be dangerous even for the health of the end user of certain products, such as when medicines are subject to counterfeiting. The manufacturer of high-quality authentic products will consequently suffer a loss of clientele.

[0003] Various anti-counterfeiting measures have been proposed in the prior art with respect, for example, to alcoholic and non-alcoholic beverages (beer, wine, liquor, soft drinks, etc.), tobacco products (cigarettes, cigars, loose tobacco, etc. ), medicinal products, perfumes and normally products subject to special taxes. It is known to use sophisticated printing techniques to make the package design as difficult as possible to reproduce.

[0004] It is also known to make use of fluorescent items that look one way under ambient light and look a different way under ultraviolet (UV) radiation. Holographic images of varying degrees of complexity are also used. Other popular techniques include watermarking technology, printed gravure lines, and marks that change color depending on the heat applied to the mark.

[0005] Document CN 202533362 U relates to a device for identifying the authenticity of printed matter based on a multispectral imaging technology. The device comprises a multispectral imager for performing multispectral scanning on a test sample (the multispectral imager comprising a light source, a grid and an image detector), a spectral data processor for comparing spectral data obtained from the scan with spectral data from a standard sample, and a data server used to store the spectral data from the standard sample. If the difference between the spectral data obtained from the scan and the spectral data of a standard sample exceeds a defined threshold value, the test sample will be judged as false. Otherwise, it is judged as authentic.

[0006] The prior art also includes several imaging spectrometers used for scientific observations. These systems generally aim to obtain high-resolution spatial and spectral information about all regions of a scene or object. In particular, image spectrometers are image generators that allow the extraction of the three-dimensional spectral irradiance map of a planar object (spatial spectral data cube) l(x, y, À) through the use of two-dimensional array detectors, such such as CCD (ie, charge-coupled device) or CMOS (ie, complementary metal oxide semiconductor) sensors. One dimension is wavelength and the other two comprise spatial information.

[0007] There are two main categories of spectral imagers: spectral raster imagers and instantaneous spectral imagers. A review of the multi- and hyperspectral imager can be found, for example, in Hagen et al, “Snapshot advantage: a review of the light collection improvement for parallel high-dimensional measurement systems”, Optical Engineering 51(11), 111702 (2012 ), and Hagen et al, “Review of snapshot spectral imaging technologies”, Optical Engineering 52(9), 090901(September, 2013).

[0008] One way to acquire three-dimensional information through a two-dimensional sensor is the sequential acquisition of images through a mechanically digitized wheel or array of optical filters installed in front of an image generator. Another possibility is to adjust the central transmission band of a filter, such as a multistage liquid crystal filter, an acoustic-optical filter or a Fabry-Perot interferometer. These two examples fall under the category of spectral raster imagers.

[0009] There are instantaneous spectral imagers capable of simultaneous acquisition of images in different spectral bands through an array of filters and an example is the camera with multi-aperture filters (MAFC), using arrays of lenses with a focal plane detector.

[0010] Transmission diffraction gratings based on instantaneous spectral imaging systems also exist. One example is the Computed Tomography Imaging Spectrometer (CTIS), which uses multiple cross-transmission gratings or specifically designed Kinoform gratings capable of dispersing multiple spectral orders around a zero order. Computed tomography algorithms must be used to reconstruct the spectral radiance of the object.

[0011] Another example with transmission diffraction grating is the aperture coded instantaneous spectral image generator (CASSI), which uses complex masks to shade some parts of the object image in order to facilitate spectrum extraction.

[0012] The integral field image spectrometers also have diffraction gratings to disperse the light. In these configurations, the image is sliced by different methods to fit into an entrance slit of a conventional spectrometer to extract spectra. Image slicing can be achieved by using the fiber bundle and distributing individual fibers into an entrance slit, or by splitting the aperture using a lense array.

[0013] Fourier transform imaging spectrometers also exist in a separate category. An interferometer is scanned to obtain images at different optical path differences and the spectra are reconstructed by Fourier transform. Some settings rely on the lense array to do aperture splitting and analyze averaged spectra in different parts of the image/object. One example is the multiple-image Fourier transform spectrometer (MIFTS) based on a Michelson interferometer. Another distinct example is the Instantaneous Hyperspectral Imaging (SHIFT) Fourier Transform spectrometer, which uses the pair of birefringent prisms to obtain different optical path lengths.

[0014] In view of the above, there is a need to provide fast, simple, inexpensive, compact and robust equipment for authentication purposes, particularly, but not only, for incorporation into portable auditing devices.

summary

[0015] To meet or at least partially meet the objectives mentioned above, imaging systems, imaging methods, computer programs, computer program products and storage media according to the invention in the claims are defined independent. Particular embodiments are defined in the dependent claims.

[0016] In one embodiment, an image generation system is provided for generating an image of an object and generating a measure of authenticity of the object. The imaging system comprises one or more optical elements, the one or more image sensors being referred to hereinafter as "image sensor array", and one or more optical elements, the one or more optical elements being referred to below as "dispersive imaging arrangement". The dispersive imaging arrangement is realized such that when the electromagnetic radiation from the object illuminates the dispersive imaging arrangement, at least part of the electromagnetic radiation splits in different directions to at least an unscattered part and a scattered part. Furthermore, the dispersive imaging array is positioned with respect to the image sensor array so as to allow the image sensor array to image said unscattered portion in a first portion of the image sensor array and said part disperses in a second portion of this. The imaging system is configured so that, after the image sensor arrangement generates an image of the non-scattered part and the scattered part in at least one imaging period, it generates a measure of authenticity of the object depending on at least one relationship between the generated image of the scattered part, the generated image of the non-scattered part, and the reference spectral information.

[0017] Such an imaging system allows efficient verification of whether, and/or to what extent, the relationship between the dispersed generated image part, the non-dispersed generated image part, and the reference spectral information, which represents the expected spectral composition of the electromagnetic radiation from the object, corresponds to the predicted physics. If so, the object is likely to be authentic. Otherwise, the object is more likely to be faked.

[0018] The invention also relates, in one embodiment, to an imaging device for generating an image of an object and for generating a measure of authenticity of an object. The imaging method makes use of: one or more image sensors, the one or more image sensors being referred to, as mentioned above, as the "image sensor array", and one or more optical elements, the one or more more optical elements being referred to, as mentioned above, as "dispersive imaging array". The dispersive imaging arrangement is realized such that when the electromagnetic radiation from the object illuminates the dispersive imaging arrangement, at least part of the electromagnetic radiation splits in different directions to at least an unscattered part and a scattered part. Furthermore, the dispersive imaging array is positioned with respect to the image sensor array so as to allow the image sensor array to image said unscattered portion in a first portion of the image sensor array and said part disperses in a second portion of this. The imaging method comprises: imaging, by arraying the image sensor, the non-scattered part and the scattered part in at least one imaging period, and generating a measure of authenticity of the object depending on at least one relationship between the generated image of the scattered part, the imaged non-scattered part, and the reference spectral information.

[0019] The invention also relates, in some embodiments, to a computer program or a set of computer programs for performing an image generation method, as described above, for a computer program product or a set of products of a computer program for storing a computer program or a set of computer programs as described above, and for a storage medium for storing a computer program or a set of computer programs as described above.

Brief description of the drawings

[0020] Modalities of the present invention will now be described, together with the attached figures, in which:

[0021] Fig. 1 schematically illustrates an object to be imaged and an image generation system in an embodiment of the invention;

[0022] Fig. 2 schematically illustrates an object to be imaged and a system in an embodiment of the invention, wherein the system comprises both an image generation system and a lighting arrangement;

[0023] Fig. 3 schematically illustrates an imaged object and a system in an embodiment of the invention, wherein the system notably comprises lighting elements arranged around a dispersive imaging arrangement;

[0024] Figs. 4 to 6 schematically illustrate three imaging systems and objects to be imaged, respectively in three embodiments of the invention;

[0025] Figs. 7 and 8 schematically represent, using a fine lens grid approximation, two image generation systems and marks to be imaged, respectively in two embodiments of the invention, where figure 8 specifically illustrates the separation in order;

[0026] Fig. 9a schematically illustrates an imaging system in an embodiment of the invention, wherein the imaging system is an imaging device;

[0027] Fig. 9b schematically illustrates a system in one embodiment of the invention, wherein the system comprises both an imaging system and a lighting arrangement, and wherein the system is an imaging device;

[0028] Fig. 10a schematically illustrates an imaging system in an embodiment of the invention, wherein the imaging system comprises an imaging device and said imaging device comprises an image sensor arrangement and a imaging arrangement. of dispersive imaging, but said imaging device is not configured to actually generate the authenticity measure;

[0029] Fig. 10b schematically illustrates a system in an embodiment of the invention, wherein the system comprises an imaging device and said imaging device comprises an image sensor array, a dispersive imaging array, and an illumination array , but said imaging device is not configured to actually generate the authenticity measure;

[0030] Fig. 11 is a flowchart of an imaging method according to an embodiment of the invention;

[0031] Fig. 12a to 12c are flow charts of imaging methods in three embodiments of the invention, wherein generating the authenticity measure depends at least on the extent to which the generated image of the scattered part corresponds to a convolution of the generated image of the unscattered part, and reference spectral information;

[0032] Fig. 13 is a flowchart of an image generation method in an embodiment of the invention, which involves decoding a code of a tag within the generated image of the unscattered portion and verifying the authenticity of the code;

[0033] Fig. 14a schematically illustrates an imaging system in an embodiment of the invention when applied, by simulation, to a single point of a two-dimensional matrix bar code;

[0034] Fig. 14b schematically illustrates an imaging system in an embodiment of the invention when applied, by simulation, to a two-dimensional matrix bar code;

[0035] Fig. 15 shows exemplary zero and first order images of two-dimensional matrix bar codes printed on labels having their image generated by an imaging system in an embodiment of the invention;

[0036] Fig. 16 shows the exemplary result (upper right graph) of column-by-column deconvolution (or similar non-linear process) of a two-dimensional matrix barcode image containing zero and first order components (left image), as well as the comparison the average of all spectrum curves in the upper right graph with the reference spectral information (lower right graph);

[0037] Figs. 17 to 19 schematically illustrate three imaging systems in respectively three embodiments of the invention;

[0038] Figs. 20 and 21 schematically illustrate the generation of an authenticity measure of an object, in two embodiments of the invention, in which the image sensor arrangement generates images of the scattered part and the non-scattered part in a plurality of lighting periods;

[0039] Figs. 22, 23a and 23b are flowcharts of imaging methods in three embodiments of the invention, wherein the generation of the authenticity measure of an object follows the imaging of the image sensor arrangement of the non-scattered part and the scattered part. a plurality of lighting periods;

[0040] Figs. 24a and 24b show images of a soda can lid without (Fig. 24a) and with a mask (Fig. 24b) acquired using an imaging system in an embodiment of the invention;

[0041] Fig. 25 shows example images of a soda can lid acquired without a physical mask, but excited in two different lighting periods by blue light (left image) and green light (right image), in an embodiment of the invention;

[0042] Fig. 26 shows examples of background subtracted images using two different linear combinations, in one embodiment of the invention;

[0043] Fig. 27 shows examples of spectra extracted with and without applying the DIBS algorithm on acquired images, in an embodiment of the invention;

[0044] Fig. 28 shows the spectral reflectivity of two different colored pigments;

[0045] Fig. 29 shows the typical relative spectral distribution of a white LED;

[0046] Fig. 30 shows the typical relative spectral distribution of an incandescent lamp at a temperature of 3000 K compared to that of the sun;

[0047] Fig. 31 shows the excitation spectrum and emission spectrum of an exemplary fluorescent dye;

[0048] Figs. 32 and 33 show the emission and excitation spectra for exemplary phosphorescent phosphor pigments;

[0049] Fig. 34 is a schematic diagram of an exemplary implementation of a computing unit in accordance with an embodiment of the invention;

[0050] Figs. 35a to 35d schematically illustrate examples of imaging periods and illumination periods in four embodiments of the invention; It is

[0051] Fig. 36 schematically illustrates an imaging system comprising, on the one hand, an imaging device comprising an image sensor arrangement, wherein the imaging device is a mobile phone with a camera, and, on the other hand , an imaging accessory comprising a dispersive imaging arrangement.

Detailed Description

[0052] The present invention will now be described in conjunction with specific embodiments. These specific embodiments serve to provide the person skilled in the art with a better understanding, but are not intended to restrict the scope of the invention, which is defined by the appended claims. A list of abbreviations and their meaning is provided at the end of the detailed description.

[0053] Fig. 1 schematically illustrates an imaging system 200 in an embodiment of the invention. The system 200 aims to generate an image of an object 10 and generate a measure of authenticity for an object 10, that is, an article. The object 10 may be, for example, without being limited to, a bottle or can of beer, wine, alcoholic beverages or soft drinks, a package, pack or box of cigarettes or cigars, a package of medicine, a bottle of perfume or any other taxable goods, a banknote, a paper of value, an identity document, a card, ticket, label, tag, security strip, security thread or the like. The object 10 has at least one part, surface or side bearing a visible or invisible mark, logo, sign, image or pattern, for example printed with an ink and/or printing coating, either printed on a label attached to the object 10 or printed directly on the object 10 (such as on a lid, capsule or the like of the object 10, where the lid or capsule may, for example, have a colored background). The expected spectral response of said part, surface or side, and possibly of the paint thereon (which may or may not have, for example, photoluminescent properties), when subjected to particular lighting conditions, is known and constitutes the reference spectral information.

[0054] System 200 comprises an array 60, hereinafter referred to as "image sensor array" 60, consisting of one or more image sensors. System 200 also comprises another array 30, referred to hereinafter as a "dispersive imaging array" 30, consisting of one or more optical elements.

[0055] In one embodiment, the image sensor array 60 comprises one or more arrays of CCD or CMOS detectors for recording the intensity distribution of incident electromagnetic energy. The dispersive imaging array 30 not only disperses electromagnetic energy, but can also collect electromagnetic energy from the object 10 and focus the electromagnetic energy rays to produce an image of the object 10 in an imaging plane where the image sensor array image 60 is positioned. In one embodiment, the dispersive imaging arrangement 30 comprises, on the one hand, at least one of a diffractive element, a refractive element, one or more lenses, and an objective, for producing an image of the object 10 in the image plane where the image sensor array 60 is positioned and, on the other hand, a long pass filter (also called a "long wavelength pass filter") in order to limit the spectral range used for authentication.

[0056] The system 200 may also optionally comprise various auxiliary elements (not shown in Fig. 1), such as for example any one or any combination of: a) a housing to contain, cover and/or protect the power generation arrangement dispersive image 30 and image sensor arrangement 60; b) support elements integrally formed within the housing, or attached thereto, for maintaining the dispersive imaging arrangement 30 in a fixed or substantially fixed relative position relative to the image sensor arrangement 60; c) a protective covering or protective covering means to be used between the object 10 and the dispersive imaging arrangement 30 to prevent stray illumination from ambient light and/or sunlight (in this case, a controlled lighting source may be contained within this protective covering); d) additional optical filters (longpass, bandpass, etc.), which may be advantageous, for example, if the imaging system 200 operates in luminescence mode, to cut reflection from the irradiation source; e) a controller or control means or units for controlling the operation of the image sensor array 60 and other elements; f) sending and input means for providing information to and receiving information from an operator, such as a display screen, a keyboard, buttons, control knobs, LED indicator lights, etc. (in this regard, see also Fig. 34 and the corresponding description); and g) a battery to power various electronic parts of system 200.

[0057] The dispersive imaging array 30 is constituted and positioned so that when electromagnetic radiation 20 from the object 10 illuminates the array 30 or particularly a specific part, surface, side, hole or aperture thereof, at least least part of the radiation 20 splits in different directions into at least an unscattered part 40 and a scattered part 50. The word "dispersive" means here: that it separates into its constituent wavelength components. For example, the array 30 may comprise: a diffractive element, a transmission diffraction grating (also known simply as a "transmission grating", or rarely as a "transmissive diffraction grating"), a diffracted transmission diffraction grating, a holographic volume grating, a grid (also called a "grid prism"), a reflective diffraction grating, an array comprising a beam splitter and a diffraction grating, an array comprising a beam splitter and a dispersive prism or a combination of any of these. If the array 30 diffracts the radiation 20, the non-scattered part 40 can be referred to as a zero diffraction order part of the radiation, and a scattered part 50 can be referred to as a non-zero diffraction order part, such as, for example, the first negative or positive diffraction order part of the radiation.

[0058] Here are some examples of transmission grids that can be used in some embodiments of the invention:

[0059] Example 1: Especially for a transmission grid mounted in front of an objective (also see about it in Figures 4 and 17), a Thorlabs #GT13-06V (from Thorlabs, Inc., headquartered in Newton, New Jersey , USA) with groove density of 600 lines per mm (l/mm), diffraction angle 28.7°, size 12.7 x 12.7 x 3 mm Schott glass B270, can be used.

[0060] Example 2: Especially for a transmission grid mounted between an objective and the image sensor(s) (see also Figs. 5, 6, 18 and 19), a Richardson grid 340056TB07-775R (from Newport Corporation, headquartered in Rochester, New York, USA) with groove density of 360 l/mm, diffraction angle of 21°, and size of 12.7 x 12.7 x 3 mm, can be used.

[0061] Example 3: Especially for a grid mounted on the back for an extended field of view, a Thorlabs #GTU13-06 with groove density of 600 l/mm, diffraction angle of 22° and size 12.7 can be used x 12.7 x 2 mm of fused silica.

[0062] The electromagnetic radiation 20 coming from the object 10 and illuminating the dispersive imaging arrangement 30 may derive, in part or in full, from the reflection of electromagnetic radiation emitted by an electromagnetic radiation source (not shown in Fig. 1) . Radiation 20 from object 0 and illuminating array 30 may alternatively, or additionally, derive, in part or in whole, from some form of photoluminescence (i.e., fluorescence or phosphorescence) of a substance from object 10 during or after illumination of the object 10 by electromagnetic radiation emitted by an electromagnetic radiation source. In either case (i.e. radiation by reflection or by some form of photoluminescence), the source of electromagnetic radiation may, in one embodiment, be integrated with, or connected to, a housing containing the imaging system 200 (or part of this). Said source of electromagnetic radiation can be, for example, a light source, an infrared radiation source and/or a UV radiation source. In one embodiment, the electromagnetic radiation source is a light source controlled by system 200 or in conjunction with system 200.

[0063] The electromagnetic radiation 20 coming from the object 10 usually contains radiation of more than one wavelength, especially when the object 10 is authentic. That is, radiation 20 is generally polychromatic in the broad sense of the term, i.e. not necessarily limited to visible colors. The radiation 20 can, for example, be in any wavelength range comprised between 180 nm (UV radiation) and 2500 nm (infrared radiation), i.e. in the visible light range and/or outside this range (for example in the near-infrared (NIR) or short-wavelength infrared (SWIR) range. The portion of radiation 20 reaching the dispersive imaging array 30 that is actually scattered may depend on the characteristics of the element(s) optical(s) forming the array (30) For example, the long pass filter can be used to select the spectral range to be analyzed.

[0064] Furthermore, the dispersive imaging array 30 is positioned relative to the image sensor array 60 so as to allow the array 60 to simultaneously image in one imaging period (as illustrated by Fig. 35a) , image sequentially in two imaging periods (as illustrated by Fig. 35b), or image partially simultaneously in two imaging periods (as illustrated by Figs. 35c and 35d), the unscattered part 40 in a first array portion 60 and dispersed portion 50 in a second array portion 60.

[0065] An example of an image sensor that can be used in some embodiments of the invention is: a 1/3 inch Wide-VGA CMOS Digital Image Sensor MT9V022 from ON Semiconductor, headquartered in Phoenix, Arizona, USA. This sensor has 752 x 480 pixels with a size of 6 μm forming the size of the active imager with dimensions of 4.51 mm x 2.88 mm and a diagonal of 5.35 mm.

[0066] An image period is defined here as being: a) if the unscattered part 40 and the scattered part 50 are simultaneously acquired by the image sensor array 60, the period during which the unscattered part 40 and the scattered part dispersed part 50 are acquired (as illustrated by Fig. 35a), or b) if the non-dispersed part 40 and the dispersed part 50 are acquired sequentially (as illustrated by Fig. 35b) or partially simultaneously (as illustrated by Figs. 35c and 35d) acquired by arraying the image sensor 60, each of the periods during which the unscattered part 40 is acquired and the period during which the scattered part 50 is acquired.

[0067] In one embodiment, each or at least one imaging period has a duration that has a value selected from a range of 5 to 1200 ms, and preferably selected from the range of 10 to 800 ms, such such as 10, 20, 30, 50, 75, 100, 150, 200 or 300 ms.

[0068] In an embodiment, the duration of the imaging period for the non-scattered imaging part 40 and the duration of the imaging period for the scattered imaging part 50 are different from each other. This modality can be particularly advantageous when using diffraction gratings with different efficiencies for zero order and first order. In one embodiment, the duration of the imaging period for the non-scattered imaging portion 40 may be 10ms, whereas the duration of the imaging period for the scattered imaging portion 50 may be of 100 ms.

[0069] An illumination period (as illustrated by Figs. 35a to 35d) is defined in this document as being a period during which the illumination conditions are considered sufficiently constant for the purpose of generating an image of the unscattered part 40 and the part scattered 50, and to generate an authenticity measure based on it.

[0070] In one embodiment, the first and second portions of the image sensor array 60 are in two different image sensors of the array 60. When using two image sensors for imaging unscattered and scattered parts 40, 50 their relative positioning must be taken into account.

[0071] In another embodiment, the first and second portions of the array 60 are in two portions of a single image sensor. In other words, in this embodiment, the non-scattered and scattered parts 40, 50 can be captured in a single frame.

[0072] The configuration (geometry, parameters, etc.) of the optical elements of the dispersive imaging array 60 allows separation of the dispersed part 50 from the non-dispersed part 40 within the single frame. Shorter wavelengths are deviated less than longer wavelengths. In one embodiment, system 200 is configured to avoid overlapping the first-order image at the shorter wavelength with the zero-order image (see also Fig. 8, which schematically illustrates order separation). A long pass filter can, for example, be used to cut shorter wavelengths as, for example, shown in Fig. 8, so as to avoid overlapping orders.

[0073] The portion of electromagnetic radiation 20 that illuminates and passes through the dispersive imaging array 30 (thus being scattered in one set of directions and being unscattered in another set of directions) that is then detected by means of the array of dispersive imaging 60 depends on the characteristics of the image sensor(s). The electromagnetic radiation detected by means of the image sensor(s) can, for example, be in any wavelength range falling between 180 nm (UV radiation) and 2500 nm (infrared radiation), i.e. in the range visible light and/or outside this range (e.g. in the near-infrared (NIR) or short-wavelength infrared (SWIR) range. In this example, the lower limit of 180 nm may be imposed by material constraints on both the dispersive imaging arrangement 30 and imaging sensor(s) 60, while the upper limit of 2500 nm may be imposed, for example, by the spectral response of infrared detectors based on indium gallium arsenide (GalnAs). In one embodiment, the electromagnetic radiation detected by means of the image sensor(s) (60) is in the visible light range. In one embodiment, the electromagnetic radiation detected by means of the image sensor(s) 60 is in the wavelength range from 180 nm to 2500 nm, more preferably in the range from 400 nm to 1000 nm.

[0074] Additionally, the imaging system 200 is configured so that, after the arrangement of the image sensor 60 generates an image of the non-dispersed part 40 and the dispersed part 50 in at least one imaging period, generates a measurement of authenticity of object 10 depending on at least one relationship between the generated image of the scattered part, the generated image of the non-scattered part, and the reference spectral information. The system 200 therefore allows verification of whether, and/or to what extent, the relationship between the scattered generated image part, the non-scattered generated image part, and the reference spectral information, which represents the expected spectral composition of electromagnetic radiation 20 from object 10, is in correspondence with the expected underlying physics of the system. If so, object 10 is likely to be authentic. If not, it is more likely to be counterfeit. Thus, the system 200 allows for a form of material-based authentication, such as, for example, at least one of: a) authentication based on the material of the ink used to create a mark 11 printed on the object 10, and b) authentication based on the material of the object 10 itself, especially if the object 10 is luminescent with a specific emission spectrum or has a specific reflection or absorption spectrum.

[0075] The nature of the analyzed relationship, that is, the relationship between the generated image of the dispersed part, the generated image of the non-dispersed part, and the reference spectral information can be understood starting from the sense that, if the spectral information matches, substantially matches, or plausibly matches the spectral composition of incident electromagnetic radiation 20 from the image generated by the object 10, the generated image of the scattered portion typically resembles (non-linear effects may also need to be taken into account) the result of the convolution of the image generated from the unscattered part with the reference spectral information, in which case the object 10 is probably authentic. In contrast, if the reference spectral information does not correspond to the spectral composition of incident radiation 20 from the generated image of the object 10, the generated image of the scattered part typically differs remarkably from the convolution result of the generated image of the non-scattered part. scattered with the reference spectral information, in which case the object 10 is likely to be a fake.

[0076] More generally, the nature of the relationship that is analyzed, i.e., the relationship between the generated image of the scattered part, the generated image of the non-scattered part, and the reference spectral information and can also differ significantly from a simple convolution, considering the existence of non-linear effects. The nature of the relationship can be determined a) based on underlying physics and geometry, b) empirically, or c) by simulation (e.g., using ray tracing methods of commercially available solutions, such as, for example, software Zemax optical design, available from Zemax, LLC, headquartered in Redmond, Washington, USA).

[0077] The underlying physics and geometry may include (i) the properties of dispersive imaging array 30, image sensor array 60, the intermediate transmission medium, etc., and (ii) image stretching effects ( zero or first order) in the direction of dispersion (y-axis), which can be compensated by mapping the image's y-axis (zero or first order) to a new y'-axis using a non-linear function. The image may be stretched due to 1) non-linear grid scattering, 2) projection distortions (with different paths from the 30 array to the 60 array), and/or 3) optics-specific field aberrations (since lenses can distort from slightly different way to zero and first order).

[0078] The non-linear effects can, in one embodiment, be modeled also as a relationship between the scattered and unscattered images, and the reference spectrum in a form as close to linear-invariant translation (LTI) as possible. In such a case, the determination of the non-linear effects can be carried out, for example, by means of a) acquisition of several zero and first order images of objects 10 with a known reference spectrum, and b) adjustment of the non-linear parameters to transform the relationship to LTI.

[0079] One way of determining the non-linear effects, and therefore the nature of the relationship to be observed, can be a mathematical analysis of the optical system and determination of the correction that needs or should be applied to make the system LTI. This can be done using optical equations, for example found in textbooks such as Yakov G. Sosking, “Field Guide to Diffractive Optics”, SPIE, 2011. This can also be done numerically using optical software such as Zemax OpticStudio™, available from Zemax, LLC.

[0080] In one embodiment, dispersive imaging array 30 diffracts electromagnetic radiation 20 using a diffraction grating, and the generated image of the non-scattered part is the zero-order diffraction image of the diffraction grating, whereas the generated image of the scattered part is the image in a first diffraction order of the diffraction grating. An average spectral irradiance of an image region can be reconstructed using the generated images of unscattered and scattered parts, and then the average spectral irradiance can be compared to the expected spectral irradiance (the reference spectral information). In one embodiment, the diffraction grating groove profiles (eg, diffraction angle) are optimized to scatter the majority of incoming electromagnetic radiation into these two orders.

[0081] In one embodiment, generating a measure of authenticity of the object 10 comprises authenticating it, that is, determining that it is likely to be authentic or not. In one embodiment, generating a measure of authenticity of the object 10 comprises generating a measure (or index) of authenticity, such as, for example, a real value between 0 and 1, where '0' can mean "totally sure that the object is not authentic" and '1' can mean "completely certain that the object is authentic".

[0082] In practice, the authentication index usually does not reach the value '1' for all authentic objects (and '0' for all non-authentic ones). Therefore, in one embodiment, a threshold between '0' and '1' is defined (for example, a value comprised between 0.80 and 0.90, and particularly 0.85) above which the object is considered authentic and below which the object is considered to be inauthentic. This limit can, for example, be defined through measurements on a set of authentic and non-authentic objects. Typically, these measurements produce a bi-modal distribution of indices (ie, a part for the authentic objects concentrated in relation to the value '1' and a part for the non-authentic ones below, both separated by a gap). The robustness of the method is directly related to the extent to which the two parts (modes) of the index distribution are far from each other. The threshold can then be placed between close to the index distribution of authentic objects to minimize false positives or closer to the non-authentic index distribution to minimize false negatives.

[0083] If object 10 is, for example, a container or package containing some products, the authenticity measure generated may simply correspond to a product authenticity measure determined through a mark or sign on the container or package ( assuming the container or packaging has not been tampered with), not necessarily making it possible to directly authenticate the products as such.

[0084] Since the scattered and non-scattered parts 40 50 of the electromagnetic radiation can be imaged in one imaging period, and since the imaging allows determination of the spectral composition of the incident electromagnetic radiation 20, imaging system 200 can be thought of as a form of instantaneous spectral imaging in the sense that the scene is not scanned during the imaging process. However, the system 200 does not enable or at least not necessarily allow obtaining the spectral composition, that is, the irradiance, of each point (x, y) of the scene, which is not necessary for authentication since there is a dominant spectral response in the image.

[0085] Fig. 2 schematically illustrates an object 10 to be imaged and a system 220 in an embodiment of the invention; System 220 comprises both an imaging system 200 (as described above with reference to Fig. 1) and a lighting arrangement 210. In one embodiment, system 220 forms a single device, such as, for example, a device portable, code reading and authentication.

[0086] The lighting arrangement 210 generates electromagnetic radiation 21 to illuminate the object 10. In one embodiment, the radiation 21 has known parameters (e.g., spectrum, power, homogeneity, etc.) to allow excitation, for example, of the luminescence emission spectra to allow imaging of the object 10 and/or mark 11 on it, and analysis of the emission spectra for authentication. As explained above with reference to Fig. 1, the electromagnetic radiation 20 originates from the object 10 and/or the mark 11 on it, and reaches the imaging system 200.

[0087] In one embodiment, the system 220 is connected to the drive electronics and sensor readout electronics, so that, for example, image data output from the image system 200 can be transferred to a processing unit for data processing.

[0088] Fig. 3 schematically illustrates an object 10 to be imaged and a system 220 in an embodiment of the invention, as a possible implementation of the system illustrated in Fig. 2. The system 220 particularly comprises lighting elements 22 arranged around the dispersive imaging array 30. Although two lighting elements 22 are shown in Fig. 3, any number of lighting elements 22 may be provided, such as for example three, four or more. Furthermore, in one embodiment, the lighting elements 22 are arranged symmetrically around the dispersive imaging array 30. The symmetrical arrangement of the lighting elements 22 around the array 30 is advantageous for homogeneously illuminating the target surface of the object 10 .

[0089] Figs. 4 to 6 schematically illustrate three imaging systems 200, respectively in three embodiments of the invention, showing possible components of the dispersive imaging arrangement 30, such as a transmission grid 31, an imaging lens 32, a long pass optical filter 33 and an additional lens arrangement 34.

[0090] The arrangement 30 of Fig. 4 comprises an imaging lens 32, a transmission grid 31 mounted in front of the lens 32, and an optical long pass filter 33 mounted behind the lens 32. This allows producing low optical aberrations for scattered and non-scattered images using the field of wide view of the objective of the lens.

[0091] In the arrangement 30 of Fig. 5, both the transmission grid 31 and the optical long pass filter 33 are mounted behind the lens 32. This allows canceling the dependency of the extracted spectra on the position of the object along the optical axis.

[0092] In the modality of Fig. 6, the optical long pass filter 33 is mounted in front of the lens 32, and the transmission grid 31 is mounted behind the lens 32. Furthermore, an additional array of lenses 34 is also mounted behind the lens 32. This configuration allows efficiently separate scattered and non-scattered images and avoid dependence on object position along the optical axis.

[0093] Figs. 7 and 8 schematically represent, using a fine lens grating approximation, two imaging systems 200 and marks 11 in two embodiments of the invention, respectively, to help understand the order separation and definition of the minimum wavelength of a spectral band that is analyzed to authenticate the mark 11.

[0094] In Fig. 7, the dispersive imaging arrangement 30 includes a lens, a transmission grid, and a long wavelength pass filter to create the unscattered image 41 (zero order) and the scattered image in the imaging plane 65 where the image sensors are positioned. The scattered beams 50-1 are for the shorter wavelength λ-1 and create the scattered image 51 corresponding to the wavelength λ-1.

[0095] Imaging system 200 receives electromagnetic energy 20 from object 10 to create an unscattered image 41 of object 10 on the imaging plane 65.

[0096] The non-dispersive portion 40 is produced by the array 30 in the same or similar manner as an ordinary, non-dispersive imaging array consisting merely of a lens.

[0097] The scattered part is shifted compared to the non-scattered image and is blurred by the electromagnetic energy spectrum 20 impinging on the array 30. The minimum shift depends on the minimum wavelength present in the spectrum emitted by the object 10 or depends on the length minimum waveform transmitted through the array 30. The minimum displacement may also depend on some parameters of the grid and the system (for example, groove density, order and angle of incidence), which parameters define the angular dispersion of the grid.

[0098] The three scattered discrete images of mark 11 in Fig. 7 correspond to discrete wavelengths À 1 , À 2 and À 3 . These discrete wavelengths can therefore be conveniently resolved since the corresponding images do not overlap. Furthermore, the system 200 separates the scattered image 51 for wavelength λ 1 from the unscattered image 41, so that, on the one hand, a marker image 11 can be conveniently read (for example, to decode the code represented by mark) and, on the other hand, the emission spectra of the ink used to print the mark 11 can be extracted.

[0099] Fig. 8 shows an image of an area 12 of the object 10, where the area 12 contains a printed mark 11, which can be in any position or orientation. If mark 10 is outside area 12, imaging system 200 must be repositioned to have mark 11 within area 12. The unscattered image 41 of area 12 contains the image of mark 11. The image scattered 51 of area 12 contains the image of brand 11.

[00100] Image 51 corresponds to the minimum wavelength Àmin that can be transmitted by the system and defined by a cutoff wavelength of a long pass filter of the array 30. Reference 62 shows the order separation, which, in in one embodiment, it corresponds to the size of the image 41 of the area 12. In one embodiment, the arrangement 30 allows this order separation for the minimum wavelength Àmin to efficiently authenticate the object 10.

[00101] In one embodiment, the lighting arrangement 210 (not illustrated in Fig. 8) illuminates only the portion of the object 10 that corresponds to the area 12. The lighting arrangement 210, together with an optional protective covering (as mentioned above) , can be designed to prevent ambient light from reaching area 12, thus providing better conditions for code reading and authentication.

[00102] Fig. 9a schematically illustrates an imaging system 200 in one embodiment of the invention, which differs from the imaging system 200 of Fig. 1, so that the system 200 of Fig. 9a specifically consists of a single imaging device. In addition to the array 30 and image sensor array 60 described with reference to Fig. 1, the system 200 comprises a processing unit 70 configured to receive data representing the generated image of the scattered and unscattered parts (as sensed by array 60), generating the authenticity measure as described with reference to FIG. 1, and outputting information 80 representing the generated authenticity measure to any type of imaging device user interface and/or to an output port for transmission to one or more external devices (not shown in Fig. 9a).

[00103] In one embodiment, the imaging device that constitutes the imaging system 200 of Fig. 9a is a portable device. Such an imaging device can therefore be considered as a portable auditing device capable of generating a measure of the authenticity of an object, and of providing the measure of authenticity, for example, to the operator of the device.

[00104] Fig. 9b schematically illustrates a system 220 in one embodiment of the invention, wherein the system 220 comprises both an imaging system 200 and a lighting arrangement 210, and wherein the system 220 is an imaging device. In other words, the embodiment of Fig. 9b can be considered a combination of the embodiments described with reference to Figs. 9a and 2. In one embodiment, the imaging device comprising system 200 of Fig. 9b is a portable device.

[00105] Fig. 10a schematically illustrates an imaging system 200 in one embodiment of the invention, which differs from the imaging system 200 of Fig. 1, so that the system 200 of Fig. 10a is shown to specifically comprise more than one device. That is, in the example of Fig. 10a, the system 200 comprises two devices: on the one hand, an imaging device 100 comprising a dispersive imaging arrangement 30 and the image sensor arrangement 60 is described with reference to Fig. 1 and, on the other hand, a processing device 110 comprising a processing unit 70. The processing device 110, instead of the image generation device 100, generates the authenticity measure (as described with reference to Fig. 1) . For this, data 90 representing the generated image of the scattered and non-scattered parts is transmitted from the imaging device 100 to the processing device 110. The data 90 may be transmitted on any wired or wireless channel suitable using any transmission format (such as, for example, using Internet Protocol (IP) packets, optionally encrypted). Then, within the processing device 110, the authenticity measure is generated by the processing unit 70, and the information 80 representing the generated authenticity measure can then be sent to a user interface of the processing device 110 and/or to a output port for transmission to one or more other external devices (not shown in Fig. 10a).

[00106] Fig. 10b schematically illustrates a system 220 in an embodiment of the invention, wherein the system 220 comprises an imaging device 100 and said imaging device 100 comprises an image sensor array 30, a dispersive imaging array 60 and a lighting arrangement 210, but said imaging device 100 is not configured to actually generate the authenticity measure; In other words, the modality of Fig. 10b can be considered a combination of the embodiments described with reference to Figs. 10a and 2.

[00107] In one embodiment, the imaging device 100 of any one of Figs. 10a and 10b is a portable device.

[00108] In one embodiment, the processing unit 70 of any one of Figs. 9a, 9b, 10a and 10b form part of a computing unit, such as, for example, the one illustrated with reference to Fig. 34 (which is discussed below). In that case, the processing unit 70 of Fig. 9a or 9b and the processing unit 503 of Fig. 34 may actually be the same element. Likewise, in such a case, the processing unit 70 of Fig. 10a or 10b (within processing device 110) and processing unit 503 of Fig. 34 may actually be the same element.

[00109] In some embodiments, the imaging device that constitutes the imaging system 200 of Fig. 9a or 9b, or imaging device 100 illustrated in Fig. 10a or 10b, comprises a handle formed integrally with or attached to the housing to enable an operator to hold the imaging device toward the object to be imaged and authenticated.

[00110] In one embodiment, the imaging device that constitutes the imaging system 200 of Fig. 9a or that constitutes the system 220 of Fig. 9b, or the imaging device 100 illustrated in any one of Figs. 10a and 10b further comprises a storage unit (not shown in any of Figs. 9a, 9b, 10a and 10b) for storing, for example, spectral reference information that is known in advance and used to generate the authenticity measure.

[00111] The reference spectral information can be stored in the form of a reference spectral profile.

[00112] Fig. 11 is a flowchart of a method in an embodiment of the invention which makes use of an image sensor arrangement 60 and a dispersive imaging arrangement 30, as described above with reference to Figs. from 1 to 10b. The generation method comprises the steps of generating an image s300, by array 60, in at least one period of imaging, non-dispersed part 40 and scattered part 50, and generating s400 a measure of authenticity of the object 10 depending at least on a ratio between the generated image of the scattered part, the generated image of the non-scattered part, and the reference spectral information. Step s400 is performed via convolution or deconvolution operation(s) (as discussed below with reference to Figs. 12a to 12c) or via convolution or deconvolution-like operations to account for non-linear effects as explained above.

[00113] If the imaging step s300 consists of imaging the non-scattered part 40 and the scattered part 50 in a single illumination period, step s300 precedes the generation step s400, normally without overlapping. However, if the step s300 consists of imaging the non-scattered part 40 and the scattered part 50 in a plurality of lighting periods (typically under different lighting conditions), the imaging step s300 and the imaging step s400 may overlap (not shown in Fig. 11). That is, the authenticity measurement generation process s400 can begin based on image data recorded during one or more lighting periods while the imaging step s300 is still ongoing.

[00114] In one embodiment, generating s400 the authenticity measure depends at least on the extent to which the generated image of the scattered part corresponds to a convolution of the generated image of the non-scattered part and the reference spectral information; This can be implemented in different ways, as illustrated by Figs. 12a to 12c.

[00115] Particularly, in a first sub-modality, illustrated by the flowchart in fig 12a, generating s400 the authenticity measure comprises: deconvolution s410 of the generated image of the dispersed part by the generated image of the non-dispersed part, and determining s420 to what extent the result corresponds to the reference spectral information;

[00116] In a second sub-modality, illustrated by the flowchart in Fig. 12b, generating s400 the authenticity measure comprises: deconvolution s430 of the image generated from the scattered part by the reference spectral information, and determining s440 to what extent the result corresponds to the image generated from the non-dispersed part; It is

[00117] In a third sub-modality, illustrated by the flowchart in fig 12c, generating s400 the authenticity measure comprises: deconvolution s450 of the image generated from the non-scattered part and the reference spectral information, and determining s460 to what extent the result corresponds to the generated image of the dispersed part.

[00118] A possible implementation of step s400 in this third sub-modality can be described as follows:

[00119] In step S450, a first synthetic diffraction order image is calculated by convoluting the known spectral signature of the authentic ink (i.e., the reference spectral information) with the zero order image (i.e., the generated image of the part non-dispersed). Then, in step s460, a cross-correlation between the acquired first-order image (i.e., the generated image of the scattered part) and the first-order synthetic image (i.e., the result of step s450) is used to compare them. them and generate a similarity parameter. This correlation can be performed not only on the images, but also on the first and second derivatives of the images to produce three similarity parameters. Then a decision is made for example by applying classifiers based on machine learning algorithms on sets of similarity parameters to authenticate tag 11 on object 10.

[00120] A convolution may, however, not always lead to the best results due to the existence of non-linear effects (as discussed above). Thus, in one embodiment of the invention, instead of performing a convolution in step S450, a model or function can be used which can be determined in advance using instrument calibration data, measurements, modeling or a combination thereof. The function or model is a computation model for computing a synthetic first-order image (i.e., asysynthetic scattered part) of a given zero-order image (i.e., non-scattered image part) and a known spectrum (i.e. the reference spectral information). Similar considerations apply to deconvolution steps S410 and S430, which can be replaced by other models or functions.

[00121] In order to perform the comparison part of step s460 in this implementation, the acquired first-order image (that is, the generated image of the scattered part) and the synthetic first-order image (that is, the output of step S450 ) are compared, and one or several corresponding similarity values are computed.

[00122] In one embodiment, the corresponding value is the cross-correlation value of the two images, ie, the acquired first-order image and the synthetic first-order image. In another embodiment, the corresponding value is the cross-correlation value of the derivative of the two images. In a later embodiment, the corresponding value is the cross-correlation value of the second derivative of the two images. In yet another embodiment, more than one corresponding values are extracted from a combination of previously proposed corresponding values. Computations can take place with the entire first-order images, or on a subset of them (region of interest). In one embodiment, the first-order image region of interest is the authentication tag boundary box. The bounding box is the smallest convex shape that contains the authentication tag. In another embodiment, an additional set of correlation values is computed based on the images called DIBS. The DIBS technique and the meaning of the DIBS images will be evident from the explanations provided below with reference to Figs. 24th to 27th.

[00123] In order to carry out the decision part of step s460 in this implementation, a decision algorithm is used to classify a measured sample into at least two categories: "genuine" or "false". Known machine learning algorithms can be used for this purpose, such as: support vector machine (SVM), decision trees, k-nearest neighbors (KNN) algorithm, etc. In one embodiment, the learning features are the similarity matching values described above. In one embodiment, other learning features are used which are not related to cross correlations, such as the standard deviation of the pixel values (i.e. intensity values) of the first order image, or the standard deviation of the values of pixel of the zero-order image.

[00124] In one embodiment, the standard deviation values and several sets of similarity that correspond to values of images obtained under different excitation wavelengths (for example, red, green or blue LED) are used. For example, a set of learning resources used to describe an example might be as shown in the following table:

[00125] In one embodiment, the classifier can be trained in advance on a heterogeneous dataset, consisting of genuine samples and randomized non-genuine samples.

[00126] During the decision phase of the method, the classifier can classify the given samples using the input features to the classifier.

[00127] The above-mentioned possible implementation of step s400 in the third submodality has been tested using classification algorithms (in this respect See for example: David Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University Press 2011) as described in the following table:

[00128] Compared to imaging spectrometers used for scientific observations, the approach in the embodiments described with reference to Figs. 12a to 12c is not focused on the reconstruction of a hypercube that contains spectral information for each pixel in the acquired image. The approach aims at creating a synthetic image with the assumption that there is only one dominant spectrum involved (the genuine brand spectrum). The computation required to produce this synthetic image mainly consists of several one-dimensional convolutions. Compared to the computation and memory needed to compute a hypercube, the approach is less expensive. Furthermore, applying a machine learning classifier is also fast and lightweight.

[00129] In one embodiment, the convolution or deconvolution operation of step s400 is performed per image line along the diffraction direction.

[00130] Furthermore, when the deconvolution step s410 of the embodiment described with reference to Fig. 12a is carried out in a line-by-line manner, the result of the deconvolution can then be calculated to reduce the noise and cancel the possible modulation by means of the no - background uniformities, before comparing the result with the reference spectral information as part of step s420.

[00131] In one embodiment, as illustrated by the flowchart in Fig. 13 generating the authenticity measure s400 further comprises decoding a code s492 of a tag within the non-dispersed part with generated image and verifying s494 the authenticity of the code; This allows generation of instantaneous markup for S492 decoding and s494 verification (based on the non-scattered imaged part generated, i.e. based on the "direct" image) and then using the output of the code verification process , in addition to verification based on the relationship between the imaged scattered part, the imaged non-scattered part, and the reference spectral information, to generate the authenticity measure. For example, in one embodiment, object 10 is considered authentic only if both verifications, i.e. code-based verification and spectrum-based verification, are successful. In other words, spatial information of a printed mark or code, as well as spectral emission information of the printed mark or code - which may have been printed, for example, using a photoluminescent ink - can be obtained for authentication purposes.

[00132] In one embodiment, the code decoding step s492 is used to obtain information on the basis of which the expected spectral composition of the electromagnetic radiation of the object 10 and therefore the spectral reference information to be used for authentication verification Spectrum-based analysis at step s400 can be obtained (eg through a database). In such a way, several different code families associated with a different ink (and therefore a different reference spectrum) can be printed on different classes of products and authenticated with the same device.

[00133] In one embodiment, the marking comprises at least one machine-readable code, which may, for example, comprise at least one of a linear barcode and a matrix barcode (for example, a Data Matrix code printout or QR code). It is therefore possible, in some embodiments of the invention, not only to decode a two-dimensional (or similar) matrix bar code, but also to perform material-based authentication using the spectrum of radiation from object 10, the spectrum of radiation corresponding, for example, to the fluorescence emission spectrum of the ink used for marking.

[00134] In one embodiment, the tag comprises unique spectral features over at least one region of the tag. The tag can also comprise unique spectral features throughout the entire tag.

[00135] In one embodiment, a mask is intentionally provided, as part of the imaging system 200 and, in addition, on or in the vicinity of the object 10 to reveal only a portion of the object 10. This is advantageous in the event that the entire object carries a substance having spectral reference information or a large marker that covers the entire image. The mask artificially creates a transition from the unmarked area to the marked area, even if there is no such transition without the mask.

[00136] In one embodiment, the imaging system 200 does not use any slit between the dispersive imaging array 30 and the object 10. Not using a slit is advantageous in that it allows simultaneous acquisition of an image and its spectrum, without having to scan (by moving the imaging device or spectrometer) the surface of the object to measure the spectrum for each position.

[00137] Now, before describing other embodiments of the invention, it might be useful to discuss some of the advantages brought by some of its embodiments, especially compared to prior art systems.

[00138] The systems and methods described above according to some embodiments of the invention are advantageous because they allow the construction of simple, compact, instantaneous (non-scanning) based devices, low cost and versatile, which can, for example, be incorporated into portable auditing devices. Acquiring images of the non-scattered part of the electromagnetic radiation and its scattered part is sufficient, along with spectral reference information that is known in advance, to generate the authenticity measure.

[00139] In contrast, imaging spectrometers used for scientific observations, as mentioned above, are typically complex, expensive or bulky. This is due to the fact that these prior art systems generally aim to obtain high resolution spatial and spectral information about all regions of the object or scene.

[00140] The mechanical sweep of different bandpass filters in front of an imager allows the reconstruction of a spectral irradiance map of the object l(x, y, Á). However, the time to check all the filters and the complexity and fragility of the scanner makes the optical system cumbersome, not robust and expensive to implement.

[00141] Fitting systems based on the Fabry-Perot interferometer or multistage liquid crystals avoid mechanical complexity, but require high quality and expensive optical components (eg interferometric mirrors). Scanning the filter parameters needed to acquire the full set of images can be slow and can become another limitation for use in portable authentication systems.

[00142] Snapshot solutions that rely on simultaneously imaging an object through the array of bandpass filters can achieve fast data acquisition and are tailored especially for portable auditing devices. In addition, such systems are compact and easily fit into a small volume of a portable device. The limited number of different bandpass filters is, however, a disadvantage, and it is also difficult to obtain suitable lens arrays. Furthermore, the spectral bands of the filter array have to be optimized for the ink's spectral response, which avoids the use of off-the-shelf filter arrays, whereas custom filter sets are typically expensive to design and manufacture.

[00143] The example of a grid-based imager using computed tomography (i.e. CTIS) requires either a complex holographically recorded Kinoform-type grid or multiple cross-grids capable of scattering light in a set of orders around zero order . The need for multiple gratings complicates setup and, in addition, exposure time must be extended to compensate for poor efficiency at higher diffraction orders. Data acquisition is therefore slowed down, making the setup unsuitable for a handheld device. Such arrangements also require large, expensive, multi-megapixel sensors and extensive calculations for tomographic inversion.

[00144] Coded aperture imagers are as slow as CTIS devices. Furthermore, there is an intrinsic problem to reconstruct the full spectrum for the specific design of the coded aperture. Meanwhile, integral field spectrometers require cumbersome image slicing optics and require relatively large surface image sensors.

[00145] Fourier transform imaging spectrometers are complex instruments that rely on expensive interferometers or birefringent prisms. In both cases, spectrometers require scanning an air gap or an angular orientation of the elements to obtain spectra that make them slow and brittle.

[00146] The prior art configurations described above require complex optics and data handling algorithms to calculate a complete spectral data cube l(x, y, Á), which is actually not necessary for authentication purposes. The inventors have not found any of these prior art configurations suitable for an economical, compact, robust and fast audit device based on a spectral imager.

[00147] Now, we will describe other embodiments of the invention, which may help to understand some aspects and advantages of the invention.

[00148] In one embodiment, the imaging system 200 has an optical configuration with a transmission diffraction grating 31 mounted in front of a lens objective 32 in a dispersive imaging array 30 which is then disposed in front of an image sensor arrangement 60, as schematically illustrated on the left side of the two Figs. 14a and 14b. The 200 system uses a 32 objective lens model EO57907 from Edmund Optics Ltd (based in York, UK) with f/2.5 and f = 3.6 mm focal length. The dispersive element in the array 30 is a transmission diffraction grating 31 of the GT13-06V type from Thorlabs, Inc., as mentioned above, with 600 lines per mm and a flame angle of 28.7°. Area 12 of object 10 is within the field of view of imaging system 200.

[00149] Fig. 14a also shows, on the right side of the Figure, the simulated dispersion of a single point (of, for example, a two-dimensional matrix bar code) at three discrete wavelengths obtained by means of the transmission diffraction grating 31 installed in front of the imaging objective 32. Shown is the scattering of the diffraction grating 31 obtained from a Zemax OpticStudio™ simulation. One can see direct ("Order 0") and scattered images in positive first orders ("Order 1") and negative first ("Order -1") of the single point (0.5 mm diameter) in the image space. image for three discrete wavelengths.

[00150] More complex marks, such as full two-dimensional array barcodes, typically produce smeared images in the first order of grid 31 due to the broader specific emission spectra of the inks, and an associated overlap of successive scattered dots is observed in the direction of diffraction, as illustrated on the right side of Fig. 14b. Particularly, Fig. 14b shows the simulated scattering of a data matrix with the unscattered image ("Order 0") and two images associated with both scattered orders, i.e., the first positive order ("Order 1") and the first negative order (" Order - 1”), assuming equal efficiency of grid 31 for all orders. The direct image at grid zero order is not influenced by the grid (except for intensity attenuation) and can be used to decode a printed two-dimensional matrix barcode. The scale shown in Fig. 14b is in intensity in arbitrary units (“I, au”).

[00151] Examples of real images of zero and first orders of a two-dimensional barcode matrix printed with two inks, that is, ink type 1 and ink type 2, are shown in Fig. 15. That is, Fig. 15 shows actual zero and first order images of two-dimensional array barcodes printed on labels, with, on the left side of Fig. 15, ink type 1 animated with blue LED light (peak 450 nm), and, on the right side of Fig. 15, ink type 2 excited with red light (peak 640 nm).

[00152] It can be seen that the images in the zero and first orders of the grid can be recorded simultaneously (as illustrated in Fig. 35a), sequentially (as illustrated in Fig. 35b), or partially sequentially (as illustrated in Figs. 35c and 35d), as both fit the array detector. Furthermore, the grid efficiency for both orders is similar, allowing you to record both orders with the same exposure time. The nth order efficiency of a grating is the ratio of nth order diffracted power to the total incident power.

[00153] The first-order scattered image is a convolution (or a convolution-like function) of the zero-order image of the two-dimensional array barcode with the emission spectrum of ink fluorescence. As a result, the ink emission spectrum can be extracted by deconvolution (or operation similar to deconvolution) from the first-order image using the spatial information of the zero-order image that is not affected by grating scattering.

[00154] A deconvolution algorithm based on the fast Fourier transform (FFT) can, for example, be used to extract the ink spectrum. This can use a set of columns from the extracted images along the grid scatter direction, comprising intensity profiles from the zero and first order images.

[00155] Fig. 16 shows an exemplary result (upper right graph) of column-by-column deconvolution (or similar non-linear process) of a two-dimensional matrix bar code image containing zero and first order components (left image), as well as the comparison of the average of all spectrum curves in the upper right graph with the reference spectral information (lower right graph), eg the spectrum of ink type 1; Each of the approximately 250 columns is subjected to deconvolution (or a similar non-linear process) and produces a spectrum. The spectra obtained from all columns are averaged. This averaging reduces noise (due, for example, to artifacts that can be created by deconvolution or similar non-linear process) and cancels the local background contribution, which can occur in limited parts of the two-dimensional array barcode. Therefore, the reconstructed spectral profile is an average for the entire printed two-dimensional matrix bar code being observed because it is assumed that all matrix dots are printed with the same ink and there is no significant background contribution to the spectrum. issue.

[00156] Figs. 17 to 19 schematically illustrate three imaging systems 200, respectively in three embodiments of the invention, showing possible components of the dispersive imaging arrangement 30, such as a transmission grid 31, an imaging lens 32, a long pass optical filter 33 and an additional lens arrangement 34. Area 12 of item 10 can have an image created by the array 30, considering its field of view (FOV) 15. The unscattered image 41 of area 12 and the scattered image 51 of area 12 corresponding to the shortest wavelength are indicated . Reference 61 is window 61 of image sensor(s) 63.

[00157] The arrangement 30 of Fig. 17 comprises an imaging lens 32, a transmission grid 31 (600 l/mm) mounted in front of lens 32 (Edmund Optics lens objective 57907), and a long pass optical filter 33 mounted behind lens 32. already explained with reference to fig 4, this allows producing low optical aberrations for scattered and non-scattered images using the wide field of view of the lens objective.

[00158] Since the grid 31 is mounted in front of the imaging lens 32, it deflects the beams differently for zero and first orders and the imaging lens 32 receives the incoming beams at different angles. In such a configuration, a wide field of view imaging lens 32 is used, which allows incident beams at specific angles to the first order.

[00159] In the arrangement 30 of Fig. 18, both the transmission grid 31 (360 l/mm) and the optical long pass filter 33 are mounted behind the lens 32 (Edmund Optics lens objective 57907). As already explained with reference to Fig. 5, this allows canceling the dependence of the extracted spectra on the position of the object along the optical axis.

[00160] In the arrangement 30 of Fig. 19, long pass optical filter 33 is mounted in front of a lens 32, and the transmission grid 31 (600 l/mm) is mounted behind lens 32 (Edmund Optics lens objective 57907). Furthermore, an additional array of lenses 34 is also mounted behind lens 32. As already explained with reference to Fig. 6, this configuration efficiently allows to separate the scattered and non-scattered images (being approximately doubled compared to the embodiment of Fig. 18) and avoids dependence on the position of the object along the optical axis.

[00161] Now, let us describe further embodiments of the invention involving imaging over a plurality of illumination periods, first with reference to Figs. 20 and 22 and then with reference to Figs. 21 and 23a-b. These additional embodiments can, of course, be combined with any of the embodiments described above.

[00162] Fig. 20 schematically illustrates the generation of an authenticity measure of the object 10 in one embodiment of the imaging system 200. In this embodiment, as a first step, the arrangement of the image sensor 60 generates the unscattered part 40 and part dispersion 50 described above in a plurality of lighting periods t1 , t2 , ..., tn. In one embodiment, n equals 2. In another embodiment, n equals 3. Object 10 is illuminated differently during each lighting period. Each illumination period may span one or two imaging periods (overlapping or non-overlapping), as schematically illustrated with reference to Figs. 35a to 35d.

[00163] Then the authenticity measure is generated. Generating the authenticity measure comprises the following steps.

[00164] First, for each period of illumination ti,(1 < i < n), an intermediate measure of authenticity ki is generated depending, at least, on a relation between the scattered part image 50 (Ai), having its image generated in the illumination period (ti), unscattered part 40 (Bi), being imaged in the illumination period ti, and a part of the reference spectral information, said part of the reference spectral information being associated with the manner in which object 10 was illuminated during the illumination period ti. In one embodiment, the intermediate measure of authenticity ki is generated, for each illumination period ti, by determining, for each illumination period ti, the extent to which the imaged scattered part generated in the illumination period ti corresponds to a convolution of the part non-scattered image generated at illumination period ti and said part of the reference spectral information associated with how the object 10 was illuminated during illumination period ti.

[00165] Second, the authenticity measure m is generated based on the plurality of intermediate authenticity measures generated ki, k2, ..., kn. This is illustrated in Fig. 20 by means of the exemplary equation: m = f(k1, k2 , ..., kn ), where f is a function such as, for example, the arithmetic mean of the intermediate measures of authenticity.

[00166] Figs. 22 is a flowchart of an imaging method corresponding to the process illustrated by Fig. 20, wherein the generation s400 of the authenticity measure of the object i0 follows the arrangement of the image sensor 60 generating image s300 of the unscattered part 40 and the scattered part 50 in a plurality of illumination periods ti, t2, ..., tn . Generating the authenticity measure s400 comprises generating s470, for each illumination period ti, an intermediate authenticity measure ki , as described above, and then generating s475 the authenticity measure m based on the plurality of generated intermediate measures ki , k2, ..., kn.

[00167] In one embodiment, generating s470, for each lighting period ti, the intermediate measure of authenticity ki comprises: determining, for each lighting period ti, to what extent the scattered part with image generated in the lighting period ti corresponds to a convolution of the imaged unscattered part generated in the illumination period ti and said part of the reference spectral information associated with how the object 10 was illuminated during the illumination period ti.

[00168] In one embodiment (not illustrated in Fig. 22), the intermediate authenticity measure ki of each illumination period is generated s470 without waiting for the completion of the imaging step s300 for all illumination periods. That is, step s470 can be performed while step s300 is still in progress. For example, once the image sensor array 60 generates the image of the non-scattered part 30 and the scattered image 50 for illumination period t1, an intermediate measure of authenticity k1 can be generated s470 for illumination period t1 and then stored, so that generation step s475 can be performed later based on all stored intermediate authenticity measures k1 , ..., k n.

[00169] Fig. 21 schematically illustrates the generation of an authenticity measure of the object 10, in another embodiment of the invention. In this embodiment, as in the embodiment described with reference to Figs. 20 and 200, the image sensor arrangement 60 first image the non-scattered part 40 and the scattered part 50 in a plurality of illumination periods t1, t2,... , tn. The value n can, for example, be equal to 2 or 3, and object 10 is illuminated differently during each lighting period. Again, each illumination period may span one or two imaging periods (overlapping or non-overlapping), as schematically illustrated with reference to Figs. 35a to 35d. The authenticity measure is then generated through the following steps:

[00170] The non-scattered part with generated image {B1 , B2, ..., Bn} is processed at least based on the non-scattered part B1 in a first lighting period t1 among the plurality of lighting periods t1 , t2, ..., tn and in the non-dispersed part B2 generated in a second illumination period t2, to produce the dispersed part with processed generated image Bx. All images B1, B2, ..., Bn can also be taken into account to produce the so-called non-scattered part with processed generated image Bx. That is, the processed non-scattered imaged part Bx can be generated based on the non-scattered imaged parts generated in a first to nth lighting periods t1 , t2, ..., tn. Similarly, the processed image-generated scattered part is generated on the basis of at least the scattered part A1 in a first illumination period t1 among the plurality of illumination periods t1, t2, ...,tn and the scattered part A2 in a second illumination period t2 , to produce the so-called scattered part with processed generated image Ax. All imaged scattered parts A1,A2,...,An in the illumination periods t1, t2,...,tn alternatively can be taken into account to produce the processed imaged scattered part Ax.

[00171] Then, the authenticity measure m is generated depending on at least one ratio between the dispersed part with processed generated image Ax, the non-scattered part with processed generated image Bx, and the reference spectral information. In one embodiment, the authenticity measure m is generated based on at least the extent to which the generated image of the processed scattered part Ax corresponds to a convolution of the generated image of the non-scattered processed part Bx and the reference spectral information;

[00172] Figs. 23a and 23b are two flowcharts of imaging methods in two embodiments corresponding to the process illustrated by Fig. 21, wherein the generation s400 of the authenticity measure follows the arrangement of the image sensor 60 generating image s300 of the unscattered part 40 and the scattered part 50 in a plurality of illumination periods t1, t2, ..., tn.

[00173] Namely, referring to Fig 23a, after imaging s300 by the arrangement of the image sensor 60 the non-scattered part 40, and the scattered part 50 in a plurality of illumination periods t1, t2, .. ., tn, the authenticity measure is generated s400.

[00174] The step s400 comprises, firstly generating s482 the so-called non-dispersed part with image generated Bx based on at least the non-dispersed parts B1, B2 with image generated in a first and second lighting period ti, t2 and preferably with basis on all non-dispersed parts B1 B2, ..., Bn imaged in periods of illumination ti, t2, ..., tn. Similarly, the so-called processed imaged scattered part Ax is generated s484 on the basis of at least the imaged scattered parts Ai, A2 on the illumination periods ti, t2, preferably on the basis of all non-scattered parts Ai, . .. An, with image generated in lighting periods ti, ... tn. Then, the authenticity measure m is generated s486 depending on at least one ratio between the scattered part with processed generated image Ax, the non-scattered part with processed generated image Bx, and the reference spectral information.

[00175] In Fig. 23a, steps s484 and s482 are performed sequentially. However, step s482 can also be performed after step s484. In one embodiment, steps S482 and s484 are performed in parallel, as illustrated in Fig. 23b.

[00176] In one embodiment, step s482 can be implemented as follows (also, step s484 can be implemented similarly): First, a weighting factor is calculated based on a statistical processing of the pixel values of the data of the first image Bi (that is, the unscattered part imaged in illumination period t1 ) and the pixel values of the data from the second image B2 (that is, the unscattered part imaged in illumination period t2 ). Then, the data of the third image Bx (that is, the so-called unscattered part with generated image) is generated by calculating a weighted combination using the pixel values of said data of the first image B1, the pixel values of the said data from the second B2 image and said weighting factor. Such an implementation can be carried out to maximize the image contrast between a marking (e.g. a bar code) and the remaining background, as described in PCT application WO 2014/187474 A1 by the same applicant. WO 2014/187474 A1 discloses techniques for improving the image of a mark or code printed on a fluorescent background or other backgrounds. Multiple images of a mark or code are acquired under different lighting conditions, and an image subtraction algorithm suppresses the background to make it easier to extract the printed codes from the images.

[00177] This embodiment, which will be described in greater detail with reference to Figs. 24a to 27 can be considered as a method of improving the spectral recognition and authentication of a mark (such as, for example, a printed mark) on backgrounds (such as, for example, complex fluorescent backgrounds), using a generator of spectral imaging with a dispersive imaging arrangement 30 (such as, for example, a transmission diffraction grating) and background subtraction using differential imaging (as described in WO 2014/187474 A1). Background subtraction using differential imaging as described in WO 2014/187474 A1 will be referred to hereinafter as Differential Illumination Background Subtraction (DIBS) feature, technique or algorithm.

[00178] This embodiment particularly addresses the following potential problems: The non-dispersed part with generated image and the dispersed part with generated image created by means of the dispersive image generation arrangement 30, as discussed above, can overlap with, and for example, the fluorescent background of a can lid (or similar) could pose problems for decoding and spectrum extraction. One embodiment of the invention to reduce the overlapping effect is to optionally use an appropriate mask that hides part of the image of the object 10 to avoid overlapping between the zero-order and first-order images of the code created by means of the array 30. Such a mask however it is physical and can, under certain circumstances, disturb code reading by reducing the usable field of view. Additionally, such a mask, however, can complicate the opto-mechanical design of the imaging system 200.

[00179] The modality based on DIBS aims to solve these problems. It uses images obtained through array 30, which has an overlap between the orders, and background subtraction is applied using the DIBS technique. The DIBS technique reduces the fluorescent (or similar) background effect in the zero-order images (non-scattered part 40) and additionally corrects the first-order images (scattered part 50), thus improving the spectrum-based generation of the authenticity measure . This is particularly advantageous when the fluorescent background has an excitation spectrum that differs from the ink to be authenticated (eg matrix code).

[00180] An example of images of a sample object 10 with a fluorescent background obtained with an image generation system 200 of Fig. 1 is shown in Fig. 24a (an image of a soda can lid without wearing a mask). A region with overlapping zero-order or first-order images of the sample object 10 can be seen in Figure 24a. In this region, it may be difficult or impossible to decode a data matrix due to reduced contrast. This makes extracting the spectrum (to generate the authenticity measure) difficult or it can lead to significant errors.

[00181] Therefore, the image in Fig. 24a has two problems: 1) the visible zero-order background overwhelms the first-order image, and 2) the background emits light that is diffracted in the 1st order and can "spectrally" interfere with the spectral information to be authenticated. The first problem can be treated using a physical mask. The DIBS technique specifically addresses the second problem by significantly reducing the background signal in the image.

[00182] Fig. 24b shows an image of the same object as sample 10 extracted with a physical mask in an embodiment of the invention. No overlap between orders is present, which makes efficient decoding and spectrum extraction possible, but the useful field of view may be limited. Such a limitation may, under certain circumstances, restrict the user to operating the device only with specific guidelines, possibly leading to an increase in authentication time for a sample object 10.

[00183] According to the aforementioned DIBS-based modality, no mask is used, but images are acquired in a plurality of illumination periods t 1 , t2, ..., tn with several different illuminations and then a subtraction of image is performed according to the DIBS technique. This reduces the influence of a fluorescent (or similar) background on decoding (if used) and spectrum extraction.

[00184] For example, the DIBS algorithm can use two images acquired by object illuminated 10 with blue and green light, respectively. As an output of the algorithm, an image is obtained which is the difference of images captured under blue and green lighting. This image usually has better contrast when it comes to the printed code compared to the initial images, thus improving the performance of the decoding engine (if used). Additionally, the resulting image also improves spectral extraction using the first order image (i.e. dispersed part 50) created by means of dispersive imaging array 30. This effect can be explained by the different excitation spectra, both for the ink used to print the code as for the fluorescent background of object 10 (for example, a soda can lid). Paint is excited better in blue than in green, while the bottom of the soda can can be excited equally for both colors. Subtracting the images then leads to increased code contrast and improved spectrum extraction.

[00185] Fig. 25 shows example images of a soda can lid acquired without a physical mask, but excited in two different lighting periods by blue light (left image) and green light (right image), in an embodiment of the invention.

[00186] Fig. 26 shows examples of background images subtracted using the DIBS algorithm, using respectively the linear combinations B - 0.94*G (right image) and 8.22*(B - 0.94*G) (left image), in an embodiment of the invention. In the linear combination B - 0.94*G, B is a first image excited in a first period of blue light illumination, G is a second image excited in a second period of green light illumination, and 0.94 the weighting factor . In the linear combination 8.22*(B - 0.94*G), the significance of B, G and 0.94 is the same as in the first linear combination, and 8.22 is a scale factor. Regarding these linear combinations, the weighting factor and the scale factor, see equation (1) in document WO 2014/187474 A1, page 8, and the corresponding description.

[00187] Thanks to the DIBS algorithm, the treated image is more suitable for decoding and improves generation based on the spectrum of the authenticity measure. Fig. 27 shows examples of spectra extracted with and without applying the DIBS algorithm on acquired images, in an embodiment of the invention; The extracted spectra can be compared in Fig. 27, where images pre-treated with DIBS allow the reconstruction of more accurate spectra.

[00188] Now, we will describe other embodiments of the invention applicable both to imaging over a single period of illumination and to imaging over a plurality of periods of illumination. These additional embodiments can be combined with any of the embodiments described above.

[00189] In one embodiment, the object 10 has a visible or invisible mark 11 (or sign) printed with a printing ink. Such ink contains coloring and/or luminescent agents, such as dye(s) and/or pigment(s) which are typically difficult to produce and reverse engineer. These optical agents can be classified into two main classes: 1) optical agents that produce specific reflective properties under controlled lighting, and 2) optical agents that produce luminescence under controlled lighting.

[00190] The expected spectral response of said optical agents, when subject to particular lighting conditions, is known a priori and constitutes the reference spectral information.

[00191] In the case of reflective properties, the spectral response is called spectral reflectivity, which is the fraction of electromagnetic power reflected per unit wavelength. For example, Fig. 28 shows the spectral reflectivity of two different color pigments (Microlith® from BASF AG, based in Ludwigshafen, Germany) as measured with a spectrophotometer in reflectance mode (e.g. model DU-640 spectrophotometer from Beckman Coulter Inc., based in Brea, California, USA).

[00192] For the reflectivity to be determined, a known broadband illumination source can be used, since the reflected electromagnetic radiation 20 (spectral radiation, which is measured), dependent on the wavelength, depends on the incident spectral composition of illumination (spectral irradiance). Spectral reflectivity can be determined using a calibrated light source (in wavelength) or by comparison with a surface of known spectral reflectivity (such as a white reference surface such as Spectralon® by LabSphere, based in North Sutton, New Hampshire , USA) using an uncalibrated broadband light source. The term “broadband” means that the light source emits at least all wavelengths in the range of interest. Examples of the spectral distribution of the broadband light source are shown for a white LED (e.g. an OSRAM OSLON SSL white LED) in Fig. 29 and tungsten filament bulb (incandescent bulb) in Fig. 30 (Source: Schroeder, D.V., 2003. "Radiant Energy," online chapter for the course, 'Energy, Entropy, and Everything,' Physics Department, Weber State University [accessed May 2016] http://physics.weber .edu/schroeder/eee/chapter6.pdf).

[00193] This can be seen in Figs. 29 and 30 that the reflected spectrum of a given brand strongly depends on the spectrum of the irradiation source. Therefore, the so-called “reference spectral information” should be the spectral reflectivity (reflectance) of the object or mark. In modalities where the reference spectral information is the recorded spectral irradiance, said reference spectral information is, then, intrinsically related to the spectral distribution of the irradiation source, which should preferably be controlled when the reference spectral information is recorded in the first time (allowed) and also when it is measured to determine the authenticity of the object 10.

[00194] A second class of optical agents covers dyes or luminescent pigments and has different requirements when it comes to illumination and measurement.

[00195] Fluorescent dyes and pigments can be selected, for example, from perylenes (eg Lumogen F Yellow 083, Lumogen F Orange 240, Lumogen F Red 300, all available from BASF AG). Fig. 31 (source: WO 2016/042025 A1) shows an example of excitation and emission spectrum of such a fluorescent dye. In particular, it shows the excitation spectrum 601 and emission spectrum 602 of a fluorescent dye (Lumogen® F Orange 240 from BASF AG) added to an ink used for printing as an example of a digital code. Double headed arrow 603 indicates the range of wavelengths where the emission spectrum can be used as reference spectral information. It can be observed from Fig. 31 that the excitation spectrum extends between about 400 and 550 nm and the emission spectrum from about 550 to 700 nm. This requires that the light source emit at least in the excitation region for the fluorescent dye to be excited, but preferably not in the spectral emission region to avoid interfering with the fluorescence emission to be detected, which is typically several orders of magnitude weaker. than direct reflection.

[00196] This illumination and detection scheme is known in the field of fluorescence measurement and typically comprises a narrowband illumination source, such as, for example, a single color LED (a blue at 450 nm or a green at 530 nm can be adapted to excite the Lumogen of Fig. 31) and an optical long pass filter in the detection optical path to cut off any reflection towards the tail of the illumination source in the emission region. Optionally, an optical short-pass filter can also be arranged between the LED and the object 10 to be authenticated.

[00197] Figs. 32 and 33 show the emission and excitation spectra for two exemplary phosphorescent phosphor pigments: Lumilux® blue SN and Lumilux® green SN-F2Y from Honeywell International, Inc., headquartered in Morris Plains, New Jersey, USA. The spectroscopic properties shown in Figs. 32 and 33 were measured on samples printed with screen printing inks using a spectrofluorimeter (Horiba Jobin Yvon Fluorolog model FLIII-22, from Horiba, headquartered in Kyoto, Japan). The approach is the same as for fluorescent dyes or pigments described above. The excitation spectra 501 and 511 and the emission spectra 502 and 522 of two phosphorescent pigments are used to print marks to be authenticated in the form of a sticker, logo or designs. Black arrow 505 in each of Figs. 32 and 33 indicate the peak wavelength of a deep blue LED at 410 nm that can be used to efficiently excite phosphorescent pigments.

[00198] In one embodiment, spectral reference information is generated before operating the system and authentication method. This can be done by recording and recording the extracted spectral information, under the same or very similar lighting and detection conditions (for example, using the same device or instrument) as those to be used in the field.

[00199] In one embodiment, an uncontrolled lighting source can be used, provided that its spectral characteristics can be determined, through a spectral measurement and a subsequent correction can be made before extracting the measured spectral information from the object 10 or tag 11 to be authenticated.

[00200] Fig. 34 is a schematic diagram of an exemplary implementation of a computation unit 700 that may be used in embodiments of the invention, such as, but not limited to, to generate the authenticity measure discussed above.

[00201] As illustrated by Fig. 34, a computing unit 700 may include a bus 705, a processing unit 703, a main memory 707, a ROM 708, a storage device 709, an input device 702, an output device 704 and a communication interface 706. Bus 705 may include a path that allows communication between components of computing unit 700.

[00202] The processing unit 703 may include a processor, a microprocessor or processing logic that can interpret and execute the instructions. Main memory 707 can include a RAM or other type of dynamic storage device that can store information and instructions for execution by processing unit 703. ROM 708 can include a ROM device or other type of static storage device that can storing static information and instructions for use by processing unit 703. Storage device 709 may include a magnetic and/or optical recording medium and driver therefor.

[00203] The input device 702 may include a mechanism that allows an operator to enter information into the processing unit 703, such as a numeric keypad, a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. . Output device 704 may include a mechanism that outputs information to the operator, including a display, printer, speaker, etc. Communication interface 706 may include any transceiver-type mechanism that enables computing unit 700 to communicate with other devices and/or systems (such as a base station, WLAN access point, etc.). For example, communication interface 706 may include mechanisms for communicating with another device or system over a network.

[00204] The computing unit 700 can perform certain operations or processes described in this document. These operations may be performed in response to processing unit 703 executing software instructions contained on a computer-readable medium, such as main memory 707, ROM 708, and/or storage device 709. A computer-readable medium may be defined as a physical or logical memory device. For example, a logical memory device can include memory space within a single physical memory device or distributed across multiple physical memory devices. Each of main memory 707, ROM 708, and storage device 709 may include computer-readable media. The magnetic and/or optical recording media (e.g., readable CDs or DVDs) of storage device 709 may also include computer-readable media. Software instructions may be read into main memory 707 from another computer-readable medium, such as storage device 709, or from another device via communication interface 706.

[00205] The software instructions contained in the main memory 709 can cause the processing unit 703 to perform operations or processes described in this document, such as, for example, generating an authenticity measure. Alternatively, wired circuitry can be used in place of or in combination with software instructions to implement processes and/or operations described in this document. Thus, the implementations described in this document are not limited to any specific combination of hardware and software.

[00206] Figs. 35a to 35d schematically illustrate examples of imaging periods and illumination periods in four embodiments of the invention. These drawings have already been referred to and elaborated on throughout the above description.

[00207] In one embodiment, the imaging system 200 comprises, on the one hand, an imaging device comprising an image sensor arrangement 60 and, on the other hand, a piece of equipment, referred to hereinafter as " imaging accessory", comprising a dispersive imaging arrangement 30.

[00208] In this modality, the image generation device has an integrated camera (including associated lenses) and can be a portable device, such as, for example, at least one of: a cell phone, a smartphone, a conventional telephone, a tablet computer, a phablet, a portable media player, a netbook, a gaming device, a personal digital assistant and a portable computer device. The image sensors incorporated in the imaging device's integrated camera act as an array of image sensor 60 in system 200.

[00209] As mentioned above, the imaging accessory comprises a dispersive imaging arrangement 30, such as, for example, a transmission diffraction grating or any other dispersive element, as already discussed above with reference to Fig. . 1.

[00210] The imaging accessory is attachable, directly or indirectly (for example through a connecting piece of equipment), to the imaging device so that the dispersive imaging arrangement 30 of the imaging accessory is positioned relative to the imaging device 60 array of the imaging device in such a way that the imaging device and the imaging accessory form an imaging system 200 as described above, operable for imaging of an object and generating a measure of object authenticity. In other words, the imaging accessory can be used, for example, to transform a smartphone into a portable imaging and authentication system, as described above. The imaging accessory can, for example, be fixedly positioned over the smartphone's rear camera. The smartphone's processing and communication capabilities can then be used to implement a processing unit 70 of the imaging system 200.

[00211] Furthermore, if the imaging device has a light source (such as, for example, flash LEDs used in a smartphone), said light source can function as a lighting arrangement 210 to illuminate the object 10 to have the image generated and authenticated. A smartphone's light source is typically well suited for reflectivity measurements. Alternatively, lighting arrangement 210 may be provided as part of the imaging accessory.

[00212] This modality is advantageous in that the image generation accessory can be a passive accessory, not requiring additional energy and thus providing an accessible authentication solution.

[00213] Fig. 36 schematically illustrates an imaging system 200 according to the embodiment described above comprising, on the one hand, an imaging device comprising an image sensor arrangement 60, wherein the imaging device is a mobile phone with a camera, and, on the other hand, an imaging fixture 36 comprising a dispersive imaging array 30. In this exemplary optical configuration, the imaging fixture 36 comprises a diffraction grating 31 and a pass filter 33 arranged in front of the cell phone camera 64. The cell phone camera 64 comprises an imaging sensor 60 and an integrated lens 66. Optionally, an additional collimator lens 35 can be positioned in front of the imaging accessory 36.

[00214] The invention additionally concerns the following modalities:

[00215] Modality (X2). The imaging system (200) according to claim 1, wherein the imaging system (200) is an imaging device.

[00216] Modality (X3). An imaging system (200) according to claim 1, comprising an imaging device (100) comprising the image sensor array (60) and the dispersive imaging array (30), in that the imaging device (100) is not configured to actually generate the authenticity measure.

[00217] Modality (X4). Imaging system (200) of embodiment (X2) or (X3) wherein the imaging device is a portable device.

[00218] Modality (X7). Imaging system (200) according to any one of claims 1 to 3 and embodiments (X2) to (X4), in which the imaging system (200) is configured to generate the authenticity measure after arrangement of the image sensor (60) having, in a plurality of illumination periods (t1, t2,..., tn), image the non-scattered part (40) and the scattered part (50); and generating the authenticity measure comprises: generating, for each illumination period (ti), an intermediate authenticity measure (ki) depending at least on a relation between the dispersed part with image generated in the illumination period (ti), the part non-dispersed generated in the period of illumination (ti) and a part of spectral reference information, said part of the spectral reference information being associated with how the object (10) was illuminated during the period of illumination (ti); and generating the authenticity measure (m) based on the plurality of the intermediate authenticity measures generated (k1, k2,..., kn).

[00219] Modality (X8). Image generation system (200), of modality (X7), in which generating, for each lighting period (ti), the intermediate authenticity measure (ki) comprises: determining, for each lighting period (ti), up to at which point the imaged scattered part generated in the illumination period (ti) corresponds to a convolution of the non-scattered imaged part generated in the illumination period (ti) and said part of the spectral reference information associated with the object (10) was illuminated during the illumination period (ti).

[00220] Modality (X9). Imaging system (200) according to any one of claims 1 to 3 and embodiments (X2) to (X4), wherein the imaging system (200) is configured to generate the authenticity measure after the image sensor arrangement (60) having, in a plurality of illumination periods (t1, t2,..., tn), image the unscattered part (40) and the scattered part (50); and generating the authenticity measure comprises: processing the non-scattered imaged part based on at least the non-scattered imaged part generated in a first period of illumination (ti) among the plurality of periods of illumination (t1, t2, tn ) and the unscattered part with image generated in a second illumination period (t2) between the plurality of illumination periods (t1, t2, tn), wherein the illumination conditions during the first illumination period (ti), at least least partially differ from lighting conditions during the second lighting period (t2); processing the scattered imaged part based on at least the scattered imaged part generated in the first illumination period (t1) and the scattered imaged part generated in the second illumination period (t2); and generating the authenticity measure (m) depending on at least one ratio between the scattered part with processed generated image (Ax), the non-scattered part with processed generated image (Bx), and the reference spectral information.

[00221] Modality (X10). Image generation system (200), according to the modality (X9), wherein generating the authenticity measure depends at least on the extent to which the generated image of the processed dispersed part (Ax) corresponds to a convolution of the generated image of the processed non-scattered part (Bx) and reference spectral information;

[00222] Modality (X11). Imaging system (200) according to any one of claims 1 to 3 and embodiments (X2) to (X4) and (X7) to (X10), wherein the dispersive imaging arrangement (30) is positioned relative to the image sensor array (60) so as to allow the image sensor array (60) to image the unscattered part (40) and the scattered part (50) on two portions of the same sensor of image.

[00223] Modality (X13). Imaging system (200) according to any one of claims 1 to 4 and embodiments (X2) to (X4) and (X11), wherein a slit is not used between the dispersive imaging arrangement ( 30) and the object (10) to be imaged.

[00224] Modality (X17). The imaging system (200) of claim 7, wherein the at least one machine-readable code comprises at least one of a linear barcode and a two-dimensional barcode.

[00225] Modality (X18). Imaging system (200) according to any one of claims 5 to and 7 of the embodiment (X17), wherein the marking (11) comprises unique spectral characteristics in at least one region of the marking (11).

[00226] Modality (X19). Image generation system (200) of embodiment (X18), wherein the marking (11) comprises unique spectral characteristics throughout the marking (11).

[00227] Modality (X20). Imaging system (200) according to any one of claims 5 to 7 and embodiments (X17) to (X19), wherein the marking (11) comprises at least one of: optical agents that produce specific reflective properties under controlled lighting, and optical agents that produce luminescence under controlled lighting.

[00228] Modality (X21). System (220), comprising an imaging system (200) according to any one of claims 1 to 7, and modalities (X2) to (X4), (X7) to (X11) and (X17) to (X20), and a lighting arrangement (210) for controlled lighting of the object (10).

[00229] Modality (X23). The imaging method of claim 8, wherein the imaging method is performed by an imaging device.

[00230] Modality (X24). An imaging method according to claim 8, wherein the imaging method is performed by an imaging system (200) comprising an imaging device (100) comprising the imaging sensor arrangement. image (60) and the dispersive imaging arrangement (30), wherein the imaging device (100) does not generate (s400) the authenticity measure.

[00231] Modality (X25). Imaging method of embodiments (X23) or (X24), wherein the imaging device is a portable device.

[00232] Modality (X32). Imaging method according to any one of claims 8 to 14 and embodiments (X23) to (X25), wherein the dispersive imaging array (30) is positioned relative to the image sensor array ( 60) so as to allow the arrangement of the image sensor (60) to image the non-scattered part (40) and the scattered part (50) onto two portions of the same image sensor.

[00233] Modality (X33). Imaging method according to any one of claims 8 to 14 and embodiments (X23) to (X25) and (X32), wherein the dispersive imaging arrangement (30) comprises at least one of a diffractive element , a transmission diffraction grating, a diffraction transmission grating, a volume holographic grating, a reflective diffraction grating, an array comprising a beam splitter and a diffraction grating, and an array comprising a light splitter beam and a dispersive prism.

[00234] Modality (X34). Imaging method according to any one of claims 8 to 14 and embodiments (X23) to (X25), (X32) and (X33), wherein a slit is not used between the dispersive imaging arrangement (30) and the object (10) to be imaged.

[00235] Modality (X37). The imaging method of claim 15 or 16, wherein the marking (11) comprises at least one machine readable code.

[00236] Modality (X38). Modality imaging method (X37), wherein the at least one machine-readable code comprises at least one of a linear barcode and a two-dimensional barcode.

[00237] Modality (X39). Imaging method (200) according to any one of claims 15 and 16 and embodiments (X37) and (X38), wherein the marking (11) comprises unique spectral characteristics in at least one region of the marking (11) .

[00238] Modality (X40). Modality imaging method (X39), wherein the tag (11) comprises unique spectral features throughout the tag (11).

[00239] Modality (X41). Imaging method according to any one of claims 15 and 16 and embodiments (X37) to (X40), wherein the marking (11) comprises at least one of: optical agents that produce specific reflective properties under controlled lighting and optical agents that produce luminescence under controlled lighting.

[00240] Modality (X43). Computer program or set of computer programs comprising computer executable instructions configured, when executed by a computer or set of computers, to perform an imaging method according to any one of claims 8 to 16, and embodiments ( X23) to (X25), (X32) to (X34), and (X37) to (X41).

[00241] Modality (X44). Computer program product or set of computer program products comprising a computer program or set of computer programs according to embodiment (X43).

[00242] Modality (X45). Storage medium storing a computer program or set of computer programs according to the modality (X43).

[00243] Where the terms "processing unit", "storage unit", etc., are used in this document, no restrictions are made as to how these elements may be distributed and as to how elements may be assembled. That is, the constituent elements of a unit can be distributed in different software or hardware components or devices to promote the intended function. A plurality of distinct elements can also be assembled to provide the intended functionalities.

[00244] Any of the aforementioned units, such as, for example, processing unit 70, or devices, such as, for example, image generation device 110, can be implemented in hardware, software, port arrangement field-programmable (FPGA), application-specific integrated circuit (ASICs), firmware, or similar.

[00245] In other embodiments of the invention, any of the processing units, storage unit, etc. mentioned above, is replaced by processing means, storage means, etc. or processing module, storage module, etc. respectively, to realize the functions of processing unit, storage unit, etc.

[00246] In further embodiments of the invention, any of the procedures, steps or processes described above can be implemented using computer-executable instructions, for example in the form of computer-executable procedures, methods or the like, in any type of computer language and/or in the form of software incorporated in firmware, integrated circuits or the like.

[00247] Although the present invention has been described based on the detailed examples, the detailed examples only serve to provide the person skilled in the art with a better understanding, and are not intended to limit the scope of the invention. The scope of the invention is much better defined by the appended claims. Abbreviations: ASICs application-specific integrated circuit a.u. CASSI arbitrary units CCD Aperture Coded Instantaneous Spectral Imager Charge Coupled Device CMOS Complementary Metal Oxide Semiconductor CTIS CT Imaging Spectrometer DIBS Differential Illumination Background Subtraction FOV Field of View FPGAField Programmable Gate Array KNN k Algorithm -nearest neighbors l/mm lines per mm LED light-emitting diode LTI linear invariant translation MAFC multi-aperture filtered camera MIFTS multi-image Fourier transform spectrometer NIR near-infrared RAM random access memory ROM read-only memory SHIFT spectrometer with Fourier transform instantaneous hyperspectral imaging SVM vector machine support SWIR short-wavelength infrared UV Ultraviolet WLAN wireless local area network

Claims (11)

1. Image generation system (200) for generating an image of an object (10) and generating a measure of authenticity of the object (10), the image generation system (200) comprising: one or more image sensors, the one or more image sensors hereinafter referred to as the "image sensor array" (60); and one or more optical elements, the one or more optical elements being referred to hereinafter as "dispersive imaging arrangement" (30), wherein the dispersive imaging arrangement (30) is performed so that when the Electromagnetic radiation (20) from the object (10) illuminates the dispersive imaging arrangement (30), at least part of the electromagnetic radiation (20) splits in different directions until at least one non-scattered part (40 ) and a dispersed part (50); and is positioned with respect to the image sensor array (60) in a manner to allow the image sensor array (60) to image said unscattered portion (40) on a first portion of the image sensor array (60). image (60) and said dispersed part (50) in a second portion of the image sensor array (60) the image generation system (200) being configured to, after the image sensor array (60), in at least at least one imaging period, having generated the image of the non-scattered part (40) and, of the dispersed part (50), generating a measure of authenticity of the object (10) depending at least on a relationship between the dispersed part with generated image, the non-scattered part with generated image and the reference spectral information, characterized by the fact that the generation of the authenticity measurement comprises one of: deconvolution of the dispersed part with generated image by the non-scattered part with generated image and determination the extent to which the result corresponds to the reference spectral information; deconvolution of the scattered imaged part generated by the reference spectral information and determining the extent to which the result corresponds to the non-scattered imaged part; and convolution of the non-scattered imaged part and the reference spectral information, and determining the extent to which the result corresponds to the scattered imaged part. 2. Image generation system (200), according to claim 1, characterized in that the dispersive image array (30) comprises at least one of: a diffraction element; a transmission diffraction grating; a diffracted transmission grating; a holographic volume grid; a reflective diffraction grating; an arrangement comprising a beam splitter and a diffraction grating; and an arrangement comprising a beam splitter and a dispersive prism. 3. Image generation system (200), according to claim 1 or 2, characterized in that it is for image generation of an object (10) bearing a marking (11). 4. Image generation system (200), according to claim 3, characterized in that the generation of the authenticity measure additionally comprises the decoding of a code from the marking (11) within the non- dispersed with generated image and verification of code authenticity. 5. Image generation system (200), according to claim 3 or 4, characterized in that the marking (11) comprises at least one machine-readable code. 6. Imaging method for generating an image of an object (10) and generating a measure of object authenticity (10), the imaging method making use of: one or more image sensors, the one or more image sensors being referred to hereinafter as "image sensor arrangement" (60); and one or more optical elements, the one or more optical elements being referred to hereinafter as "dispersive imaging arrangement" (30), wherein the dispersive imaging arrangement (30) is performed so that when the electromagnetic radiation (20) from the object (10) illuminates the dispersive imaging arrangement (30), at least part of the electromagnetic radiation (20) splits in different directions until at least a non-scattered part (40) and a part scattered (50); and is positioned with respect to the image sensor array (60) so as to allow the image sensor array (60) to image said unscattered portion (40) in a first portion of the image sensor array (60) and said dispersed portion (50) in a second portion of the image sensor array (60); and the imaging method comprising: imaging (s300) by arraying the image sensor (60) in at least one period of imaging the non-scattered part (40) and the scattered part (50) ; and generating (s400) a measure of authenticity of the object (10) depending on at least one relationship between the dispersed part with generated image, the non-scattered part with generated image and the reference spectral information, characterized by the fact that the generation (s400) of the authenticity measure comprises one of the steps of: deconvolution (s410) of the image-generated dispersed part by the image-generated non-scattered part and determination (s420) of the extent to which the result corresponds to the reference spectral information ; deconvolution (s430) of the scattered imaged part generated by the reference spectral information and determining (s440) the extent to which the result matches the non-scattered imaged part; and convolution (s450) of the unscattered imaged part and the spectral information of the reference, and determining (s460) the extent to which the result corresponds to the scattered imaged part. 7. Image generation method, according to claim 6, characterized in that it comprises image generation (s300), by means of the image sensor arrangement (60), in a plurality of lighting periods (ti , t2, ..., tn), the non-dispersed part (40) and the dispersed part (50), where the generation (s400) of the authenticity measure comprises: the generation (s470), for each lighting period (ti), an intermediate measure of authenticity (ki) depending, at least, on a ratio between the scattered part with image generated in the illumination period (ti), the non-scattered part with image generated in the illumination period (ti ) and a part of the reference spectral information, said part of the reference spectral information being associated with the way in which the object (i0) was illuminated during the period of illumination (ti); and generating (s475) the authenticity measure (m) based on the plurality of generated intermediate authenticity measures (ki, k2, ., kn). 8. Image generation method, according to claim 6, characterized in that it comprises image generation (s300), by means of the image sensor arrangement (60), in a plurality of lighting periods (ti , t2, ..., tn), the non-dispersed part (40) and the dispersed part (50), where the generation (s400) of the authenticity measure comprises: processing (s482) the non-dispersed part with image generated on the basis of at least the unscattered imaged part generated in a first illumination period (ti) among the plurality of illumination periods (ti, t2, ., tn) and the unscattered part imaged in a second lighting period (t2) among the plurality of lighting periods (ti, t2, ., tn), wherein the lighting conditions during the first lighting period (ti) at least partially differ from the lighting conditions during the second lighting period (t2); processing (s484) the scattered imaged part based on at least the scattered imaged part generated in the first period of illumination (ti) and the scattered part generated in the second period of illumination (t2); and generating (s486) the authenticity measure (m) depending on at least one ratio between the scattered part with processed generated image (Ax), the non-scattered part with processed generated image (Bx) and the reference spectral information. 9. Image generation method, according to any one of claims 6 to 8, characterized in that it is for generating an image of an object (i0) carrying a marking (ii). 10. Image generation method, according to claim 9, characterized in that the generation (s400) of the authenticity measure additionally comprises the decoding (s492) of a marking code (ii) within the unsigned part -scattered with generated image and verification (s494) of code authenticity. 11. Image generation method, according to any one of claims 6 to 10, characterized in that it additionally comprises a step of controlled lighting of the object (10).