JP2017038368A - System and method classifying image acquisition setting for pattern stitching and decoding by using multiple captured images - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide imaging system and method determining an order for applying to image acquisition setting while acquiring multiple images.SOLUTION: A system and a method acquire and decode multiple images. A first image is acquired and then processed, and decoding of a symbol is tried. Contribution of the first image to decode trial is specified. An updated acquisition setting order is determined based, at least partially, on the contribution of the first image to decode trial. A second image is acquired or processed based, at least partially, on the updated acquisition setting order.SELECTED DRAWING: None

Description

  The present technology relates to an imaging system and method for decoding an image, and more particularly, to an imaging system and method for determining an order to apply to an image acquisition setting while acquiring a plurality of images.

  An imaging system uses an image acquisition device that includes a camera sensor that supplies information of a visual object. The system then interprets this information based on various algorithms and implements programmed decision making and / or specific functions. Objects are typically illuminated for images that are most efficiently acquired by sensors in the visible and near-visible light range.

  Symbol reading using an image sensor (commonly referred to as a “barcode” scan) imposes the target of the image acquisition sensor (CMOS camera, CCD, etc.) there on the object containing the symbol (eg “barcode”) Get the image. The symbol contains a predetermined pattern set representing a character or shape order group, and an onboard data processor (eg, a microcomputer) provides useful information about the subject (eg, serial number, model, model, price, etc.). Derived from the pattern. Symbols / barcodes are available in various shapes and sizes. The two most commonly used symbol types used for target marking and identification are from so-called one-dimensional barcodes consisting of lines of vertical stripes of varying width and spacing, and two-dimensional arrays of dots or rectangles It is a so-called two-dimensional barcode configured.

  In many imaging applications, an individual image of a symbol becomes partially unreadable due to surface features, illumination, partial movement, or other variation vibrations or magnitudes. For example, an imaging system acquires multiple images of a target symbol as the target moves on a conveyor line. With this arrangement, relative movement between the imaging device and the object, phenomena that are detrimental to image analysis (eg, as described above) occur. Therefore, the symbols of one or more images cannot be partially read.

  The exemplary machine vision detector acquires multiple images of the object / feature of interest as it passes through the field of view, and each image is used to perform detection and / or trigger functions.

  This embodiment provides a system and method for determining the order for applying image acquisition settings, such as exposure time between multiple image acquisitions and illumination settings, overcoming the disadvantages of the prior art, and certain implementations. In form, it is useful for decoding symbol data based on information from multiple images of the symbol. Multiple images are acquired and a score number associated with the attempted decoding of the image is assigned to the image. The acquisition setting table is then classified based at least in part on the assigned score, and different image acquisition settings are used in subsequent shooting or processing of the image set in the order based on the classification order of the acquisition setting table.

  Accordingly, some embodiments include a method of decoding a symbol using an image of the symbol. The method includes generating a composite model of a symbol including a model of a plurality of known features of the symbol. First and second images for a first read cycle are acquired, wherein the first image is acquired using a first acquisition setting and includes a first symbol data region, and the second image is It includes the second symbol data area acquired using the second acquisition. A composite model of symbols is extracted by comparing with the first and second images and the first and second binary matrices. The first binary matrix is at least partially combined with the second binary matrix, and a composite binary matrix is generated in which the composite binary matrix is a decodable representative of the symbols. An attempt to decode the symbol is made based at least in part on the composite binary matrix. Respective first and second contributions to the attempt to decode the symbols of the first and second images are identified. Based on at least in part on the first and second contributions, an updated acquisition setting order is determined for the first and second acquisition settings. The imaging device may acquire a third image for the second read cycle using a third acquisition setting determined based at least in part on the updated acquisition setting order.

  Other embodiments include another method of decoding a symbol using an image of the symbol, wherein the imaging device uses an initial sequence of each acquisition setting that is determined based at least in part on the initial acquisition setting order. Thus, the first image is acquired in the first acquisition order. The method includes processing the first image and attempting to decode the symbols by at least partially stitching image data from two or more first images. At least one contribution corresponding to the attempt to decode the symbol may be identified for two or more first images acquired using at least one of the initial acquisition settings. An updated acquisition setting order may be determined for the set of initial acquisition settings based at least in part on the at least one contribution. A second image is acquired by the imaging device using an updated sequence of second acquisition settings determined based at least in part on the updated acquisition setting order, or the updated acquisition setting order The second image may be processed in an attempt to decode using a processing order that is determined based at least in part.

  In accordance with the above, in one embodiment, a system is provided for decoding a symbol using a symbol image. The imaging device may be configured to acquire a plurality of images with the acquired images including respective symbol data regions. A processor operably coupled to the imaging device may be configured to receive a first plurality of images for a first read cycle of the system. A first plurality of images are acquired by the imaging device in a first acquisition order using respective acquisition settings and acquired using first and second acquisition settings determined according to an initial acquisition setting order, respectively. First and second images to be included. The processor is further configured to execute a data stitching algorithm to generate a symbol composite model that includes a model of a plurality of known features of the symbol and compare the symbol composite model to at least the first and second images. , Converting the first symbol data area of the first image into a first binary matrix, converting the second symbol data area of the second image into a second binary matrix, and converting the first binary matrix into the first binary matrix; Generating a composite binary matrix including at least partially composited binary binary matrices including decodable representatives of symbols. A processor may be configured to attempt to decode the symbol based at least in part on the composite binary matrix and to receive a second plurality of images for a second read cycle of the system. A second plurality of images of the image may be acquired by the imaging device in a second acquisition order using the updated acquisition settings determined according to the updated acquisition setting order. An updated acquisition setting order may be determined based at least in part on the first and second contributions to the respective attempts to decode the symbols of the first and second images.

  In yet another embodiment, a system for decoding a symbol using a symbol image is provided. The imaging device may be configured to acquire a plurality of images with each acquired image including a symbol data region. A processor operably coupled to the imaging device may be configured to receive a first plurality of images for a first read cycle of the system. The first plurality of images are acquired by the imaging device in a first acquisition order using the respective acquisition settings and each acquired using first and second acquisition settings determined according to the initial acquisition setting order First and second images to be included. The processor is further configured to execute a data stitch algorithm that generates a composite model that includes a model of a plurality of known features of the symbol, and that combines the composite model of the symbol with at least the first and second images. The first symbol data area of the first image is converted to a first binary matrix, the second symbol data area of the second image is converted to a second binary matrix, and the first binary Including at least partially combining the matrix with a second binary matrix to generate a composite binary matrix including a decodable representative of the symbols. The processor further includes attempting to decode the symbol and receives a second plurality of images for the second read cycle of the system based at least in part on the composite binary matrix. The second plurality of images may be processed for decoding in a processing order determined based at least in part on the first and second contributions to the attempt to decode the symbols of the first and second images. .

  In order to implement the above and related state-of-the-art, the technology then has features that are fully described below. The following description and the annexed drawings set forth in detail certain illustrative aspects of the technology. These aspects, however, only illustrate some of the various ways in which the principles of the technology are used. Advantages and novel features of other aspects and techniques will become apparent from the following detailed description of the technology when considered in conjunction with the drawings.

1 is a schematic diagram of an exemplary vision system including a stationary scanner for acquiring multiple images of an object, according to this embodiment. FIG. FIG. 2 is a diagram of the characteristics and format of various two-dimensional matrix symbols. FIG. 2 is a diagram of the characteristics and format of various two-dimensional matrix symbols. FIG. 2 is a diagram of the characteristics and format of various two-dimensional matrix symbols. FIG. 2 is a diagram of the characteristics and format of various two-dimensional matrix symbols. FIG. 2 is a diagram of the characteristics and format of various two-dimensional matrix symbols. FIG. 2 is a diagram of the characteristics and format of various two-dimensional matrix symbols. It is a figure of the promising candidate area | region and background noise arrange | positioned in an image. It is a figure which shows two promising candidate area | regions with the 1st and 2nd image of object. It is a figure which shows two promising candidate area | regions with the 1st and 2nd image of object. 10 is an image showing a binary matrix of first promising candidates in the first image of FIG. 9. 10 is an image showing a binary matrix of second promising candidates in the first image of FIG. 9. FIG. 11 is an image showing a binary matrix of first promising candidates in the second image of FIG. 10. FIG. 11 is an image showing a binary matrix of second promising candidates in the second image of FIG. 10. FIG. 11 is an image showing a cumulative binary matrix obtained by stitching the binary matrix of the first promising candidate in the first image of FIG. 9 and the binary matrix of the first promising candidate in the second image of FIG. 10. 10 is an image showing a cumulative binary matrix obtained by stitching the binary matrix of the second promising candidate in the first image of FIG. 9 and the binary matrix of the second promising candidate in the second image of FIG. 10. A first image of the symbol is shown. FIG. 17 shows a second image of the same or the same symbol in FIG. 17, showing the symbol moved slightly. A composite model that can be used for correlation techniques is shown. The correlation between the composite symbol model and the second image represents the highest symbol in the size and position parameter space. The refined promising region in the second image is shown. FIG. 5 shows a first image of a symbol using bright field illumination with a promising region surrounding a corrupted data region. FIG. 23 shows a second image of the symbol of FIG. 22 with an unknown pole. Fig. 4 shows a first DOH image using light-on-dark. Figure 2 shows a second DOH image using dark-on-light. FIG. 24 shows a decodable stitch and cumulative binary matrix from the first image of FIG. 22 and the second image of FIG. It is the schematic of an image acquisition and score allocation operation | movement with an acquisition setting table. FIG. 28 is a schematic diagram of another acquisition operation with the acquisition setting table of FIG. 27 classified based on one embodiment of the disclosure. It is a figure which shows the table with the example image acquisition setting index of a score allocation, table classification, and image acquisition operation | movement. FIG. 30 shows a table representing a read cycle set for an image shot with image acquisition settings from the table of FIG. 29 and a score assignment for an image based on the result of the read cycle. 30 shows the score assignment of FIG. 30 adjusted to calculate the average classification value for the acquisition settings table. FIG. 32 illustrates an exponential moving average of classification values calculated based on the adjusted score assignment of FIG. The classification order of the averaged classification value of FIG. 32 is shown. The first table shows the classification order of FIG. 32, and the second different table shows image acquisition settings that contribute to the successful symbol decoding represented by the separation table. FIG. 35 shows an updated order of image acquisition settings based on the first and second tables of FIG. Fig. 4 shows different sets of images used for different decoding attempts.

  While the technology is subject to various modifications and alternative forms, specific embodiments are shown by way of example in the drawings and are described in detail herein. It should be understood, however, that the intention is not to limit the technology to the specific forms disclosed by the description of the specific embodiment, but rather, as defined in the appended claims. All modifications, equivalents, and alternatives within the spirit and scope of the present invention.

  Various aspects of the subject technology will now be described with reference to the accompanying drawings, wherein like reference numerals correspond to like elements throughout the several views. It will be understood, however, that the drawings and detailed description below are not intended to limit the invention-specific matter to the particular forms disclosed. On the contrary, it is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention specific matters.

  The terms “component”, “system”, and “device” as used herein refer to hardware, a combination of hardware and software, software, software for execution. It is what you point to. The term “exemplary” herein is used to mean providing as “example”, “instance”, or “illustration”. Any aspect or design described herein as “example” is not necessarily to be construed as preferred or advantageous over other aspects or designs.

  Furthermore, the described invention specifics may be implemented as a system, method, or product using standard programs and / or design techniques and / or programming that generates hardware, firmware, software, or any combination thereof. May control an electronic-based device that implements the aspects described herein.

  Unless specified or limited, the terms “connected”, “coupled”, and variations thereof are widely used to implement both direct and indirect implementation, connection, support, and coupling. It reaches. Further, “connected” and “coupled” are not limited to physical or mechanical connections or couplings. As used herein, unless explicitly stated, “connected” means that one element / feature is directly or indirectly connected to another element / feature and is not necessarily electrical or mechanical. Means that. Similarly, unless explicitly stated, “coupled” means that one element / feature is directly or indirectly coupled to another element / feature and is not necessarily electrical or mechanical.

  As used herein, the term “processor” may include one or more processors and memory and / or one or more programmable hardware elements. As used herein, the term “processor” is intended to include any type of processor, CPU, microcontroller, digital signal processor, or other device capable of executing software instructions.

  As used herein, the term “memory” refers to magnetic media or hard disks, optical storage, or non-volatile media such as flash memory, random access memory (RAM) such as DRAM, SDRAM, EDORAM, RAMBUSRAM, DRDRAM, etc. A volatile medium such as a system memory, or a mounting medium such as a CD-ROM or a flexible disk on which a program is stored and / or data communication is buffered. The term “memory” may include other types of memory or a combination thereof.

  Embodiments of the technology are described below using schematic diagrams illustrating either the structure or the process of the embodiments used to implement the embodiments of the technology. Providing embodiments of the technology using schematic diagrams of this method is not to be construed as limiting its scope. The present technology contemplates electronic device structures and systems and methods for stitching and decoding images using data combined from multiple captured images.

  Various embodiments of a machine vision system are described with respect to a fixed-mount scanner that applies symbols to a two-dimensional matrix symbol and decodes a symbol based on a region of interest of a plurality of images of the symbol having unreadable regions. Can be combined to generate a symbol decodable image. This is because the technical features and advantages are well suited for this method. However, it should be understood that various aspects of the technology can be applied to robot-controlled scanners, handheld imaging systems, and imaging applications, which are combined from machine-readable symbols, imaging systems, and multi-shot images. Other and forms are included, including any other imaging system that may benefit from the ability to decode symbols using image data.

  FIG. 1 illustrates an example machine vision system applied to acquire one or more images 32 of an object 34 that includes machine readable symbols 36. A conveyor 38 conveys the object 34 and causes relative movement between the object 34 and the field of view 40 of the imaging device 42. The example machine vision system may be used as a non-limiting example, among others, in manufacturing assembly, test, measurement, and / or control applications. The machine vision system 30 may use image acquisition software 44 that can perform any of various types of image acquisition.

  The imaging device 42 includes a processor 46 used, for example, for image processing and decoding. The processor 46 may be coupled to or part of the visual sensor 48 or may be locally connected to the visual sensor 48. The processor 46 may be encoded with the image acquisition software 44, or in some embodiments the image acquisition software 44 may run on another computer 50 or processor. Among other things, the image acquisition software 44 may be configured to acquire a plurality of images, control illumination, acquire image data, and process / decode the acquired data into usable information within a single reading operation.

  The imaging device 42 may also have a memory medium 52 coupled to the visual sensor 48 and / or the processor 46. A memory medium 52 is used to store the scanned or processed image 32 or to buffer data and communications. A communication line 54 may also be coupled to the imaging device 42 to provide a connection point to any computer 50. A computer 50 may be used, for example, for uploading and downloading scanned or processed images 32. It will be appreciated that wireless communication may also be considered. In this example, the imaging device 42 may be a conventional fixed mount scanner that can provide high and / or low angle illumination, or a combination of high and low illumination.

  Various embodiments described herein combine image data from a plurality of images 32 of the object 34 to decode symbols 36 that would otherwise not be decodable from individual images. Specifically, various embodiments are described in terms of imaging and decoding two-dimensional matrix symbols. In this example, a symbol 36 is affixed to the surface of a generally flat object 34. Because the object 34 is sometimes not properly illustrated or partially covered for any other reason, certain portions of the symbol 36 may become unreadable.

  2-7, two-dimensional matrix symbols 56 may be composed of one or more data areas 58 including nominal square symbol modules 60 arranged in a regular array structure. The data area 58 may be partially surrounded by a generally “L” shaped finder pattern 62 and the data area 58 may be surrounded by a quiet zone 64 on all sides. Timing pattern 66 provides the number of matrices in symbol 36. FIG. 5 shows an example of the dark-on-light two-dimensional matrix symbol 56, and FIG. 6 shows an example of the light-on-dark two-dimensional matrix symbol 70 of the symbol 70. An alignment pattern 72 may also be included and is typically used with a larger grid size symbol (see FIG. 7).

  The machine vision system 30 identifies the position of a promising candidate using symbol identification software 74 that identifies the position of a two-dimensional matrix symbol based on the rectangular or square shape of the symbol or the unique finder pattern 62 and the timing pattern 66. In some embodiments, the image acquisition software 44 and the symbol location software 74 may be combined into one software application, and in other embodiments, the image acquisition software 44 and the symbol location software 74 may be separate software applications. Either or both of image acquisition software 44 and symbol location software 74 may reside and execute on computer 50 or imaging device 42.

  One embodiment of a symbol location algorithm is described in US Pat. No. 7,016,539 and incorporated herein. Other symbol location algorithms are available and will be considered for use. In use, the symbol location software 74 can locate the candidate symbol by looking for the finder pattern 62 and / or timing pattern 66 of the two-dimensional matrix symbol. If the symbol data area 58 is damaged and the symbol cannot be decoded, the symbol position specifying software 74 may locate a plurality of promising candidate areas that match the finder and timing pattern to some extent. Even if a promising candidate region is located, it may be a symbol region that cannot be decoded if there is not enough data available for the image. A promising candidate region may be promising if, for example, 65 percent or more of the symbol module 60 matches and is applicable to the expected funder pattern 62, timing pattern 66, and alignment pattern 72.

  With reference to FIG. 8, a promising candidate region 76 is found using a symbol location algorithm 74. If the remaining symbols 78 of the image 80 such as text and numbers are considered as background noise and do not contain any or sufficient features required for a two-dimensional matrix symbol such as the finder pattern 62 and timing pattern 66. The symbol location software 74 ignores it. As described above, since the image acquisition software 44 can acquire a plurality of images, if the symbol data area 58 is damaged and the symbols cannot be decoded, the symbol position specifying software 74 can generate a plurality of promising candidate areas 76 from the plurality of images. Adjust. In certain embodiments, different image acquisition parameters may be used for multiple images.

  With reference to FIGS. 9 and 10, a first image 84 and a second image 86 are shown. A first image 84 taken of the subject shows two promising candidate regions 88 and 90. Although the second image 86 is another image obtained by photographing the same object and shows the same two promising candidate areas 88 and 90, the usable image data in the second image 86 is the first image because of certain conditions. This is different from the image data that can be used for the image 84. The image data from the two images 84 and 86 or a plurality of images are combined to generate decodable data.

  As seen in FIG. 9, the symbol position specifying software 74 cannot determine that the area is a false promising area because the promising candidate area 88 includes a feature such as a finder pattern searched for by the symbol position specifying algorithm. This false candidate area may be an image of another symbol system, text, texture or the like. The symbol location software 74 can determine that the promising area 90 is a two-dimensional matrix symbol, and the promising data area 92 and the right damaged area 94 (there is no symbol module 60 present in the damaged area 94). Including.

  FIG. 10 shows a second image 86 of two promising candidate regions 88 and 90. In this second image, the promising candidate area 88 is still a false promising area because it includes features that the data module cannot decode even if the symbol location software searches. The symbol location software 74 again determines that the promising candidate region 90 is a two-dimensional matrix symbol, and that region includes a different promising data region 96 surrounding the damaged data region 98 on the left side in the second image 86.

  The promising candidate area 88 from the first image 84 is combined with the promising candidate area 88 from the second image 86, and the promising candidate area 90 from the first image 84 is similarly merged with the promising candidate area from the second image 86. In an attempt to generate decodable data for compounding with 90, a binary matrix of two promising candidate regions 88 and 90 is generated and “stitched” together using the data stitching algorithm 100. FIGS. 11-16 illustrate the generation of a binary matrix of promising candidate regions 88 and 90 and the progress of the stitches.

  FIG. 11 shows a binary matrix 102 of promising candidate regions 88 generated from the first image 84. FIG. 12 shows a binary matrix 104 of promising candidate regions 90 also generated from the first image. Similarly, FIG. 13 shows the binary matrix 106 of the promising candidate area 88 generated from the second image 86, and FIG. 14 shows the binary matrix 108 of the promising candidate area 90 also generated from the second image.

  FIGS. 15 and 16 show stitched binary matrices developed from both the first image 84 and the second image 86 for both the promising candidate region 88 and the promising candidate region 90. In FIG. 15, the stitched binary matrices 102 and 106 of the promising candidate area 88 from the first image 84 and the second image 88 remain undecoded from the cumulative binary matrix 107. Features such as finder patterns and / or timing patterns are not detected by the symbol location software 74. Conversely, in FIG. 16, stitch binary matrices 104 and 108 of promising candidate regions 90 from the first image 84 and the second image 86 can be decoded from the cumulative binary matrix 109. As can be seen, both the finder pattern and the timing pattern 66 can be detected by the symbol location software 74.

  In some embodiments, when multiple images of the same symbol are acquired, the position of the symbol and / or the promising candidate region of the symbol changes between images. This is due to changes in illumination or just the movement of the object. By using a relationship (or other comparison) between the symbol synthesis model and the available symbol data 110 in the current image of the symbol, the embodiment of the technique looks for location related, here called correspondence. Address the changing positions of symbols in multiple images. The data stitch algorithm 100 assumes that the changing position is modeled using a known affine transformation technique. When the symbol location software 74 operates on successive images (not necessarily the next image or images) acquired on the object 24, the candidate candidates previously acquired by the symbol location software 74 or the data stitch algorithm 100 A correspondence, for example an association, between the region and the symbol data 110 in the current image can be established.

  FIG. 17 shows a first image 114, FIG. 18 shows a continuation or second image 116 of the same symbol 112, in relation to where the second image 116 is placed in the first image 114 of the symbol 112. The symbol 112 is moving (slightly lower left). Because of this movement between the first and second images 114 and 116, correspondence between these images (another comparison) is developed.

  Referring to FIG. 19, by a corresponding (other comparison) technique, a composite model 118 can be generated using the data stitch algorithm 199. The synthesis model 118 is a model of known features of a particular symbol, the two-dimensional 8 × 32 matrix symbol in this example. A composite model 118 can be generated by using at least one of the known finder pattern 62, timing pattern 66, and possible alignment pattern 72. Correspondence (or other comparison) is done using known image analysis methods, including, for example, gray level image analysis or known filter image analysis. In this example, a dot filter analysis, Hessian Cross Determinant of Hessian (DOH), was used to generate a set of features that is a set of facilitating dots 120. DOH is a commonly known technique used to promote dots. The method of finding correspondence is extended to more complex methods such as perspective models and polynomial models depending on the application and speed requirements. Using DOH technology, the known module 60 can generate a higher DOH response. The feature set changes for different applications.

  Referring to FIG. 20, using a correspondence (or other comparison) technique, a correspondence between the composite model 118 and the moved second image 116 is found. Correspondence can be established by maximizing the correspondence score in the parameter space including scale, angle, and transformation. The shifted set 122 of symbol modules 66 has a higher DOH response than the module position 124 established from the first symbol region in the first image 114, ie, the correspondence shown for the composite model 118. The shifted set 122 is the new module position established by correlating the composite model 118 with the DOH response. Finally, with the correspondence established for the composite model 118, the second image 116 is refined based on the correspondence to produce a refined promising region 126, as shown in FIG. For the new refined second image 126, the data stitch algorithm 100 is used to combine the data matrix from the first image 114 with the data matrix from the refined promising region 126, as described above. Stitch. The confidence of each sampling point, the sampling point being the symbol module 66, is obtained and the corresponding accumulated binary matrix 109 (as seen in FIG. 16) is updated until the accumulated binary matrix 109 result can be decoded.

  In some embodiments, the data stitch algorithm 100 can analyze images with the same or opposite poles. FIG. 22 shows a first image 128 of a symbol 130 using bright field illumination with a promising candidate area 132 and a damaged area 134 on the left side. When the second image 140 (see FIG. 23) is acquired and the plurality of second images is unknown, the data stitch algorithm 100 is used to analyze the promising candidate region in the second image to obtain the second The pole of the image 140 is determined.

  24 and 25, using the second image 140, two DOH images, a first DOH image 136 using a light-on-dark filter (FIG. 24), and a dark-on-light filter (FIG. 25) were used. A second DOH image 138 is generated. Then, as described above, a correspondence-based method is applied to both the first DOH image 136 and the second DOH image 138 to look for correspondences in both DOH images 136 and 138. In this example, the corresponding score from the first DOH image 136 using the light on dark filter is higher than the corresponding score from the second DOH image 138 using the dark on light filter. Based on the high correspondence score for the first DOH image 136 using the light on dark filter, the pole of the second image 140 analyzed by the data stitch algorithm 100 is determined to be light on dark.

  With the poles determined, the data stitching algorithm 100 stitches together the data matrix from the first image 128 and the data matrix from the analyzed second image 140 as previously described. Process as follows. FIG. 26 shows images 146 of binary matrices 142 and 144 that are decodable stitched and accumulated (or otherwise at least partially composited) from first image 128 and second image 140, respectively.

  In some machine vision systems, such as machine vision system 30, different image acquisition settings such as exposure time or lighting settings may be made available for image acquisition. In general, various lighting characteristics, including lighting settings for specific image acquisition, bright and dark field settings, power range (specifically as applied to bright or field settings), extreme values, etc. Can be identified. Here various examples address lighting settings as exemplary image acquisition settings for various lead cycles. In other embodiments, other image acquisition settings such as exposure time or other settings may additionally (or alternatively) be used.

  Images acquired using a specific image acquisition (eg lighting) setting may be different from other images due to various factors, including environmental factors in the associated workplace, obstacles, shadows caused by robots or other devices, etc. An attempt to decode more successfully than an image acquired using acquisition settings may contribute more easily. Thus, it would be useful to provide a method and system that prioritizes image acquisition settings (and images acquired with image acquisition settings) that are used for decoding attempts and consecutive decoding attempts. In certain embodiments, this may be an example of editing the acquisition table for successive attempts to decode the image and prioritizing the image acquisition settings based at least in part on the editing order. A method or system for determining may be included. For example, an imaging manager (eg, a manager module for a program that images and analyzes semiconductor wafer-like symbols) can be configured to determine a preferred order of image acquisition settings for image analysis, and images within the preferred order. It can be selected to decode based on the location of the image acquisition settings used to acquire.

  In some embodiments, image acquisition settings (eg, lighting) may be stored in an acquisition setting table with multiple entries. In such a case, for example, each entry in the acquisition setting table includes at least an image acquisition setting (eg, lighting) field, and a specific image acquisition setting (eg, a specific lighting mode or image) associated with the corresponding entry in the acquisition setting table. Exposure, image gain, image offset).

  In one embodiment, a specific number or alphanumeric code is used in the image acquisition setting field to specify a specific lighting setting (or other setting) in the corresponding table entry. For example, illumination modes such as BF1, BF2, and BF3 are used to identify specific brightness field modes, and illumination mode codes such as DF1, DF2, DF4, DF5, and DF6 are used to darken The field mode is specified, and the optical power value is in a specific range of specific numerical values (for example, between 0.0 and 127.0). In some embodiments, index values (eg, 1, 2, 3, 4, 5, etc.) are used for image entry settings fields of various entries, and specific image acquisition settings (eg, specific brightness or dark fields). Specific lighting settings, such as mode, pole, and power settings) correspond to specific index values. In such cases, for example, another lookup table can identify a particular image acquisition (eg, lighting) setting that corresponds to a particular index value.

  In certain embodiments, a sequence of images, each with a specific image acquisition (lighting) setting, may be acquired by an imaging system (eg, machine vision system 30). It can be utilized in a variety of environments, including during initial setup or subsequent training or calibration of the imaging system. For example, when a wafer identification system is set up for operation, many different image acquisition (eg lighting) settings can be used to determine whether a particular image acquisition setting will contribute more reliably to successful decoding of the acquired image. The system can be used to take an image using.

  In certain embodiments, a sequence of image acquisition (eg, lighting modes) settings for successive image decodes (ie, “updated order” of image acquisition settings) may be identified based on the order of entries in the acquisition settings. For example, for an attempt to decode a symbol using a set of 10 images, the imaging manager will respond to the image acquisition (eg lighting mode) setting field in the first 10 entries of the classified acquisition setting table The update order for the image acquisition (illumination mode) set value to be determined can be determined. Ten images for the decoding attempt are then selected (obtained in some cases) and the images in turn represent the corresponding image acquisition (eg, lighting mode) settings from the acquisition settings table. For example, the first image is selected based on the image acquired using the first image acquisition (for example, illumination mode) setting from the sorted order of the acquisition setting table, and the second image is acquired in the acquisition setting table. Are selected based on the second image acquisition (illumination mode) setting acquired from the sorted order. In other embodiments, other image acquisition (eg, illumination mode) orders and methods for a particular image (or image) are also possible.

  In one embodiment, the acquisition settings table classification is performed on an already acquired image based on decoding (or other processing) attempts, where the classification order of the acquisition settings table results is for subsequent image analysis. Used to specify the updated order of acquisition (eg lighting) settings. Referring to FIG. 27, for example, a set of images 200 (eg, images 200a, 200b, 200c, 200d, etc.) may be acquired by the machine vision system 30. Each of the images of the set 200 may be acquired with a particular lighting setting. For example, the imaging manager 210 accesses an acquisition table configured as a lighting settings table 202 and communicates a series of lighting settings from the table 202 to an image acquisition system (eg, various modules 44 of the image acquisition software 44). To work. The image acquisition system then directs successive (or other) acquisition of the image set 200 using the lighting settings specified by the image manager 210.

  In one embodiment, the set of images 200 is acquired for use in a specific read cycle (eg, a specific set of operations for decoding symbols contained at least partially in the image) for the machine vision system 30. Can be done. Thus, in some embodiments, all of the images in the set 200 can be processed together in various ways and combinations. In other embodiments, the image set 200 is acquired for use in different read cycles, so that the images can be processed separately in various ways and combinations.

  In general, the illumination setting table 202 is configured in various ways and includes various types of data in addition to illumination setting data (eg, other image acquisition setting data), and image acquisition software 44 (or another program or module). (Or so accessible). As described, the lighting setting table 202 includes entry sets 202a to 202h, each including at least a lighting setting field 204 and a classification value field 206. In general, the values in the lighting settings field 204 of the various entries in the table 202 identify specific lighting settings (eg, dark / bright fields, power, etc.), and the values in the classification value field 206 are in the table 202 (eg, classification value field 206). (Based on an increase or decrease in the value of). In other embodiments, other configurations of table 202 are possible, including configurations with additional fields.

  In the described system, the imaging manager 210 operates to access the lighting setting table 202 and the lighting setting (or other) from the lighting setting table 202 as specified by the lighting setting field (or other field). The image acquisition settings) to the image acquisition software 44 (or generally another aspect of the machine vision 30). In this manner, the image acquisition software 44 (or another system) can each determine specific lighting settings (or other image acquisition settings) for the various images of the set 200 for acquisition. Similarly, the images acquired by the image acquisition software 44 can be marked in various ways (eg, metadata associated with the images) and the specific lighting settings (or other images used to acquire the images) Acquisition setting). For example, an image acquired with lighting settings from the table 202 may be marked with a value representing an associated entry in the lighting settings field 204 (eg, an index value or otherwise a value representing a related lighting setting). .

  The imaging manager 210 then communicates the lighting settings (or other image acquisition settings) from the table 202 as a required set of lighting settings, or the imaging manager 210 may contact the lighting settings (or other image acquisition settings). Specify acquisition (or other) order for and parameters of their lighting settings (or other image acquisition settings) (eg, power, bright or dark field, etc.) or specific lighting in a specific image Identify associated with settings (or other image acquisition settings). As described, for example, the classification value field 206 represents the described sorted order for the table 202, where the entries proceed in order from the entry 202 a at the top of the table 202 and indicated by the entry 202 h at the bottom of the table 202. It is. Thus, based on the communication of the imaging manager 210, the image acquisition software 44 determines that the first four lighting settings in the table 202 (e.g., represented by values corresponding to the entries 202a through 202d in the lighting settings field 204) It can be determined that each of the four images 200a to 200d of the first set 200 should be used, or the first four lighting settings of the table 202 can be changed to the first four images 200a to 200d. It can be determined to correspond to the lighting settings used for acquisition.

  Each of the images of the set 200 (or a subset thereof) is then in an attempt to decode symbols (eg, classified 2D matrix symbol text characters such as data matrix symbols) contained in one or more images of the set 200. Processed by the image acquisition software 44. Various tools are used to decode the symbols of the set 200 images. In some embodiments, for example, a decoding process can utilize a tool for decoding a two-dimensional matrix symbol (eg, an algorithm for decoding a data matrix symbol). In certain embodiments, the decoding process can utilize an optical character recognition (“OCR”) tool (eg, an OCR algorithm) to decode the text. In some embodiments, multiple tools may be used. For example, attempting to process the images of the set 200 and decoding the symbols of the images can include processing the images with both a two-dimensional matrix symbol decoding tool and an OCR tool. Various images (e.g., images 200a-200d) may be processed sequentially or in parallel with data matrix decoding tool 212 and OCR decoding tool 214. In some embodiments, the process for decoding can be performed in parallel with active image acquisition.

  In certain embodiments, processing the images of the set 200 may include processing each subset of the images of the set 200 with a specific tool (eg, a specific algorithm). In one embodiment, the read cycle for a particular tool may include multiple image separation processes, and centralized processing of various composites of images (or information contained in or derived from them). Image processing may be included. For example, as described in detail above, data from multiple images can be stitched together for centralized processing, and multiple images can contribute to the decoding of a particular symbol.

  Various descriptions herein address examples of stitching data from multiple images. In some embodiments, however, other analyzes of multiple images are also used. For example, a symbol may be decoded based on an image being analyzed and stitched together. Symbols that make use of stitches between images rather than (or in addition to) a set of image captures and stitches of data from images, unless the description prioritizing specific image acquisition settings herein is limited or otherwise specified It will be appreciated that it can be applied in various operations, including decoding operations.

  Following the execution of a specific read cycle (e.g., a process that attempts to decode a symbol of one or more images of the set 200), the score is the specific image (and thus the image used to capture those images). Can be assigned based on the processing of the image in the read cycle. In general, such a score can be assigned from a predetermined total score, with the total score assigned in a particular set of images. For example, where 1000 total points are available for allocation, all 1000 points are allocated into the set of images, and different images in the set receive different allocations based on their contribution to the decoding operation of a particular image.

  In one embodiment, a score is assigned to the image used by a particular tool that successfully decodes the symbol during the read cycle. For example, if the OCR 214 successfully decodes a symbol during a read cycle that includes analysis of each of the images (or sub-sets thereof), the preferred total score is that of the set 200 images (or sub-sets thereof). All the preferred total scores are assigned to all sets 200 of images (or sub-sets thereof). Scores are assigned by the imaging manager 210, in some embodiments by other software, modules, hardware, and the like.

  If only one image is used for a successful read cycle or decoding attempt (eg when a “singleton” results in a successful decoding), the total score is preferably assigned to that one image. . As multiple images are utilized for a successful read cycle, the score is assigned among the multiple images in various ways, including being relatively proportional to the contribution of successful decoding of a particular image. In one embodiment, for example, one decoding attempt for image set 200 includes stitching data from images 200a, 200b, and 200c together in a centralized process to decode common symbols. The image 200a is used as the main image for the decoding process, and the images 200b and 200c are used as the sub-image for the decoding process. Half of the available scores (for example, 500 points) are assigned to the image 200a, and the remaining scores are the images. Divided between 200b and 200c (eg, 250 points are assigned to each). In general, a score may be assigned among multiple images based on various factors, such as whether the degree of symbol content of each image has been reliably determined by read cycle analysis.

  In some embodiments, an image processed as part of a read cycle may not contribute to successful symbol decoding. For example, referring also to FIG. 27, another decoding attempt for image set 200 includes stitching data in various composites of images 200a, 200b, 200c, and 200d together for decoding processing. obtain. If image 200a is used as the main image for decoding, images 200b and 200c are used as sub-images for decoding, and image 200d does not contribute to the success of decoding, half of the available scores ( For example, 500 scores are assigned to image 200a, the remaining scores are assigned between images 200b and 200c (eg, 250 points are assigned to each), and a minimum impact (eg, zero) is assigned to image 200d. Similarly, if none of the images 200a to 200d contributes to the decoding process (eg, the read cycle does not successfully decode the symbol in the data stitch combination of images 200a to 200d), the minimum impact (eg, zero) is the images 200a to 200d. Assigned to each.

  Where multiple tools contribute to successful decoding of symbols during a successful read cycle, a given total score may be reduced in proportion to the number of tools. In some embodiments, for example, two tools can be used for a special read cycle. Thus, the score available for assignment of images processed by either tool is reduced in half, and the tool score is distributed to both tools. For example, the OCR tool 214 can successfully decode a symbol by stitching data from images 100a to 200c in a specific read cycle, and the data matrix tool 214 can stitch data from images 200c to 200d. When a symbol is successfully decoded, the total score available for assignment to each tool is reduced by half from the total score if only one of the tools successfully decodes the symbol. Thus, for example, half of the total score (eg, 500 scores) can be used to assign images 200a to 200c to the image based on the decoding results in OCR tool 214, and half of the total scores (eg, 500 scores). May be used for assignment to the images 200c and 200d based on the decoding result in the data matrix tool 212.

  In some embodiments where multiple tools are used, no score is assigned to a particular image unless a successful decoding attempt is made to at least two tools. For example, where the system 30 is configured to perform OCR and data matrix decoding, a score may sometimes be assigned to an image only if both tools successfully decode the symbol. In such a configuration, no score is assigned to any processed image if, for example, the OCR tool successfully decodes the symbol during the read cycle and the data matrix tool does not. In other embodiments using multiple tools, the score is assigned to a tool that successfully decodes the symbol and not to a tool that does not decode the symbol successfully.

  Once a score has been assigned based on a decoding attempt, the table 202 is then updated (eg, by the image manager 210). In some embodiments, the sorted rules in table 202 are used to determine past decoding results in order to prioritize the use of lighting settings (or other image acquisition settings) that previously contributed to successful decoding attempts (eg, successful read cycles). Can be updated based on This may be achieved in various ways.

  In some embodiments, for example, an exponential moving average (EPA) may be calculated for the scores assigned to the corresponding images following various entries in the classification value field 206 and a successful lead cycle (eg, , Imaging manager 210). For example, the scores assigned to the various images are appropriately adjusted to correspond to the value of the classification value field 206 of the entry in the table 202, and the associated value is considered as new data into the classification value field 206 and is EMA. May be easily calculated. More specifically, the score assigned to a particular image may be treated as new data into the classification value field 206 of the table 202 entry corresponding to the lighting setting used to capture that image. The EMA may then be calculated for the associated score and the corresponding entry in the classification value score 206 of the table 202, and the classification value field 206 may be updated with the resulting EMA value.

Still referring to FIG. 27, when the lighting setting indicated by the lighting setting field 204 for the entry 202a of the table 202 is used to obtain the image 200a, the score assigned to the image 202a after processing of the image 202a in a decoding attempt. May be associated with the classification value field 206 for entry 202a. When the EMA (or other average) is calculated, the score of the image 200a is thus treated as new data to the classification value field 206 for the entry 202a, and the EMA to the field 206 is calculated in a conventional manner. obtain. For example, the EMA for a particular classification value field is calculated as:
EMA = (P · α) + (SVF _C · (1−α)) (1)
Where SVF _C represents the current value of the associated entry in the classification value field 206, P represents the corresponding associated score, α represents the smoothing factor, which is, for example, a specific number of images for the associated cycle. Calculated based on.

  A calculation similar to EMA (or other average) may also be performed on other entries in the classification value field 206 (eg, each entry or each entry whose value is assigned to the corresponding image). Thus, for example, the lighting settings (or other image acquisition settings) used to acquire images that contribute to a successful lead cycle comprise an increased value in the associated entry of the classification value field 206. Similarly, the lighting settings (or other image acquisition settings) used to acquire images that do not contribute to a successful lead cycle may comprise a reduced value in the relevant entry in the classification value field 206.

  Once the EMA (or other average) is calculated for the appropriate entry in the classification value field 206, using the corresponding score as new data for the EMA, the entry in the classification value field 206 is updated with the new EMA value. Can be done. In certain embodiments, the table 202 may then be classified based at least in part on the EMA value (ie, based on the current value of the classification value field 206). The update of the classification value field 206 may be achieved by the imaging manager 210 or by other software, hardware, or the like.

  In one embodiment, the table 202 is classified based on the classification value field 206 (ie, updated to a new classification order), and the lighting settings (or other acquisition settings) that contribute to successful decoding are in the sorted order. The lighting settings (or other image acquisition settings) that do not contribute to successful decoding may be moved downward in the order in which they are classified. Thus, for example, when an image is selected (acquired) for a subsequent decoding attempt, an image acquired with a particular image acquisition (eg, lighting) that previously contributed to a successful decoding attempt may be successful. It may be prioritized over images acquired with fewer image acquisition (eg lighting) settings.

  Referring also to FIG. 28, for example, when the images 200c and 200d (see FIG. 27) contribute to the successful decoding and the images 200a and 200b (see FIG. 27) do not contribute to the successful decoding, the images 200c and 200d become the images 200a and 200a. You can be assigned more points than 200b. Thus, after the EMA calculation and the update of the classification value field 206, the reclassification of the table 202 to the new classified order sets the lighting settings for the images 200c and 200d based on the classification value of the field 206 (ie, , Reflected in the lighting settings field 204 for the entries 202c and 202d), prior to the lighting settings of the images 200a and 200b (ie reflected in the lighting settings field 204 for the entries 202a and 202b). Thus, the lighting settings may take precedence over the lighting settings for images 200c and 200d in subsequent (same or different symbol) image decoding attempts or other operations. However, this particular classification order (ie, the lighting settings for images 200c and 200d prior to the lighting settings for images 200a and 200b) may not necessarily be obtained depending on the results of various EMA calculations and various other factors. .

  In other embodiments, other classification algorithms or prescriptions may be used. It is further understood that the classification of the table 202 can refer to various entries in the classification value field 206, rather than physically rewriting the table entries to different memory locations, or virtually classifying and rewriting the table entries. The virtual classification of the table 202 is included rather than the composite.

  Once the classification order of the table 202 is updated (eg, once the table 202 is classified based on the EMA calculation described above), the lighting settings for the image (or other image acquisition settings) are updated in the table 202. Used to prioritize based on the sort order, a new decoding attempt is made. As described above, for example, the illumination settings from the illumination settings table 202 are imaged to be communicated to the image acquisition software 44 for another set of processing of the images 220, as specified by the illumination settings field 204. The manager 210 accesses the lighting setting table 202. However, contrary to the lighting setting classification order for the image set 200 (see FIG. 27), the updated classification order of the table 202 is now used first by the lighting setting (or other image acquisition settings) for the table entry 202d. Followed by lighting settings (or other image acquisition settings) for table entry 202c, followed by lighting settings (or other image acquisition settings) for table entries 202a and 202b. Thus, the image acquisition software 44 first tries to decode the image 220a of the set 220 having the lighting setting corresponding to the table 202d, and tries to decode the image 220b of the set 220 having the lighting setting corresponding to the table entry 202c. The same goes on. Once these new decoding attempts are complete, a score is assigned to the set 220 images (eg, as described above) and a new EMA is calculated for the classification value field 206, thus determining a new classification order for the table 202. The

  In general, table entries in table 202 having preferred (eg, relatively high) EMA values take precedence over entries for table 202 having relatively unfavorable (eg, low) EMA values, as reflected in classification value field 206. (I.e., preceded by sort order or acquisition order). In certain embodiments, however, other rules may be used in addition (or alternatively).

  In some embodiments, if the user provides acquisition settings, such as lighting settings or related values, the image acquisition (eg, lighting) settings (or related values) provided to the user are latent regardless of the sort order of the table 202. Thus, priority may be given to other image acquisition (eg, lighting) settings of table 202. For example, if the user specifies a particular bright or dark field setting, power setting, or other lighting setting parameter, the decoding attempt is first made before the other image with other lighting settings from the other image table 202 before the user. The image is handled with the lighting settings (or settings) specified by, and when other lighting settings indicate a more preferred (eg, greater) value for the classification value field 206 than the lighting settings specified for the user.

  In some embodiments, image acquisition settings (eg, lighting settings) for images that contributed to successful decoding results in previous (eg, immediately preceding) read cycles are prioritized in subsequent decoding attempts (for the same or different symbols). obtain. For example, referring also to FIG. 27, image 200b contributes to a successful decoding attempt, and the lighting settings on image 200b are used for subsequent decoding attempts, and lighting that does not contribute to a successful decoding (at least the most recent decoding attempt). Before selecting another image in the set 220 in the settings, an image from the set 200 is selected as an image 200b with the same illumination settings. This prioritization can be performed even if the latter lighting setting corresponds to a preferred (eg, larger) value for the classification value field 206 than the lighting setting for the image 200b. Similarly, if multiple images of set 200 (eg, images 200a, 200c, and 200d) contribute to a successful decoding attempt, before selecting other images of set 220 with lighting settings that do not contribute to successful decoding. The images are selected as images 200a, 200c, and 200d from a set 200 with the same illumination settings that are used in a decoding attempt followed by illumination settings for images 200a, 200b, and 200d. This prioritization can be performed in the same way, even if the latter illumination setting corresponds to a more preferred (eg, larger) value for the classification value field 206 than the illumination setting for the images 200a, 200c, and 200d.

  In certain embodiments, further classification may be performed within (or otherwise in addition) the above priorities. For example, lighting settings for a set of successful decoded images (eg, images 200a, 200c, and 200d) are prioritized relative to each other based on the EMA values corresponding to those images, and the lighting settings for the entry set of images (eg, image 200a). , 200c, and 200d) are prioritized over lighting settings for images that did not contribute to successful decoding.

  In certain embodiments, image acquisition (eg, lighting) settings for a particular tool (eg, a two-dimensional matrix tool) may be overridden over image acquisition (eg, lighting) settings for a different tool (eg, OCR tool). The preferred image acquisition setting for successful decoding attempts based on the use of a particular tool may be used in various ways. For example, one purpose of image recognition analysis is to achieve relatively high read rates and successful decoded symbol results. Thus, for example, the image manager 210 (or other software, module, hardware, etc.) identifies lighting settings that require a relatively small number of processes for each product image, for example, for each imaged product (eg, semiconductor It may operate to reduce the average lead time (per wafer). Data Matrix and other similar tools are often required to process a relatively large number of images to achieve successful decoding, so before doing so for other tools It is useful to prioritize image acquisition (eg lighting) settings for these tools. Thus, for example, a tool timeout without a decodable image may be avoided.

  Thus, in one embodiment, referring again to FIG. 27, the lighting setting for successful decoding using the data matrix tool 212 may be prioritized over the lighting setting for successful decoding using the OCR tool 214. For example, if images 200a, 200b, and 200c contribute to a successful decoding attempt with data matrix tool 212 and image 200d contributes to a successful decoding attempt with OCR tool 214, lighting settings 200a, 200b, and 200d are paired for decoding. From 200, the next selection of images may be moved higher (ie, may be prioritized) in the classification order of table 202, even if the lighting setting for image 200d is images 200a, 200c, and 200d. This is true even if it corresponds to a preferred (eg, larger) value for the classification value field 206 than the lighting setting for.

  The various tools, algorithms, etc. described above may be implemented in various ways using various computing devices and system types. In certain embodiments, for example, the imaging manager 210 may be integrated into one software application with the image acquisition software 44 and / or the symbol location software 74. In other embodiments, the imaging manager 210 may be a software application separate from the image acquisition software 44 and / or the symbol location software 74. In different embodiments, the imaging manager 210 may reside and execute on the computer 50 or another device (eg, the imaging device 42).

  29-35, exemplary data for scoring, table classification, and image decoding operations are shown. Some tables of FIGS. 29-35 and various calculations or intermediate results of processing may be reflected, or may be represented only to clarify the nature of other tables and values. Thus, certain tables and values of FIGS. 29-35 need not necessarily be stored (or stored for a substantial amount of time) and may simply be utilized by system 30. Further, as described above, the prioritization and classification described herein may be used in various image acquisition settings. Thus, it will be appreciated that even if the illumination setting is shown as a specific example of a statement, similar principles may be applied to other types of image acquisition settings.

  With particular reference to FIG. 29, exemplary indexes are provided for various lighting settings. As illustrated, 18 different lighting settings 300 are specified by a bright field (“BF”), a dark field (“DF”), and a power code (“P”), and have many associated values, an integer This corresponds to 18 indexes 302. As described above, the illumination settings table (eg, table 202) containing the illumination settings of FIG. 29 includes the code of the illumination settings 300 itself (eg, in the illumination field 204), or the illumination settings as the value of the index 302. Other values representing 300 may be included.

  In an exemplary operation, an image may be acquired using various lighting settings 300. An image from which a set of 9 read cycles has been acquired (eg, after image acquisition or in parallel), each read cycle attempts to decode the acquired image symbols using both a 2D matrix decoding tool and an OCR tool May be attempted in a state of containing. An exemplary set of results for operations etc. is represented in FIG. As shown by the read cycle column 304 in the table of FIG. 30, each of the read cycles is represented by a specific two-dimensional matrix decoding tool (in the tool column 306 of FIG. 2), and attempts to decode symbols from many images using specific OCR tools. In some embodiments, the 2-D and OCR tools may operate sequentially. In some embodiments, the 2-D and OCR tools may operate in parallel.

  Each image contributing to the successful decoding result in a particular read cycle is represented by an index value from the lighting setting index 302 (see FIG. 29) corresponding to the lighting setting used to obtain the related image in the index field 308 of FIG. expressed. For example, in a first lead cycle (ie “Lead Cycle 1”), a 2-D tool is used to obtain two images acquired with illumination settings BF3P02 and DF1P20 (corresponding to values 11 and 4 of index 302). Use to successfully decode the symbol. Similarly, in the first read cycle, an OCR tool is used to symbolize using three images acquired with illumination settings BF3P01, DF3P20, and BF2P02 (corresponding to values 302, 3, and 9 in index 302). Successfully decoded. For other read cycles, except for read cycles 6 and 7, respectively, the other values from index 302 are similarly other (or other) images used to successfully decode symbols in a particular read cycle. The same) represents the lighting setting. Symbols fail to decode any of the images used by both tools for read cycle 6 and no value is provided from the lighting settings index 302. For the lead cycle 7, the OCR tool successfully decodes the symbol on the single-tag image of the lighting setting DF3P20 (corresponding to the value 5 of the index 302), and the 2-D tool succeeds in the symbol on any image. And did not decode.

  In some embodiments, data from images processed for a particular read cycle can be stitched together as described above, and a decoding attempt can process data from multiple images simultaneously. For example, for the first lead cycle, the 2-D tool was derived from the two images represented (ie, the images taken with the illumination settings for index values 11 and 4 in the illumination setting index 302 (see FIG. 29)). The symbol may be successfully decoded based on a cumulative (or at least partially composite) matrix, and the OCR tool may display the three images represented (ie, index values 3, 6, and 9 of index 302). The symbol may be successfully decoded based on the image taken with the illumination setting corresponding to. As described above, although the described read cycle 7 includes successful decoding on a single card, a similar use of stitches may be utilized for one or more other read cycles.

  The image represented by the value of the index 302 in the table of FIG. 30 is an image captured during a specific acquisition operation, or a decoding attempt for a specific read cycle including a single tag decoding and a decoding attempt using a stitch operation. It will be understood that not all rendered images need necessarily be represented. As will be described, the table of FIG. 30 only represents those images that have substantially contributed to successful decoding attempts for a particular read cycle. As described above, in various implementations, it is utilized to assign scores only to such contributing images.

  After a successful decoding attempt is completed for a particular read cycle, a score may be assigned to each image that contributed to the successful decoding (eg, by imaging manager 210). As mentioned above, the magnitude of these scores can correspond to relative contributions to different successful decoding of different images. In FIG. 30, the assigned score is represented by the score column 316. In the first read cycle of FIG. 30, for example, an image photographed with the illumination setting BF3P02 (corresponding to the value 11 of the index 302) was photographed with the illumination setting DF1P20 (corresponding to the value 4 of the index 302). Contributed more significantly to 2-D decoding than images. Therefore, a BF3P02 image (index value 11) was assigned 750 points (out of 1000) and DF1P20 (index value 4) was assigned 250 points.

  Conversely, each image listed for successful OCR decoding in the first read cycle contributes roughly equivalent to successful decoding, each image is assigned 333 points, and one image is rounded to 334 points. Ensure that a full 1000 total score is assigned for the tool and lead cycle. In other lead cycles, as seen in FIG. 30, other score numbers are assigned to successful decoding based on different (or similar) different images (and correspondingly different lighting settings). As can be seen, a zero score is assigned to the read cycle 6 because no successful decoding has been performed. Similarly, no zero score is assigned to the 2-D tool in read cycle 7 because no successful decoding is achieved with that tool. In some embodiments, a non-zero minimum impact score (eg, a negative maximum value or a minimum positive number on the scoring scale) is assigned instead.

  As described above, during operations that include those that require successful decoding by both OCR and 2-D tools, a minimum impact (eg, zero) may be assigned to a read cycle in which only one of the tools succeeds. In such a case, for example, in the seventh read cycle of FIG. 30, the 2-D tool does not successfully decode the symbol, and therefore a score may not be assigned to the index 5.

  As described in FIG. 30, a total score number equal to 1000 is assigned to each tool for each successful lead cycle. In other examples, other numbers of total scores can be used. For example, one embodiment assigns an equivalent total number to 1, the various images in the set used for a particular read cycle are assigned a fraction of 1, and the entire set of images is assigned a total score of 1.

  Also, as described above, various images to which scores are assigned can be used to update the classification value field of the acquisition (lighting) setting table. In some embodiments, however, the scores assigned to the various images may be required to be adjusted appropriately initially. As shown in FIG. 31, for example, for each read cycle, the total score assigned for decoding operations in both of the two tools (see FIG. 30) is adjusted so that the total score for a given read cycle is the sum 1000. For example, for the first read cycle, as can be seen from FIG. 30, the total score assigned to both 2-D and OCR tools is 2000 in total. Therefore, these assigned scores are halved (and appropriately rounded) with respect to the first read cycle column (column 310) in FIG. Similarly, the number of points assigned to other read cycles is adjusted appropriately to ensure a uniform total score for each read cycle. Note that the number of points assigned to the seventh read cycle (ie, reflected in column 314) is not adjusted because only the OCR tool successfully decodes the symbol during that read cycle. Because.

  After the execution of a specific lead cycle (or its related part), each score number entry (e.g. along field 310) with a score that the EMA is assigned to a specific image (and corresponding lighting settings) in the specific read cycle. Each score entry) and an EMA value is calculated for the previous read cycle (or another initial score as described below). The result of the EMA calculation is then used to enter the corresponding classification value field in the acquisition (lighting) settings table (eg, the classification value field 206 in the table 202 of FIGS. 27 and 28). Thus, for example, the value of the classification setting field for a particular image (eg, lighting) acquisition setting (eg, a particular entry in index 302 or lighting setting field 204) is updated to reflect the history decoding results. Therefore, when the acquisition (for example, lighting) setting table is classified based on the classification value field, the result is prioritized over the image processing following the image acquisition (for example, lighting) setting in which the classification order of the table can be used in the history.

  Referring again to FIG. 32, the subsequent columns of the displayed table represent the results of subsequent EMA calculations based on the read cycle results and score assignments shown in FIGS. As noted above, in some embodiments, the total number of scores (eg, 1000 in the illustrated example) can be used to provide an initial basis for calculating EMA (or other average). The initial assignment may be relatively uniform among all (or a subset) of different lighting settings. As seen in column 312 of FIG. 32, 1000 total scores for a given read cycle are initially distributed relatively evenly over the entry index 302. In other embodiments, other initial assignments of scores may be used. For example, a custom initial score assignment may be provided for the user to replace with a relatively uniform score assignment in column 312. This is useful, for example, to initially prioritize certain lighting settings over others.

  Starting from an initial score assignment (eg, shown in column 312) or from another set of score numbers (eg, previous EMA calculations), the value of the classification setpoint field can then be updated based on the previous read cycle results. . As can be seen from column 318, for example, after the first read cycle, the initial (ie, as indicated by column 312) relatively uniform classification setpoint field value is updated to succeed during the first read cycle. Reflecting the decoding attempt, the image has lighting settings corresponding to index values 3, 4, 6, 9, and 11 (see FIG. 30). Similarly, as can be seen from column 320, after the second read cycle, the classification value field after the first read cycle (see column 318) is updated and used for successful decoding during the second read cycle. Reflects the lighting setting of the displayed image.

  In the example shown in FIG. 32, the EMA smoothing constant, 0.0392157, is used to be calculated by assuming a period value of 50 entries. In various embodiments, it is useful to assume such a period value, and a semiconductor wafer analysis cycle involves the acquisition of images for analysis of a set of 50 wafers. In other embodiments, other period values (and other smoothing constants) may be used as appropriate.

  After a particular lead cycle, the acquisition settings table (eg, lighting settings table 202) may be classified based on the EMA values updated from the lead cycle (and previous processing). For example, the various read cycles of FIG. 32 (eg, columns 318 and 320, etc.) can represent stored entries for a lighting settings table (eg, lighting settings table 202) following each respective read cycle.

  The acquisition setting table (for example, the lighting setting table 202) is classified based on the EMA (or other similar value) shown in FIG. May be prioritized. As shown in FIG. 33, for example, the illumination setting index value from the index 302 (see, for example, FIG. 32) is classified based on the number of assigned, adjusted, and averaged points reflected in the read cycle column of FIG. ing. For example, as shown in FIG. 32, after the first read cycle, the lighting setting index 11 of index 302 corresponds to the highest EMA value in column 318, lighting setting indexes 3, 6, and 9, and lighting setting. Index 4 continues. Therefore, in the classification column 318a of FIG. 33, these illumination setting indexes move to the top of the classification order. As a result, this sort order is accessed and stored (or, for example) in an acquisition (eg, illumination) settings table that determines the appropriate image acquisition (illumination) settings for subsequent decoding attempts (for the same or different symbols) Otherwise, the image acquisition (for example, illumination) setting corresponding to the illumination setting indexes 11, 3, 6, 9, and 4 is given priority.

  As shown, various columns (eg, columns 318a and 320a) are categorized, with higher score lighting settings taking precedence (ie, increased in the sort order) and lower score lighting settings being given lower priority. Not (ie descended in the sort order). In other embodiments, other classification algorithms are included and used to apply to other types of image acquisition settings.

  In some embodiments, the sorting order of the lighting settings index 302 (eg, as reflected in columns 318a and 320a) occupies the order in which the images are subsequently analyzed to decode symbols. As can be seen in FIG. 33, the lighting settings that result in successful decoding for a certain read cycle may be highly prioritized only by value-based classification, as indicated by the shaded cells. For example, lighting settings 8, 13, and 14 contributed to successful decoding in read cycle 8, but these lighting settings are relatively low in classification order in classification column 322. Therefore, it is sometimes useful to adjust the classification order (eg, based on updated EMA values), and the lighting settings that contribute to successful decoding (eg, in the most recent preceding decoding attempt) Move to the beginning of the sort order to the block.

  An example of such a block of entries for each read cycle is represented in table 324 of FIG. For example, column 326a of table 324, block 328 for lead cycle 8 includes images with lighting settings 8, 13, and 14, because these images were successful in real lead cycle 8 (see column 322 in FIG. 33). This is because it contributed to the decoding attempt. The remaining lighting setting index values (ie those not included in the block of table 324) are represented in table 330 of FIG. 34 sorted according to the EMA values represented in FIG. Note that the table 330 does not include the lighting setting index values included in the table 324 for a given read cycle. For example, column 326b of table 330 represents the indexed tuple for read cycle 8, but does not include index values 8, 13, and 14 because these values are included in block 328 of table 324. It is.

  In order to guide the selection of an image for subsequent processing, the various blocks (eg, block 328) of table 324 are held in the resulting classification order prior to the index value in the corresponding column of table 330, and 330 may be combined. Furthermore, the classification index value is transitioned by one read cycle (or plural), and the classification index value is seen as a representative value for the subsequent read cycle, as a display value for the read cycle calculated therefrom. Thus, for example, a classification index value derived from an earlier read cycle may be used to prioritize certain image acquisition settings for later read cycles.

  Referring to FIG. 35, a lighting setting index table 334 represents a composite of one example of tables 324 and 330. As shown, for example, the column 326c for the ninth read cycle (ie, the read cycle following the eighth read cycle) includes a block 328 of illumination setting index values from the table 324 of FIG. 34, and the table 330 of FIG. Followed by the order index value in column 326b. Furthermore, as described above, the order of the illumination setting index values shown in FIG. 35 (and therefore prioritization of the illumination settings during the lead cycle 9) is based on the classification order of the corresponding EMA values (see FIG. 32). Reflects the prioritization of lighting settings that will result in successful decoding for the lead cycle (see FIG. 34, block 328). Thus, during the lead cycle 9, images having illumination settings corresponding to indexes 8, 13, and 14 are first selected for decoding, followed by images having illumination settings in the remaining classification order in column 326c.

  If no image contributes to a successful decoding attempt for a particular read cycle, it may not be updated based on the lighting settings of the image that contributed substantially to the successful decoding. For example, referring to FIGS. 30 and 33-35, it can be seen that there is no image successfully decoded during read cycle 6 (as reflected in read cycle 7 in FIG. 35). Therefore, there is no lighting setting block provided in the lighting setting classification order for the read cycle in table 324 of FIG. 34 or in table 334 (ie, as reflected in column 336).

  With continued reference to read cycle 6, it can be seen in FIG. 32 that the EMA value between cycle 5 (as reflected in column 338) and cycle 6 (ie, as reflected in column 340) is not changed. . Since no successful decoding is performed from the read cycle 6 (see FIG. 30), no score is assigned to the image based on the read cycle 6, and the EMA value from the read cycle 5 (column 338) is read in the read cycle 6 (column 340). Simply carried over. Referring to FIG. 35, however, it can be seen that the sorting order in column 336 is determined by the result of read cycle 6, but changes from the sorting order in column 342. This is because the classification order in the column 342 is updated to give priority to the block 344 of the index corresponding to the image contributing to the successful decoding attempt in the read cycle 5 (see FIGS. 30 and 34).

  In general, with reference to FIGS. 32-35, it can be seen that the user specific lighting setting (“user entry”) is not provided for the example shown. However, placeholders are maintained for user entries in the classification table of FIG. 33 (see user entry column 346) and the updated classification table 334 of FIG. 35 (user entry column 348). If the user identifies a particular lighting setting (or aspect thereof), the user entry may be prioritized in the following read cycle, as is the case for the above lighting setting that contributed to a previous successful decoding attempt.

  As described above, the tables shown in FIGS. 29-35 do not necessarily have to be stored entirely by the system 30, or are stored in a specific table or other storage in the system 30 (lighting setting table 202). It is understood that it need not be included. For example, in one embodiment, system 30 stores a table similar to table 202 (see FIG. 27), whose classification order is updated after each successful read cycle based on the analysis shown in FIGS. May be.

  In some embodiments, it is useful to limit the number of images that may be stitched together for decoding purposes. For example, for a decoding operation using a two-dimensional matrix symbol decoding tool, data stitching may worsen rather than improve the decoding result if the number of images is excessive. Thus, in certain embodiments, the maximum number of images may be specified in the score assignment for a particular read cycle. If the initial set does not result in a successful decode before the initial set of images reaches the maximum number of images, a new decoding attempt (within the same read cycle) may begin with a different set of images. In certain embodiments, for example, if a maximum number of 8 images is specified and the 8 sets do not result in successful decoding with the associated tool, a new set of 8 images may be used for a new decoding attempt. .

  Even if a particular set of images does not result in successful decoding, however, some images in the set may still be useful for subsequent decoding attempts. Thus, in one embodiment, when the initial set of images fails the result of a successful decoding attempt and the new image is incorporated into a new decoding attempt, the highest score (s) from the initial image is (Eg, carried over as a “seed” image). In such a case, the score assignment to a new (or other) image may be relaxed based on the nature of successful decoding of the image nominated from the initial image set. For example, where the seed image from the initial set of images contributes to the new set of images and a successful decoding attempt, the seed image can be assigned an enhanced score (eg, 600 of 1000 scores) and the remaining score (eg, 1000). A score of 400) is assigned among the remaining images of the new set of images.

  Referring to FIG. 36, for example, the stitch analysis of the initial set 350 of images does not result in a successful decoding attempt, and the image 352 of the set 350 does not contain the decoding attempt on the set 350 (eg, the symbol (or symbol For the representative of the part), the score may be relatively high compared to the other images 352. The maximum number of images (eg, 8 images) is already included in set 350 and no additional images are added to set 350 anymore. Rather, a new set of images 354 may be acquired. However, for decoding attempts on set 350, image 352 may score relatively high and image 352 may carry over to a new set of images 354 as the seed image for that set.

  In some embodiments, other operations may be performed to use the information from the images of the initial set 350 of images along with the information from the images acquired for the new set 354 of images. For example, after the highest (or otherwise relatively high) score image of the initial set 350 has been identified, the image itself can be specified for a new set 354 or another image acquired with the same acquisition settings. , The highest score image is nominated for the new set 354. The nominated image (eg, image 352 or other similarly acquired image) substantially serves as part of the new set 354, or the information from the nominated image is with information from other images in the new set 354 Used, image 352 does not serve as part of a new set 354. As an example of the latter, where image 352 is nominated for a new set 354, image 352 may be processed for decoding without necessarily functioning entirely as part of the new set 354. The results of processing of image 352 are then used along with the results of processing of other images in new set 354 to attempt to decode related symbols.

  In one embodiment, a specific strategy is used to assign scores to images that are analyzed using an OCR tool. For example, if an image allows one or more characters at a particular location to be successfully decoded, the image can be assigned a percentage of available scores for each character. For example, where twelve character strings of text are decoded, an image that allows for successful decoding of one or more characters in the string can be assigned about 8.3% of the available score for each character.

As another example, if one image allows for successful decoding of the entire string, the one image may be assigned a total score of available scores. However, if one image allows for successful decoding of a column and one or more other images allow for successful decoding of a particular character in the column, a score is assigned within each of the contributing images. May be. For example, the image that provides for the successful decoding of the entire column can be assigned a fixed percentage of available scores (generally X percent), and other images (or multiple images) can be calculated and assigned to ,
Score = 1 / (C _T ) × (100−X) / 100 × C _D (2)
Here, C _T represents the total number of characters in the string indicates the number of characters to be decoded on the basis of the image C _D is assigned a score.

  In some embodiments, the number of available acquisition (eg, lighting) settings may change the progress of the action or the continuation of the action. For example, for system 30, a user can enable or disable auxiliary lighting settings, for example, the number of available lighting settings can vary depending on user input. When a table of EMA values for lighting (or other image acquisition) is constructed (eg, as described above) and additional lighting (or other image acquisition) settings are enabled, the sum of EMA values is It is useful to ensure that it remains equivalent to the maximum number of scores (eg 1000 scores in the above example), including those for additional lighting (or other image acquisition) settings. Thus, when additional lighting (or other image acquisition) settings are available, the additional lighting (or other image acquisition) settings may initially be assigned zero at the EMA value.

  Similarly, a table of EMA values for various image acquisition (eg, lighting) settings is constructed (as described above), and some image acquisition (eg, lighting) settings are disabled for subsequent decoding operations, EMA values for new unavailable image acquisition (eg, lighting) settings can be distributed among the remaining acquisition (lighting) settings. Thus, for example, the sum of EMA values for available image acquisition (illumination) settings remains equivalent to the maximum number of scores. Further, if the EMA values for the newly unavailable image acquisition (lighting) settings are evenly distributed among the remaining image acquisition (eg lighting) settings, the current classification of the remaining image acquisition (eg lighting) settings. Order is maintained (eg, based on EMA values).

  In one embodiment, each decode tool provides a Result Contributors vector via a getter function in both success and failure cases, and includes the following instructions:

  Thus, the set returned by the tool may include an image index (eg, similar to lighting (or other image acquisition) settings, index 302 of FIG. 29), and the number of scores that the tool has been assigned to the image.

  In some embodiments, the image index returned by the tool may indicate the order in which a particular image was passed to the tool for analysis (or a corresponding arrangement of related image acquisitions in the image acquisition settings table). For example, an image showing the lighting settings corresponding to the seventh entry in the lighting settings table may be passed to the tool, which will contribute to the decoding, and then the image index value may be returned with the value 7.

  In one embodiment, if the tool fails to analyze an image set, the best and tooled image for display is assigned a maximum score (eg, 1000 in score) and the remaining images in the set have a value of zero. receive. In other embodiments, no score is assigned to any image for unsuccessful decoding attempts.

  Under the example instructions above, if the tool successfully decodes a symbol in a particular read cycle, the result contribution vector may only work on images that contributed to successful decoding. However, other images may be addressed in case of failure.

  In some embodiments, a classification similar to that described above for acquisition settings may be used for post acquisition processing. For example, different post acquisition processes (eg, different image filter settings applications or areas of interest adjustment) may be applied to different acquired images. The post-acquisition setting order is determined based on the contribution of the various post-processed images to the decoding attempt on the image, where the post-acquisition setting for the image having a strong contribution to the decoding attempt is for the image having a weak contribution to the decoding attempt. A higher impact (eg, larger) score is assigned than the post acquisition process setting. In certain embodiments, this assignment of scores may be processed in a similar manner to the score assignment described above for acquisition order.

  Once determined, the post acquisition set order is then used to guide subsequent decoding attempts. For example, the post acquisition setting order (eg, as updated for subsequent read cycles) determines the order of post acquisition processing for an image that will receive a new decoding attempt, or is already at a specific post acquisition processing setting. Used to prioritize decoding attempts on certain processed images.

  Referring again to FIGS. 34-35, for example, different types of post processing are applied to images with lighting settings 8, 13, and 14 during the lead cycle 8, and the post processing is to attempt to decode those images. May contribute at least in part to the relative contribution of and the corresponding increase score assignment for those images. Based on the score assignment and the specific type of post processing used for a particular image, the post acquisition processing set order may be determined for the post processing or the specific image. During the read cycle 9, a specific post-acquisition process is then applied to the acquired image or a specific post-processed image prioritized for decoding, which is based on the post-acquisition process setting order. For example, the post acquisition process setting for a new image can be determined based on the setting priority in the post acquisition process setting order.

  In some embodiments, the determined image acquisition settings are reported as an alternative (or in addition) to inform the acquisition order of the processing order (eg, the order in which the images are processed and attempt to decode the symbols). For example, referring to FIG. 35 further, the acquisition settings for the read cycle 10 prioritize the acquisition settings indicated by indexes 5, 8, 7, and 3 in order. In some embodiments, processing to an attempt to decode an image from read cycle 10 may be performed even if the images for read cycle 10 were acquired in a different order of acquisition settings (eg, 8, 7, 5, then 3). It is performed based on the order (ie 5, 8, 7, then 3). Thus, at this point, the acquisition settings shown in FIG. 35 are seen separately (or in addition) as the processing order for the acquired images, and the images acquired with the specific acquisition settings are processed and represented there. The order in which to attempt to decode the symbols may be specified.

  Although the present technology has been described with reference to preferred embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the technology. For example, the present technology is not limited to the two-dimensional data matrix symbol embodiment and may be implemented with other machine-readable symbol technologies.

  Certain methods described herein can be generalized to handheld applications, and corresponding methods described herein can be generalized to pattern alignment applications.

  The techniques described herein may be applied to stitch data from multiple images to another ID application such as OCR reading. Known methods of OCR reading from multiple images select reading characters from individual images with the highest score. Individual characters are required so that the known method can be read from at least one image. With this technique, character reading can occur after the individual strokes of the character have been combined from multiple images.

  While only the specific embodiments described above are described, the techniques will be modified and implemented in a different and equivalent manner apparent to those skilled in the art having the benefit of the techniques herein. Furthermore, no limitations are intended except as set forth in the construction and design details herein, and as set forth in the claims below. Thus, it will be apparent that the specific embodiments described above may be substituted and modified, and that such modifications are considered within the scope and spirit of the technology. Accordingly, the protection sought here is set forth in the following claims.

Claims (29)

In a system for decoding a symbol using a symbol image,
An imaging device configured to acquire a plurality of images, each including a respective symbol data region;
A processor operably connected to the imaging device;
The processor is
(1) receiving a first plurality of images for a first read cycle of the system, wherein the first plurality of images are acquired by the imaging device in a first acquisition order using respective acquisition settings; The first plurality of images includes first and second images respectively acquired using first and second acquisition settings determined based on an initial acquisition setting order;
(2) A data stitch algorithm is executed, and the data stitch algorithm includes:
(I) generating a composite model that is a model of a plurality of known features of the symbol;
(Ii) comparing the composite model of the symbol with at least one of the first and second images;
(Iii) converting a first symbol data area of the first image into a first binary matrix, converting a second symbol data area of the second image into a second binary matrix;
(Iv) at least partially combining the first binary matrix with the second binary matrix to generate a composite binary matrix that is a decodable representative of the symbol;
(3) attempting to decode the symbol based at least in part on the composite binary matrix;
(4) receiving a second plurality of images for a second read cycle of the system and using the updated acquisition settings, wherein the second plurality of images are determined based on an updated acquisition setting order; First and second contributions of the first and second images, respectively, to the attempt to decode the symbol, wherein the updated acquisition setting order is acquired by the imaging device in a second acquisition order. Determined based at least in part on the
A system characterized by being configured as follows.   If the first image contributes to the successful decoding of the symbol more than the second image contributes to the successful decoding of the symbol, then the first acquisition setting is in the updated acquisition setting order The system of claim 1, wherein the system is determined to precede the second acquisition setting.   When the user specifies an acquisition setting different from the first and second acquisition settings for the second read cycle, the user specific acquisition setting is changed to the first and second acquisition settings in the updated acquisition setting order. The system of claim 1, wherein the system is determined to precede. In order to determine the updated acquisition setting order based at least in part on the first and second contributions, the processor further comprises:
A score associated with the first and second contributions is assigned to each of the first and second acquisition settings, and each average is assigned to each of the first and second acquisition settings. 2 based at least in part on each of the pre-determined scores of the initial acquisition settings of 2 and based at least in part on the respective score assigned to the first and second acquisition settings;
The system of claim 1, wherein the updated acquisition setting order is determined based at least in part on the calculated average.   A minimum impact point is set to at least one of the first and second acquisition settings based at least in part on at least one of the corresponding first and second images that do not contribute to successful decoding of the symbol. 5. The system of claim 4, wherein the system is assigned to.   The system of claim 4, wherein the average is calculated only if an attempt to decode the symbol results in a successful decoding of the symbol for the first read cycle. The average calculation is based at least in part on the processor access to a stored retrieval settings table that includes a number of table entries;
Each of the table entries includes an acquisition setting field and a classification setting value field;
5. The system of claim 4, wherein each of the classification value fields for a particular table entry represents the previously determined score for the acquisition setting represented by the acquisition setting field for the specific table entry. The processor is further configured to update an entry in the classification value field based at least in part on the calculated average, respectively.
The system of claim 7, wherein the updated acquisition setting order is determined based at least in part on the updated entry in the classification value field. In order to determine the updated acquisition setting order, the processor further comprises:
5) Set the maximum number of images for a given decoding attempt,
6) identify a first subset of the first plurality of images including the maximum number of the images;
7) process the subset of images and attempt to decode the symbols;
8) If the processing of the first sub-set results in a successful decoding of the symbol, assign a score to the acquisition setting used to acquire the image of the first sub-set;
9) If the processing of the first subset does not result in successful decoding,
v) identify the highest score image of the first sub-set in which the highest score was acquired with specific acquisition settings;
vi) Taking the highest number of images as a part of a second sub-set of the first plurality of images, at least one of the highest score image of the first sub-set and another image acquired with the acquisition setting Nominate one
vii) processing the second subset of images to attempt to decode the symbols;
viii) configured to assign a score to the acquisition settings used to acquire the images of the second sub-set if the processing of the second sub-set results in a successful decoding The system according to claim 1, wherein: A method of decoding a symbol using an image of the symbol,
Generating a composite model of the symbol, which is a model of a known feature of the symbol;
An imaging device is used to acquire a first image and a second image for a first read cycle, wherein the first image is acquired using a first acquisition setting and a first symbol data region is The second image is acquired using a second acquisition setting and includes a second symbol data region;
Comparing a composite model of the symbols with the first image to extract a first binary matrix;
Comparing the combined model of the symbols with the second image to extract a second binary matrix;
Compound at least partially the first binary matrix into the second binary matrix;
Generating a composite binary matrix that is a decodable representative of the symbol;
Attempting to decode the symbol based at least in part on the composite binary matrix;
Identifying first and second contributions of the first and second images, respectively, to an attempt to decode the symbol;
Determining an acquisition setting order configured for at least the first and second acquisition settings based at least in part on the first and second contributions;
Causing the imaging device to acquire a third image for a second read cycle and using the third acquisition setting determined at least in part based on the updated acquisition setting order; A method of decoding a symbol, characterized in that is obtained.   If the first image contributes to the successful decoding of the symbol more than the second image contributes to the successful decoding of the symbol, the first acquisition setting is changed to the first acquisition setting in the updated acquisition setting order. The method of claim 10, wherein the method is determined to precede 2.   When the user specifies an acquisition setting in the second read cycle, the user characteristic acquisition setting is determined to precede the first and second acquisition settings in the updated acquisition setting order. The method of claim 10. Determining the updated acquisition setting based at least in part on the first and second contributions;
Assigning to the first and second acquisition settings a score number associated with the first and second contributions, respectively, from a predetermined number of total scores;
The respective moving averages for the first and second initial acquisition settings are based at least in part on respective previously determined scores and the respective acquisitions assigned to the first and second acquisition settings. Calculating for each of the first and second acquisition settings based at least in part on the score;
The method of claim 10, wherein the updated acquisition setting order is determined based at least in part on the calculated moving average.   A minimum impact score is assigned to at least one of the first and second acquisition settings based at least on the corresponding at least one first and second image that does not contribute to successful decoding of the symbol. 14. A method according to claim 13 characterized in that   14. The method of claim 13, wherein the moving average is calculated only if an attempt to decode the symbol results in a successful decoding of the symbol for the first read cycle.   11. The method of claim 10, wherein at least one of the first, second, and third acquisition settings specifies at least one of illumination mode, image exposure, image gain, and image offset. The attempt to decode the symbol is further based at least in part on applying post acquisition processing to the first and second images according to first and second post acquisition processing settings, respectively. And the second post acquisition processing setting includes one or more of an image filter setting and an adjacent region of interest;
The method further comprises determining an updated post acquisition setting order based at least in part on the first and second contributions;
11. The post-acquisition process of claim 10, wherein the third image is processed after acquisition using a third post acquisition processing setting that is determined based at least in part on the updated post acquisition setting order. The method described. Using the initial order of each acquisition setting determined at least based on the initial acquisition setting order, the first image is acquired by the imaging device in the first acquisition order, and the symbol is decoded using the image of the symbol A way to
Attempting to decode the symbol by processing the first image by at least partially stitching from two or more of the first images;
Identifying at least one contribution corresponding to an attempt to decode the symbol for at least one of two or more of the first images acquired using at least one of the initial acquisition settings;
Determining an updated acquisition setting order for the set of initial acquisition settings based at least in part on the at least one contribution;
In the imaging device, at least one second image is updated in a decoding attempt using an updated order of second acquisition settings determined based at least in part on the updated acquisition settings A method of decoding symbols to be processed using a processing order determined based at least in part on the acquired acquisition order. Attempting to decode the symbol includes applying a post acquisition process to two or more of the first images based at least in part on an initial post acquisition set order;
The method further comprises determining an updated post acquisition setting based at least in part on the at least one contribution;
The method of claim 18, wherein after being acquired, the second image is processed based at least in part on the updated post acquisition set order. Applying one or more image filters to at least one or more of the first images in the post-acquisition process, and adjusting one or more of the first images At least one of them,
For at least one of providing one or more filters and adjusting one or more regions of interest, the initial and updated post acquisition set orders are the initial and updated orders respectively. 20. The method of claim 19, wherein: A system for decoding a symbol using an image of the symbol,
An imaging device configured to acquire a plurality of images, each of the acquired images having a respective symbol data region;
A processor operably coupled to the imaging device;
The processor is
1) receiving a first plurality of images for a first read cycle of the system, wherein the plurality of first images are in a first acquisition order using respective acquisition settings determined according to an initial acquisition setting order; Acquired by the imaging device,
2) performing a data stitching algorithm,
The data stitch algorithm is
i) generating a composite model that is a model of a plurality of known features of the symbol;
ii) comparing the combined model of the symbols with at least the first and second images;
iii) converting a first symbol data region of the first image into a first binary matrix, converting a second symbol data region of the second image into a second binary matrix;
iv) at least partially combining the first binary matrix with the second binary matrix to generate a composite binary matrix that is a decodable representative of the symbol;
3) Attempt to decode the symbol based at least in part on the composite binary matrix;
4) receiving a second plurality of images for a second read cycle of the system, the second plurality of images being processed for decoding in a processing order, the processing order being the ones of the first and second images; A system for decoding a symbol, characterized in that it is determined based at least in part on first and second contributions to a symbol decoding attempt, respectively. If the first image contributes to the successful decoding of the symbol more than contributes to the successful decoding of the second image, the image acquired with the first acquisition setting is the processing image in the processing order. The system of claim 21, wherein the system is determined to precede an image acquired with the second acquisition setting.   The system of claim 21, wherein when a user specifies an acquisition setting for the second read cycle, the user specific acquisition setting is prioritized in the processing order. In order to determine the processing order based at least in part on the first and second contributions, the processor further comprises:
Assigning a score associated with the first and second contributions to the first and second acquisition settings, respectively;
For each of the first and second acquisition settings, the first and second initial acquisitions are based at least in part on scores previously determined for the first and second initial acquisition settings, respectively. The system of claim 21, wherein the system is configured to calculate a respective average based at least in part on a score assigned to each setting.   A minimum impact score is assigned to at least one of the first and second acquisition settings based at least in part on a corresponding at least one of the first and second images that does not contribute to successful decoding of the symbol. 25. The system of claim 24, wherein:   25. The system of claim 24, wherein the average is calculated only if an attempt to decode the symbol results in a successful decoding of the symbol for the first read cycle. The average calculation is based at least in part on the processor evaluating a stored acquisition setting that includes the number of table entries;
Each table entry includes an acquisition setting field and a classification value field,
25. The system of claim 24, wherein each of the classification value fields for a particular table entry indicates the previously determined score for the acquisition setting indicated by the acquisition setting for the specific entry. The processor is further configured to update each of the entries in the classification value field based at least in part on the calculated average;
28. The system of claim 27, wherein the processing order is determined based at least in part on the updated entry in the classification value field. To determine the processing order, the processor further sets 5) the maximum number of images for a given decoding attempt;
6) identify a first subset of the first plurality of images including the maximum number of the images;
7) try to process the first subset to decode the symbols;
8) If the processing of the first sub-set results in a successful decoding of the symbol, assign a score to the acquisition setting used to acquire the first sub-set of images;
9) If the processing of the first subset does not result in successful decoding:
i) specifying the highest score image acquired with a specific acquisition setting in the first sub-set;
ii) as part of a second sub-set of the first plurality of images including the highest number of images, the highest score image of the first sub-set and another image acquired with the specific acquisition setting Nominate at least one of
iii) processing the second subset of images to decode the symbols;
iv) If the processing of the second sub-set results in a successful decoding, it is configured to assign a score to the acquisition settings used to acquire the images of the second sub-set The system according to claim 21, wherein:

Images