US20120099758A1 - Correcting Image Capture Distortion - Google Patents

Correcting Image Capture Distortion Download PDF

Info

Publication number
US20120099758A1
US20120099758A1 US13278513 US201113278513A US2012099758A1 US 20120099758 A1 US20120099758 A1 US 20120099758A1 US 13278513 US13278513 US 13278513 US 201113278513 A US201113278513 A US 201113278513A US 2012099758 A1 US2012099758 A1 US 2012099758A1
Authority
US
Grant status
Application
Patent type
Prior art keywords
digital watermark
distortion
input data
watermark signal
pre
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US13278513
Inventor
Alastair M. Reed
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Digimarc Corp
Original Assignee
Digimarc Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00General purpose image data processing
    • G06T1/0021Image watermarking
    • G06T1/005Robust watermarking, e.g. average attack or collusion attack resistant
    • G06T1/0064Geometric transfor invariant watermarking, e.g. affine transform invariant
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001Image restoration
    • G06T5/003Deblurring; Sharpening
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001Image restoration
    • G06T5/002Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/006Geometric correction

Abstract

The present disclosure relates generally to improving readability of machine-readable information, despite signal capture distortion. One claim recites an apparatus comprising: memory for storing input data; an electronic processor programmed for: altering a digital watermark or a digital watermarking process to pre-distort a digital watermark signal, wherein the altering is intended to counteract or compensate for expected distortion so as to enable machine-based detection of an embedded, pre-distorted digital watermark signal despite the expected distortion, wherein the expected distortion includes distortion associated with optical capture of the input data; and embedding the pre-distorted digital watermark signal in the input data. Of course, other claims and combinations are provided as well.

Description

    RELATED APPLICATION DATA
  • This application is a division of U.S. patent application Ser. No. 12/109,573, filed Apr. 25, 2008 (published as U.S. 2008-0298632 A1), which claims the benefit of U.S. Patent Application No. 60/913,987, filed Apr. 25, 2007. Each of the above patent documents is hereby incorporated by reference in its entirety.
  • TECHNICAL FIELD
  • The present disclosure relates generally to data hiding and digital watermarking. In some implementations the present disclosure relates to correcting optical scan or other capture distortion, e.g., such as lens blurring, focus errors and other scanning anomalies.
  • BACKGROUND AND SUMMARY
  • Digital watermarking—a form of steganography—is a process for modifying media content to embed a machine-readable code into the content. The content may be modified such that the embedded code is imperceptible or nearly imperceptible to the user, yet may be detected through an automated detection process. Most commonly, digital watermarking is applied to media such as images, audio signals, and video signals. However, it may also be applied to other types of data, including text documents (e.g., through line, word or character shifting, background texturing, etc.), software, multi-dimensional graphics models, and surface textures of objects.
  • Digital watermarking systems typically have two primary components: an embedding component (or encoder) that embeds a watermark in media content, and a reading component (or detector) that detects and reads the embedded watermark. The embedding component embeds a watermark by altering data samples of the media content in the spatial, temporal or some other domain (e.g., Fourier, Discrete Cosine or Wavelet transform domains). The reading component analyzes target content to detect whether a watermark is present. In applications where the watermark encodes information (e.g., a plural-bit message), the reader extracts this information from the detected watermark.
  • The present assignee's work in steganography, data hiding and digital watermarking is reflected, e.g., in U.S. Pat. Nos. 5,862,260, 6,408,082, 6,614,914 and 7,027,614, which are each hereby incorporated by reference. A great many other approaches are familiar to those skilled in the art. The artisan is presumed to be familiar with the full range of literature concerning steganography, data hiding and digital watermarking.
  • In some cases digital watermarking and other machine-readable indicia (e.g., barcodes, data glyphs, etc.) may be detected from optical scan data, examples of which are disclosed in, e.g., U.S. Pat. Nos. 5,978,773, 6,522,770, 6,681,028, 6,947,571 and 7,174,031, which are each hereby incorporated by reference.
  • Today's camera phones and other handheld readers present expanded decoding opportunities—and challenges.
  • One challenge is providing handheld cameras (e.g., in a cell phone or other mobile device) to an army of users, with nearly each user having a different idea on proper focal length for image and video capture.
  • In some cases a user may want to be close to a marked image or video to, e.g., capture high spatial frequency content (see FIG. 1); but the close positioning often results in a captured image that is slightly out of focus (or blurred)—which may hamper detection of a machine-readable code contained or represented in the image.
  • Thus, one inventive combination provides a method including: obtaining input data; altering a digital watermark or a digital watermarking process to pre-distort a digital watermark signal, wherein the altering is intended to counteract or compensate for expected distortion enabling machine-based detection of an embedded, pre-distorted digital watermark signal despite the expected distortion; and embedding the pre-distorted digital watermark signal in the input data.
  • Another inventive combination provides a method including: obtaining a plurality of different models representing different expected distortion associated with a plurality of different data captures, the different data captures each resulting in distortion of a machine-readable signal; indexing the different models; upon receiving a request, selecting a model associated with the request; and providing the selected model.
  • Still another inventive combination provides a method including: obtaining input data; altering a digital watermark or a digital watermarking process to pre-distort a digital watermark signal, wherein the altering is intended to counteract or compensate for expected distortion due to image capture or an image capture device, the altering enabling machine-based detection of an embedded, pre-distorted digital watermark signal despite the expected distortion; and embedding the pre-distorted digital watermark signal in the input data.
  • Yet another inventive combination provides a method including: obtaining input data, the input data representing imagery captured with at least an optical lens, the input data comprising test data and a machine-readable signal; evaluating characteristics associated with the test data to determine information regarding lens blurring of the input data associated with the optical lens; adjusting the input data to compensate for or to correct the lens blurring based at least in part on the information; and analyzing the compensated for or corrected input data to obtain the machine-readable signal.
  • Another inventive combination includes: obtaining input data, the input data representing imagery or video, the input data comprising test data and a machine-readable signal; determining characteristics associated with the test data to determine information regarding signal capture distortion of the input data; based on at least the characteristics, determining an amount of correction or counteracting to be applied to the input data; applying a determined amount of correction or counteracting to the input data; and analyzing corrected or counteracted input data to obtain the machine-readable signal.
  • Further combinations, aspects, implementations, features, embodiments and advantages will become even more apparent with reference to the following detailed description, the accompanying drawings and claims.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 illustrates image capture with a cell phone including an optical sensor.
  • FIG. 2A illustrates an example target image.
  • FIG. 2B illustrates examples of adverse effects on focus (e.g., blurring) of the FIG. 2A example target image.
  • FIG. 3 illustrates a plot diagram.
  • FIG. 4 is a block diagram illustrating an embedding process.
  • FIG. 5 is a diagram showing an approximation or estimate of lens blurring.
  • FIG. 6 is a correction template or filter to compensate for the blurring shown in FIG. 5.
  • FIG. 7 is a blurred signal (FIG. 5) after compensation by the FIG. 6 correction template or filter.
  • FIG. 8A is a diagram showing test data in a spatial frequency domain (with acceptable lens blur); FIG. 8B is another diagram showing test data in a spatial frequency domain (with unacceptable lens blur).
  • FIG. 9 shows a digital watermarked gray patch.
  • FIG. 10A shows the watermarked gray patch of FIG. 9 after capture distortion (e.g., lens blurring).
  • FIG. 10B shows the FIG. 10A patch after compensation or correction (e.g., using the FIG. 6 correction).
  • DETAILED DESCRIPTION
  • When an image is distorted (e.g., blurred, out of focus, etc.) by an optical capture device (e.g., a cell phone camera, PDA, digital camera, etc.) the loss of information from a target image (FIG. 2A) can be in the form shown in FIG. 2B. For example, in some cases there may be a loss of information in some high spatial frequencies.
  • The distortion may result in some high spatial frequencies of the target image being “out of phase” or otherwise distorted.
  • In the case of a target image including or representing machine-readable information, e.g., a barcode, data glyph or digital watermark, some higher spatial frequency information corresponding to at least some of the machine-readable information is also out of phase, making reliable detection of the machine-readable information more difficult.
  • Distortion such as blurring can be modeled or approximated by a convolution of a target image with, e.g., a Bessel function, as postulated by J. I. Yellott et al., “Correcting spurious resolution in defocused images,” SPIE 6492, pp. 649200-1-649200-12 (2007), hereby incorporated by reference. FIG. 3 illustrates a portion of a Bessel function, where the negative areas (shown with dotted boxes) correspond to a 90 degree phase shift.
  • Some examples are now provided illustrating and describing compensation for distortion.
  • In a first implementation a target image includes a digital watermark signal. But prior to being embedded in the target image, the digital watermark signal is pre-distorted with a phase shift, e.g., shifted 90 degrees relative to expected distortion. For example, if the distortion is likely to occur in high frequency areas such as discussed above with respect to convolving with the function shown in FIG. 3, high frequency components of the digital watermark are shifted 90 degrees. (An appropriate shift can be achieved by, e.g., convolving a digital watermark signal with an appropriate or corresponding Bessel function.) The pre-distorted watermark is then embedded into an image and printed, engraved or otherwise reproduced. Then, when expected distortion occurs (e.g., blurring due to image capture or focus errors), the high frequency watermark components are distorted (e.g., convolved) into a more readable form, enabling better watermark detection.
  • As another example, if a digital watermark includes signal elements with values corresponding to [+1, 0, −1] at positions in a high frequency area, the signal is preferably phase shifted to compensate for excepted distortion. In the case of distortion modeled by a convolution with a target image and the function shown in FIG. 3, the signal is shifted 90 degrees resulting in signal elements with values corresponding to [−1, 0, +1] at the positions in the high frequency area. When this pre-distorted digital watermark signal is subjected to blurring during image capture, the pre-distorted signal elements are distorted again—but in an expected manner—resulting in signal elements with values corresponding to [+1, +1, 0 −1], which correspond to the original signal elements. (The blurring shifts the signal essentially back to its original, pre-distorted form.)
  • FIG. 4 illustrates a block diagram corresponding to one implementation of pre-distortion.
  • An image 10 is obtained to be watermarked. The image 10 can be of any form, e.g., a color image, grayscale, a photograph, a graphic or artwork, video representation, etc. If the image 10 is in analog form, e.g., a printed image, it can be optically scanned or captured (e.g., with a digital camera or optical sensor) or otherwise converted into a digital image.
  • A message 12 is provided (e.g., 1 or more bits). The message 12 is intended to be hidden within the image 10 with digital watermarking or steganography. A digital watermarking signal is generated by a digital watermark encoder 20 to represent or otherwise carry the message 12. The digital watermark signal may be an additive signal, e.g., one that is added (or subtracted, multiplied, etc.) to the image 10, may represent instructions or changes that should be made to the image 10 to carry the message, e.g., based on a key, and/or may include changes or modifications to frequency domain coefficients, and/or may include a random or pseudo-random component. Of course, other digital watermarking techniques may be used as well.
  • The digital watermark signal is pre-distorted to compensate for expected distortion, e.g., by a distortion module 22. Distortion module 22 may be incorporated into encoder 20 or encoder 20 may other wise communicate or cooperate with a distortion module 22. Distortion module 22 accesses or otherwise determines a distortion model 24. Distortion model 24 provides a template, mask or other instructions to be used by distortion module 22 when pre-distorting the digital watermark signal. The distortion model 24 can be based on or tailored to, e.g., the type of image 10, the watermark message 12, the expected distribution channel through which image 10 will travel, the expected type of image capture, optical lens system or a model of distortion introduced by such, digital watermark encoder 20, human visual system (HVS), etc. In one implementation, the distortion module 22 (or watermark encoder 20) communicates with a database, model library or a network remote resource to access an appropriate model 24. The term “appropriate” in this context implies that a model 24 is selected or obtained to compensate for expected distortion. Of course, the distortion module 22 may be pre-programmed with a default distortion model 24, e.g., based on or tailored to expected distortion that a digital watermark will most likely encounter.
  • Prior to embedding the digital watermark signal in the image 10, the distortion module 22 accesses a distortion model 24 and pre-distorts a digital watermark signal to counteract or compensate for expected distortion associated with optical scanning or capture. Returning to an example discussed above, e.g., blurring or other distortion that can be modeled with convolving image 10 with a Bessel Function, the digital watermark is phase shifted in some or all higher frequencies, e.g., 90 degrees, to compensate for the expected distortion.
  • The pre-distorted digital watermark—representing or carrying message 12—is embedded in image 10 to yield a watermarked image 14. The watermarked image 14 can then be printed or otherwise reproduced in an analog form.
  • A printed (or otherwise fixed) image 16 that includes a pre-distorted digital watermark signal can then be optically scanned or captured—which introduces expected distortion. But since the digital watermark signal has been pre-distorted, the expected distortion transforms (e.g., modeled as a convolution with a determined function) the pre-distorted digital watermark signal into a more readable form.
  • Optical data corresponding to the printed (or otherwise fixed), watermarked image 16 is provided to a digital watermark decoder to analyze the scan data and recover the message 12. Of course, the digital watermark decoder may be co-located with an image sensor or otherwise located on a handheld device (e.g., a cell phone or personal digital assistant (PDA)). Still further, the digital watermark decoder may be remotely located from an image capture device.
  • Post-Capture Correction
  • In other embodiments, a digital watermark is provided without the pre-distortion discussed above. Correction for lens blur is applied after image capture. These post-capture correction embodiments may allow for an even more imperceptible watermark, relative to a pre-distortion watermark.
  • While post-capture creation may include some post-processing, it may be more flexible relative to the pre-distortion methods and systems discussed above, as many types or amounts of capture distortion can be corrected.
  • A digital watermark is embedded in a host signal (e.g., an image or video). The embedded host signal is provided onto a surface (projected or rendered if video) or object (e.g., printed, engraved, etc.) and then an optical scan or digital image is captured of the surface or object.
  • Recall, here, the digital watermark has not been pre-distorted as discussed above with respect to FIG. 4. So prior to watermark detection (or as an initial stage of the watermark detection) the captured image data is filtered or corrected. The filter adjusts the captured image data to compensate for the capture distortion (e.g., lens blur).
  • To determine an amount or level of compensation or correction, one process quantifies or otherwise evaluates image blur in the image or video data. Such an evaluation or quantification helps to determine an appropriate amount of correction to be applied to captured image and video.
  • In one example, test data can be introduced into an image or video. Characteristics associated with the test data can be evaluated in captured imagery to help determine an amount or level of lens blur.
  • One example of test data is white noise (or other type of noise, e.g., a pseudo- random pattern, noise in some predetermined frequencies, etc.), which can be added to an image or video, e.g., as a part of or before/after digital watermark embedding. The white noise is preferably imperceptible in the image or video, but of course, there may be some applications where some perceptibility is allowed. After image or video capture, the white noise can be evaluated, e.g., in a spatial frequency domain as shown in FIGS. 8A and 8B.
  • FIG. 8A illustrates a spatial frequency response for captured imagery with acceptable capture distortion. The term “acceptable” can be quantified by determining white noise signal magnitude at a predetermined spatial frequency or range of frequencies. If the signal magnitude is at or above a predetermined magnitude for the predetermined spatial frequency (or range of frequencies) the capture distortion can be deemed acceptable.
  • FIG. 8B illustrates a spatial frequency response for captured imagery with unacceptable capture distortion. The term “unacceptable” can be quantified by determining white noise signal magnitude at a predetermined spatial frequency or range of frequencies. If the signal magnitude is below a predetermined magnitude for the predetermined spatial frequency (or range of frequencies) the capture distortion can be deemed unacceptable.
  • The signal magnitude level can also be used to help determine or estimate an amount or level of correction or adjustment that is needed as well. For example, if the magnitude is below (or between) a first pre-determined amount, a first level is determined; and if the magnitude is below (or between) a second pre-determined amount, a second level is determined An amount or level of correction or adjustment corresponding to the first pre-determined amount or the second pre-determined amount is then selected.
  • Another example of test data are predetermined structures. For example, a dark spot surrounded by a lighter (e.g., white) color is provided in the spatial domain in an image or video. The dark spot and surrounding lighter color are preferably subtle so as not to detract from the image or video. A spatial frequency plot of captured imagery including such test data is useful in evaluating capture distortion such as lens blur. The dot structure is approximated by a delta function in a spatial frequency domain. The magnitude and frequency location of the delta function can be used to determine an amount, type or level of capture distortion. Appropriate correction can be determined and applied to captured imagery if needed.
  • Once an estimate of the lens blur is determined, it can be used to help correct capture distortion in captured imagery. For example, the estimate may be mapped against pre-determined levels of lens blurring, e.g., stored in a data structure associated with or cooperating with a digital watermark detector. In one implementation, estimated distortion is used to determine a level, type or degree of lens blurring or other distortion. A corresponding scale factor or other corresponding modification can then be obtained, e.g., from a data structure. An obtained scale factor or other modification can then be applied to a master correction template or filter.
  • An approximation of one example lens blur is shown in FIG. 5, where pixel magnitude of the blur is illustrated. The illustrated blur spans more than 15 pixels, which would severely disrupt an image or video in the pixel area. A master correction template or filter is shown in FIG. 6, e.g., for a 1-dimensional signal. (Of course, other types of signals, e.g., 2-dimensional signal can be similarly corrected.) The master correction template or filter is adjusted, scaled or modified by a determined scale factor or other corresponding modification, if needed. The adjusted, scaled or modified template or filter is then applied to the captured imagery to counteract, adjust or correct image blur (FIG. 7). A digital watermark detector (or other machine-readable symbology detector) analyzes the counteracted, adjusted or corrected captured imagery to read or detect a digital watermark (or other machine-readable code).
  • In another implementation, a correction template or filter is generated upon evaluation of image blur. For example, based on an evaluation of test data in a spatial frequency domain, a correction filter is generated to counteract lens blur. A generated correction template or filter is applied to captured imagery to counteract, adjust or correct image blur. A digital watermark detector (or other machine-readable symbology detector) analyzes the counteracted, adjusted or corrected captured imagery to read or detect a digital watermark (or other machine-readable code).
  • To further illustrate some of these processes, an original watermark was created by embedding a Digimarc Mobile digital watermark signal (provided by Digimarc Corporation headquartered in Beaverton, Oregon) into a gray image patch. The digital watermark signal is readily detectable from the watermarked gray image patch (FIG. 9). Lens blur (e.g., approximated by FIG. 5) is applied to the watermarked gray patch (FIG. 9), resulting in a blurred image patch shown in FIG. 10A. The digital watermark is difficult (and perhaps impossible) to read from the distorted FIG. 10A patch. But, after compensation (e.g., as approximated in FIG. 6) is applied to the distorted FIG. 10A image, e.g., as shown in FIG. 6, the watermark is readily detected from the resulting image shown in FIG. 10B. My tests show that the compensated for gray patch (FIG. 10B) reads about 5 dB better than the FIG. 10A patch and approaches the detection results of a digital watermark embedded in the original gray image patch (FIG. 9).
  • Concluding Remarks
  • Having described and illustrated the principles of the technology with reference to specific implementations, it will be recognized that the technology can be implemented in many other, different, forms.
  • The methods, processes, components, modules, generators and systems described above may be implemented in hardware, software or a combination of hardware and software. For example, digital watermark embedding or decoding processes may be implemented in a programmable computer or a special purpose digital circuit. Similarly, these watermarking processes may be implemented in software, firmware, hardware, or combinations of software, firmware and hardware.
  • The methods, components and processes described above may be implemented in software programs (e.g., C, C++, Visual Basic, Java, executable binary files, etc.) executed from a system's memory (e.g., a computer readable medium, such as an electronic circuitry and/or an optical or magnetic storage device).
  • The section headings are provided for the reader's convenience. Features found under one heading can be combined with features found under another heading. Of course, many other combinations are possible given the above detailed and enabling disclosure.
  • We have used the terms “image” and “imagery” above. Both of these terms should be construed broadly enough to include images or video.
  • The particular combinations of elements and features in the above-detailed embodiments are exemplary only; the interchanging and substitution of these teachings with other teachings in this and the above-mentioned U.S. patent documents are also contemplated.

Claims (25)

  1. 1. A method comprising:
    obtaining input data;
    using a programmed processor, altering a digital watermark or a digital watermarking process to pre-distort a digital watermark signal, wherein the altering is intended to counteract or compensate for expected distortion so as to enable machine-based detection of an embedded, pre-distorted digital watermark signal despite the expected distortion, wherein the expected distortion includes distortion associated with optical capture of the input data; and
    embedding the pre-distorted digital watermark signal in the input data.
  2. 2. The method of claim 1 further comprising printing or engraving the input data including the pre-distorted digital watermark signal.
  3. 3. The method of claim 1 wherein the input data comprises data representing an image or video.
  4. 4. The method of claim 1 wherein the altering utilizes a model of the distortion resulting from optical capture.
  5. 5. The method of claim 1 wherein the expected distortion includes blurring resulting from optical capture of the printed or engraved input data.
  6. 6. The method of claim 5 wherein the blurring is associated with lens focal length.
  7. 7. The method of claim 5 wherein the blurring—left uncompensated for—would result in a loss of high frequency information of the digital watermark signal upon signal detection.
  8. 8. The method of claim 1 wherein said act of altering alters a phase of the digital watermark signal relative to the expected distortion.
  9. 9. The method of claim 8 wherein an altered phase of the digital watermark signal causes the digital watermark signal to be less readable in the absence of the expected distortion.
  10. 10. The method of claim 1 wherein expected distortion is modeled as a convolution of the input data with a predetermined function.
  11. 11. The method of claim 10 wherein the predetermined function is approximated by a Bessel function.
  12. 12. The method of claim 1 wherein the expected distortion is associated with lens focal length.
  13. 13. A non-transitory computer readable medium comprising instructions or circuitry to perform the method of claim 1.
  14. 14. An apparatus comprising:
    memory for storing input data;
    an electronic processor programmed for:
    altering a digital watermark or a digital watermarking process to pre-distort a digital watermark signal, wherein the altering is intended to counteract or compensate for expected distortion so as to enable machine-based detection of an embedded, pre-distorted digital watermark signal despite the expected distortion, wherein the expected distortion includes distortion associated with optical capture of the input data; and
    embedding the pre-distorted digital watermark signal in the input data.
  15. 15. The apparatus of claim 14 in which the electronic processor controls printing or engraving of the input data including the pre-distorted digital watermark signal.
  16. 16. The apparatus of claim 14 wherein the input data comprises data representing an image or video.
  17. 17. The apparatus of claim 14 wherein the altering utilizes a model of the distortion resulting from optical capture.
  18. 18. The apparatus of claim 14 wherein the expected distortion includes blurring resulting from optical capture of the printed or engraved input data.
  19. 19. The apparatus of claim 18 wherein the blurring is associated with lens focal length.
  20. 20. The apparatus of claim 18 wherein the blurring—left uncompensated for—would result in a loss of high frequency information of the digital watermark signal upon signal detection.
  21. 21. The apparatus of claim 14 wherein the altering alters a phase of the digital watermark signal relative to the expected distortion.
  22. 22. The apparatus of claim 21 wherein an altered phase of the digital watermark signal causes the digital watermark signal to be less readable in the absence of the expected distortion.
  23. 23. The apparatus of claim 14 wherein expected distortion is modeled as a convolution of the input data with a predetermined function.
  24. 24. The apparatus of claim 23 wherein the predetermined function is approximated by a Bessel function.
  25. 25. The apparatus of claim 14 wherein the expected distortion is associated with lens focal length.
US13278513 2007-04-25 2011-10-21 Correcting Image Capture Distortion Abandoned US20120099758A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US91398707 true 2007-04-25 2007-04-25
US12109573 US9349153B2 (en) 2007-04-25 2008-04-25 Correcting image capture distortion
US13278513 US20120099758A1 (en) 2007-04-25 2011-10-21 Correcting Image Capture Distortion

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US13278513 US20120099758A1 (en) 2007-04-25 2011-10-21 Correcting Image Capture Distortion

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US12109573 Division US9349153B2 (en) 2007-04-25 2008-04-25 Correcting image capture distortion

Publications (1)

Publication Number Publication Date
US20120099758A1 true true US20120099758A1 (en) 2012-04-26

Family

ID=40088241

Family Applications (3)

Application Number Title Priority Date Filing Date
US12109573 Active 2030-04-28 US9349153B2 (en) 2007-04-25 2008-04-25 Correcting image capture distortion
US13278548 Abandoned US20120099759A1 (en) 2007-04-25 2011-10-21 Managing Models Representing Different Expected Distortions Associated with a Plurality of Data Captures
US13278513 Abandoned US20120099758A1 (en) 2007-04-25 2011-10-21 Correcting Image Capture Distortion

Family Applications Before (2)

Application Number Title Priority Date Filing Date
US12109573 Active 2030-04-28 US9349153B2 (en) 2007-04-25 2008-04-25 Correcting image capture distortion
US13278548 Abandoned US20120099759A1 (en) 2007-04-25 2011-10-21 Managing Models Representing Different Expected Distortions Associated with a Plurality of Data Captures

Country Status (1)

Country Link
US (3) US9349153B2 (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110216936A1 (en) * 2010-03-05 2011-09-08 Reed Alastair M Reducing Watermark Perceptibility and Extending Detection Distortion Tolerances
US8971567B2 (en) 2010-03-05 2015-03-03 Digimarc Corporation Reducing watermark perceptibility and extending detection distortion tolerances

Families Citing this family (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7644282B2 (en) 1998-05-28 2010-01-05 Verance Corporation Pre-processed information embedding system
US6737957B1 (en) 2000-02-16 2004-05-18 Verance Corporation Remote control signaling using audio watermarks
EP2782337A3 (en) 2002-10-15 2014-11-26 Verance Corporation Media monitoring, management and information system
US20060239501A1 (en) 2005-04-26 2006-10-26 Verance Corporation Security enhancements of digital watermarks for multi-media content
US8020004B2 (en) 2005-07-01 2011-09-13 Verance Corporation Forensic marking using a common customization function
US8781967B2 (en) 2005-07-07 2014-07-15 Verance Corporation Watermarking in an encrypted domain
US9147222B2 (en) 2010-06-23 2015-09-29 Digimarc Corporation Detecting encoded signals under adverse lighting conditions using adaptive signal detection
US8488900B2 (en) 2010-06-23 2013-07-16 Digimarc Corporation Identifying and redressing shadows in connection with digital watermarking and fingerprinting
US8923546B2 (en) 2010-07-02 2014-12-30 Digimarc Corporation Assessment of camera phone distortion for digital watermarking
US8838978B2 (en) 2010-09-16 2014-09-16 Verance Corporation Content access management using extracted watermark information
US8615104B2 (en) 2011-11-03 2013-12-24 Verance Corporation Watermark extraction based on tentative watermarks
US8923548B2 (en) 2011-11-03 2014-12-30 Verance Corporation Extraction of embedded watermarks from a host content using a plurality of tentative watermarks
US8682026B2 (en) * 2011-11-03 2014-03-25 Verance Corporation Efficient extraction of embedded watermarks in the presence of host content distortions
US8745403B2 (en) 2011-11-23 2014-06-03 Verance Corporation Enhanced content management based on watermark extraction records
US9323902B2 (en) 2011-12-13 2016-04-26 Verance Corporation Conditional access using embedded watermarks
US9571606B2 (en) 2012-08-31 2017-02-14 Verance Corporation Social media viewing system
US20140075469A1 (en) 2012-09-13 2014-03-13 Verance Corporation Content distribution including advertisements
US8869222B2 (en) 2012-09-13 2014-10-21 Verance Corporation Second screen content
WO2014153199A1 (en) 2013-03-14 2014-09-25 Verance Corporation Transactional video marking system
US9251549B2 (en) 2013-07-23 2016-02-02 Verance Corporation Watermark extractor enhancements based on payload ranking
US9208334B2 (en) 2013-10-25 2015-12-08 Verance Corporation Content management using multiple abstraction layers
US9596521B2 (en) 2014-03-13 2017-03-14 Verance Corporation Interactive content acquisition using embedded codes
JP6371092B2 (en) * 2014-03-27 2018-08-08 マクセル株式会社 Video processing apparatus and a projector apparatus using the same
US9965601B2 (en) * 2016-03-29 2018-05-08 Adobe Systems Incorporated Editing watermarked assets

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040125952A1 (en) * 2002-01-22 2004-07-01 Alattar Adnan M. Digital watermarking of low bit rate video
US20050018876A1 (en) * 2000-01-11 2005-01-27 Canon Kabushiki Kaisha Determination of a segmentation of a digital signal for inserting watermarking signals and the associated insertion
US20050213790A1 (en) * 1999-05-19 2005-09-29 Rhoads Geoffrey B Methods for using wireless phones having optical capabilities
US20060256227A1 (en) * 2003-05-16 2006-11-16 Heinrich Gotzig Digital camera device and method for producing the same
US7352913B2 (en) * 2001-06-12 2008-04-01 Silicon Optix Inc. System and method for correcting multiple axis displacement distortion

Family Cites Families (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5313280A (en) * 1992-07-13 1994-05-17 U S West Advanced Technologies, Inc. Method and apparatus for testing codec devices
US6408082B1 (en) * 1996-04-25 2002-06-18 Digimarc Corporation Watermark detection using a fourier mellin transform
US6614914B1 (en) * 1995-05-08 2003-09-02 Digimarc Corporation Watermark embedder and reader
US5862260A (en) 1993-11-18 1999-01-19 Digimarc Corporation Methods for surveying dissemination of proprietary empirical data
US6744906B2 (en) * 1995-05-08 2004-06-01 Digimarc Corporation Methods and systems using multiple watermarks
US6681028B2 (en) * 1995-07-27 2004-01-20 Digimarc Corporation Paper-based control of computer systems
US6307949B1 (en) * 1996-05-07 2001-10-23 Digimarc Corporation Methods for optimizing watermark detection
US6385329B1 (en) * 2000-02-14 2002-05-07 Digimarc Corporation Wavelet domain watermarks
US7027614B2 (en) * 2000-04-19 2006-04-11 Digimarc Corporation Hiding information to reduce or offset perceptible artifacts
US6590996B1 (en) * 2000-02-14 2003-07-08 Digimarc Corporation Color adaptive watermarking
US5978773A (en) * 1995-06-20 1999-11-02 Neomedia Technologies, Inc. System and method for using an ordinary article of commerce to access a remote computer
US6522770B1 (en) * 1999-05-19 2003-02-18 Digimarc Corporation Management of documents and other objects using optical devices
US7107451B2 (en) * 1996-07-02 2006-09-12 Wistaria Trading, Inc. Optimization methods for the insertion, protection, and detection of digital watermarks in digital data
DE69715118D1 (en) * 1996-09-18 2002-10-10 Nihon Koshitsu Garasu K K Building block and panel
US5825892A (en) * 1996-10-28 1998-10-20 International Business Machines Corporation Protecting images with an image watermark
US6266419B1 (en) * 1997-07-03 2001-07-24 At&T Corp. Custom character-coding compression for encoding and watermarking media content
US6108434A (en) * 1997-09-12 2000-08-22 Signafy, Inc. Counteracting geometric distortions for DCT based watermarking
JP3156667B2 (en) * 1998-06-01 2001-04-16 日本電気株式会社 Electronic watermark insertion system, the digital watermark characteristic table creation device
US6332194B1 (en) * 1998-06-05 2001-12-18 Signafy, Inc. Method for data preparation and watermark insertion
US6628329B1 (en) * 1998-08-26 2003-09-30 Eastman Kodak Company Correction of position dependent blur in a digital image
US6563935B1 (en) * 1998-12-02 2003-05-13 Hitachi, Ltd. Method of extracting digital watermark information and method of judging bit value of digital watermark information
US6459818B1 (en) * 1999-03-01 2002-10-01 University Of Rochester System for recovery of degraded images
JP3848008B2 (en) * 1999-03-26 2006-11-22 富士写真フイルム株式会社 Image quality evaluation method of the display device
US6571144B1 (en) * 1999-10-20 2003-05-27 Intel Corporation System for providing a digital watermark in an audio signal
US6721459B1 (en) * 1999-12-03 2004-04-13 Eastman Kodak Company Storing sharpness data using embedded carriers
US6721439B1 (en) 2000-08-18 2004-04-13 Hewlett-Packard Development Company, L.P. Method and system of watermarking digital data using scaled bin encoding and maximum likelihood decoding
JP3893922B2 (en) * 2000-10-18 2007-03-14 セイコーエプソン株式会社 Lens evaluation method and the lens evaluation device
GB0110151D0 (en) 2001-04-25 2001-06-20 Univ Bristol Watermarking with predistortion
US20040003052A1 (en) * 2002-03-20 2004-01-01 Fuji Photo Film Co., Ltd. Data detection method, apparatus, and program
US20030216654A1 (en) * 2002-05-07 2003-11-20 Weichao Xu Bayesian discriminator for rapidly detecting arrhythmias
US7596286B2 (en) * 2003-08-06 2009-09-29 Sony Corporation Image processing apparatus, image processing system, imaging apparatus and image processing method
US7443537B2 (en) * 2003-09-30 2008-10-28 Digimarc Corporation Methods and apparatuses for printer recalibration
US7269300B2 (en) * 2003-10-24 2007-09-11 Eastman Kodak Company Sharpening a digital image in accordance with magnification values
US7657750B2 (en) * 2003-11-24 2010-02-02 Pitney Bowes Inc. Watermarking method with print-scan compensation
US7672474B2 (en) * 2004-02-02 2010-03-02 Nippon Telegraph And Telephone Corporation Electronic watermark embedding device, electronic watermark detection device, method thereof, and program
EP1721216A2 (en) * 2004-02-23 2006-11-15 Philips Electronics N.V. Determining image blur in an imaging system
JP4201812B2 (en) * 2004-03-25 2008-12-24 三洋電機株式会社 Information data providing apparatus, and an image processing apparatus
US7379493B2 (en) * 2004-06-07 2008-05-27 Lockheed Martin Corporation Signal analyzer for detecting distortions in signals
US7668334B2 (en) * 2004-07-02 2010-02-23 Digimarc Corp Conditioning imagery to better receive steganographic encoding
US7730313B2 (en) * 2004-07-30 2010-06-01 Dolby Laboratories Licensing Corporation Tracing content usage
EP1889171A4 (en) * 2005-04-07 2012-11-28 Visionsense Ltd Method for reconstructing a three- dimensional surface of an object
JP4819723B2 (en) * 2006-03-16 2011-11-24 株式会社リコー Information extraction device, the information extraction method, an information extraction program, and a recording medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050213790A1 (en) * 1999-05-19 2005-09-29 Rhoads Geoffrey B Methods for using wireless phones having optical capabilities
US20050018876A1 (en) * 2000-01-11 2005-01-27 Canon Kabushiki Kaisha Determination of a segmentation of a digital signal for inserting watermarking signals and the associated insertion
US7352913B2 (en) * 2001-06-12 2008-04-01 Silicon Optix Inc. System and method for correcting multiple axis displacement distortion
US20040125952A1 (en) * 2002-01-22 2004-07-01 Alattar Adnan M. Digital watermarking of low bit rate video
US20060256227A1 (en) * 2003-05-16 2006-11-16 Heinrich Gotzig Digital camera device and method for producing the same

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Yellott et al., Correcting spurious resolution in defocused images, SPIE 6492, pp. 1-12, (1/28-2/1/2007) *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110216936A1 (en) * 2010-03-05 2011-09-08 Reed Alastair M Reducing Watermark Perceptibility and Extending Detection Distortion Tolerances
US8477990B2 (en) 2010-03-05 2013-07-02 Digimarc Corporation Reducing watermark perceptibility and extending detection distortion tolerances
US8873797B2 (en) 2010-03-05 2014-10-28 Digimarc Corporation Reducing watermark perceptibility and extending detection distortion tolerances
US8971567B2 (en) 2010-03-05 2015-03-03 Digimarc Corporation Reducing watermark perceptibility and extending detection distortion tolerances
US9311687B2 (en) 2010-03-05 2016-04-12 Digimarc Corporation Reducing watermark perceptibility and extending detection distortion tolerances
US9710870B2 (en) 2010-03-05 2017-07-18 Digimarc Corporation Extending watermark detection distortion tolerances

Also Published As

Publication number Publication date Type
US20080298632A1 (en) 2008-12-04 application
US9349153B2 (en) 2016-05-24 grant
US20120099759A1 (en) 2012-04-26 application

Similar Documents

Publication Publication Date Title
Swanson et al. Multiresolution scene-based video watermarking using perceptual models
Potdar et al. A survey of digital image watermarking techniques
Podilchuk et al. Image-adaptive watermarking using visual models
Popescu et al. Statistical tools for digital forensics
US6542620B1 (en) Signal processing to hide plural-bit information in image, video, and audio data
US6049627A (en) Covert digital identifying indicia for digital image
US6704869B2 (en) Extracting digital watermarks using logarithmic sampling and symmetrical attributes
US6804379B2 (en) Digital watermarks and postage
US7391880B2 (en) Color adaptive watermarking
Wang et al. Quality-aware images
Su et al. Digital watermarking of text, image, and video documents
US7068809B2 (en) Segmentation in digital watermarking
Voyatzis et al. Chaotic watermarks for embedding in the spatial digital image domain
US7277468B2 (en) Measuring quality of service of broadcast multimedia signals using digital watermark analyses
US20020015509A1 (en) Method and apparatus for superposing a digital watermark and method and apparatus for detecting a digital watermark
US7831062B2 (en) Arrangement of objects in images or graphics to convey a machine-readable signal
Hu et al. An algorithm for removable visible watermarking
US6959098B1 (en) Method and system for determining image transformation
Nikolaidis et al. Digital image watermarking: an overview
US20050041835A1 (en) Fragile and emerging digital watermarks
US6757407B2 (en) Transform domain image watermarking method and system
Pereira et al. Second generation benchmarking and application oriented evaluation
Nikolaidis et al. Robust image watermarking in the spatial domain
US7656930B2 (en) Assessing quality of service using digital watermark information
US6614914B1 (en) Watermark embedder and reader

Legal Events

Date Code Title Description
AS Assignment

Owner name: DIGIMARC CORPORATION, OREGON

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:REED, ALASTAIR M.;REEL/FRAME:027478/0750

Effective date: 20120104