IP Library Granted Patent US 10,127,441
Granted Patent B2
US 10,127,441 · App. 15/385,707 · Granted Nov 13, 2018

Systems and methods for classifying objects in digital images captured using mobile devices

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Quick Facts
Patent No.
US 10,127,441
App. No.
15/385,707
Granted
Nov 13, 2018
Kind
B2
Abstract

In one embodiment, a system includes: a processor; and logic in and/or executable by the processor to cause the processor to: generate a first feature vector based on a digital image captured by a mobile device; compare the first feature vector to a plurality of reference feature matrices; classify an object depicted in the digital image as a member of a particular object class based at least in part on the comparison; determine one or more object features of the object based at least in part on the particular object class; and detect one or more additional objects belonging to the particular object class based on the determined object feature(s). The one or more additional objects are depicted either in the digital image or another digital image received by the mobile device. Corresponding computer program products are also disclosed.

Claims (91)

1. A computer program product comprising: a computer readable storage medium having program code embodied therewith, the program code readable/executable by a processor to:

generate a first feature vector based on a digital image captured by a mobile device;

compare the first feature vector to a plurality of reference feature matrices;

classify an object depicted in the digital image as a member of a particular object class based at least in part on the comparison;

predict an identity of text depicted on the object using optical character recognition (OCR), wherein the prediction is based at least in part on the particular object class;

modify at least one identity predicted using OCR based on an expected format of text depicted on the object, wherein the expected format is determined based at least in part on the particular object class;

determine one or more object features of the object based at least in part on the particular object class; and

detect one or more additional objects belonging to the particular object class based on the determined object feature(s);

wherein the one or more additional objects are depicted either in the digital image or another digital image received by the mobile device.

2. The computer program product as recited in claim 1 , further comprising program code readable/executable by the processor to: generate a first representation of the digital image, the first representation being characterized by a reduced resolution,

wherein generating the first representation comprises:

dividing the digital image into a plurality of sections; and

determining, for each section, at least one characteristic value, each characteristic value corresponding to one or more features descriptive of the section; and

wherein the first feature vector is generated based on the first representation of the digital image.

3. The computer program product as recited in claim 1 , further comprising program code readable/executable by the processor to: define at least one hyperplane between at least some of the reference feature matrices, each hyperplane defining a boundary between first objects corresponding to one of the reference feature matrices and second objects corresponding to another of the reference feature matrices.

4. The computer program product as recited in claim 1 , wherein generating the first feature vector comprises determining a color profile of some or all of the object.

5. The computer program product as recited in claim 1 , further comprising program code readable/executable by the processor to: determine a geographical location associated with the mobile device, wherein classifying the object is further based at least in part on the geographical location.

6. The computer program product as recited in claim 1 , further comprising program code readable/executable by the processor to:

output an indication of the particular object class to a display of the mobile device; and

receive user input via the display of the mobile device in response to outputting the indication, the user input comprising either a confirmation, a negation, or a modification of the particular object class.

7. A computer program product comprising: a computer readable storage medium having program code embodied therewith, the program code readable/executable by a processor to:

generate a first feature vector based on a digital image captured by a mobile device;

compare the first feature vector to a plurality of reference feature matrices;

classify an object depicted in the digital image as a member of a particular object class based at least in part on the comparison;

determine one or more object features of the object based at least in part on the particular object class; and

detect one or more additional objects belonging to the particular object class based on the determined object feature(s);

wherein the one or more additional objects are depicted either in the digital image or another digital image received by the mobile device;

wherein the one or more object features comprise an object color scheme; and

wherein the computer program product further comprises program code readable/executable by the processor to binarize the digital image based at least in part on:

determining the object color scheme;

adjusting one or more binarization parameters based on the object color scheme; and

thresholding the digital image using the one or more adjusted binarization parameters.

8. The computer program product as recited in claim 7 , wherein the one or more object features comprise an object class mask, and

wherein the computer program product further comprises program code readable/executable by the processor to binarize the digital image based at least in part on:

determining the object class mask;

applying the object class mask to the digital image; and

thresholding a subregion of the digital image based on the object class mask

wherein the one or more object features further comprise an object color scheme, and

wherein binarizing the digital image further comprises:

determining the object color scheme;

adjusting one or more binarization parameters based on the object color scheme; and

thresholding the digital image using the one or more adjusted binarization parameters.

9. The computer program product as recited in claim 7 , further comprising program code readable/executable by the processor to: generate a first representation of the digital image, the first representation being characterized by a reduced resolution,

wherein generating the first representation comprises:

dividing the digital image into a plurality of sections; and

determining, for each section, at least one characteristic value, each characteristic value corresponding to one or more features descriptive of the section; and

wherein the first feature vector is generated based on the first representation of the digital image.

10. The computer program product as recited in claim 7 , further comprising program code readable/executable by the processor to: define at least one hyperplane between at least some of the reference feature matrices, each hyperplane defining a boundary between first objects corresponding to one of the reference feature matrices and second objects corresponding to another of the reference feature matrices.

11. The computer program product as recited in claim 7 , wherein generating the first feature vector comprises determining a color profile of some or all of the object.

12. The computer program product as recited in claim 7 , further comprising program code readable/executable by the processor to perform at least one of the following functions:

predict an identity of text depicted on the object using optical character recognition (OCR), wherein the prediction is based at least in part on the particular object class; and

modify at least one identity predicted using OCR based on an expected format of text depicted on the object, wherein the expected format is determined based at least in part on the particular object class.

13. A computer program product comprising: a computer readable storage medium having program code embodied therewith, the program code readable/executable by a processor to:

generate a first feature vector based on a digital image captured by a mobile device;

compare the first feature vector to a plurality of reference feature matrices;

classify an object depicted in the digital image as a member of a particular object class based at least in part on the comparison;

determine one or more object features of the object based at least in part on the particular object class; and

detect one or more additional objects belonging to the particular object class based on the determined object feature(s);

wherein the one or more additional objects are depicted either in the digital image or another digital image received by the mobile device;

wherein the one or more object features comprise an object class mask, and

wherein the computer program product further comprises program code readable/executable by the processor to binarize the digital image based at least in part on:

determining the object class mask;

applying the object class mask to the digital image; and

thresholding a subregion of the digital image based on the object class mask.

14. The computer program product as recited in claim 13 , wherein the one or more object features further comprise an object color scheme, and

wherein binarizing the digital image further comprises:

determining the object color scheme;

adjusting one or more binarization parameters based on the object color scheme; and

thresholding the digital image using the one or more adjusted binarization parameters.

15. The computer program product as recited in claim 13 , wherein the one or more object features comprise an object class mask, and

wherein the computer program product further comprises program code

readable/executable by the processor to binarize the digital image based at least in

part on:

determining the object class mask;

applying the object class mask to the digital image; and

thresholding a subregion of the digital image based on the object class mask

wherein the one or more object features further comprise an object color scheme, and

wherein binarizing the digital image further comprises:

determining the object color scheme;

adjusting one or more binarization parameters based on the object color scheme; and

thresholding the digital image using the one or more adjusted binarization parameters.

16. The computer program product as recited in claim 13 , further comprising program code readable/executable by the processor to: generate a first representation of the digital image, the first representation being characterized by a reduced resolution,

wherein generating the first representation comprises:

dividing the digital image into a plurality of sections; and

determining, for each section, at least one characteristic value, each characteristic value corresponding to one or more features descriptive of the section; and

wherein the first feature vector is generated based on the first representation of the digital image.

17. The computer program product as recited in claim 13 , further comprising program code readable/executable by the processor to: define at least one hyperplane between at least some of the reference feature matrices, each hyperplane defining a boundary between first objects corresponding to one of the reference feature matrices and second objects corresponding to another of the reference feature matrices.

18. The computer program product as recited in claim 13 , wherein generating the first feature vector comprises determining a color profile of some or all of the object.

19. The computer program product as recited in claim 13 , further comprising program code readable/executable by the processor to perform at least one of the following functions:

predict an identity of text depicted on the object using optical character recognition (OCR), wherein the prediction is based at least in part on the particular object class; and

modify at least one identity predicted using OCR based on an expected format of text depicted on the object, wherein the expected format is determined based at least in part on the particular object class.

Assignments (6)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 15, 2024
From: KOFAX, INC.
To: TUNGSTEN AUTOMATION CORPORATION
Reel/Frame 067428/0392 →
RELEASE OF SECURITY INTEREST Recorded Jul 21, 2022
From: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH
To: KAPOW TECHNOLOGIES, INC.; KOFAX, INC.
Reel/Frame 060805/0161 →
FIRST LIEN INTELLECTUAL PROPERTY SECURITY AGREEMENT Recorded Jul 20, 2022
From: KOFAX, INC.; PSIGEN SOFTWARE, INC.
To: JPMORGAN CHASE BANK, N.A. AS COLLATERAL AGENT
Reel/Frame 060757/0565 →
SECURITY INTEREST Recorded Jul 20, 2022
From: KOFAX, INC.; PSIGEN SOFTWARE, INC.
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT
Reel/Frame 060768/0159 →
SECURITY INTEREST Recorded Jul 7, 2017
From: KOFAX, INC.
To: CREDIT SUISSE
Reel/Frame 043108/0207 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 2, 2017
From: AMTRUP, JAN W.; MACCIOLA, ANTHONY; THOMPSON, STEVE; MA, JIYONG; SHUSTOROVICH, ALEXANDER; THRASHER, CHRISTOPHER W.
To: KOFAX, INC.
Reel/Frame 042675/0643 →