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

• US 9,355,312 B2
• Filed: 03/13/2013
• Issued: 05/31/2016
• Est. Priority Date: 03/13/2013
• Status: Active Grant

• Current Assignee: Kofax Incorporated
• Original Assignee: Kofax Incorporated
• Inventors: Amtrup, Jan Willers, Macciola, Anthony, Thompson, Steve, Ma, Jiyong, Shustorovich, Alexander, Thrasher, Christopher W.
• Primary Examiner(s): SHAH, UTPAL D
• Application Number:
  US13/802,226
• Publication Number:
  US 20140270349A1
• Time in Patent Office: 1,175 Days
• Field of Search: 382224-225
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06V 10/247 by affine transforms, e.g. correction due to perspective effects; Quadrilaterals, e.g. trapezoids
• CPC Class Codes: G06V 20/62 Text, e.g. of license plates, overlay texts or captions on TV images
• CPC Class Codes: G06V 30/142 using hand-held instruments; Constructional details of the instruments
• CPC Class Codes: G06V 30/224 of printed characters having additional code marks or containing code marks
• CPC Class Codes: G06V 30/40 Document-oriented image-based pattern recognition
• CPC Class Codes: G06V 30/412 Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables
• CPC Class Codes: G06V 30/413 Classification of content, e.g. text, photographs or tables
• CPC Class Codes: G06V 30/416 Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors

Abstract

In one embodiment, a method includes receiving a digital image captured by a mobile device; and using a processor of the mobile device: generating a first representation of the digital image, the first representation being characterized by a reduced resolution; generating a first feature vector based on the first representation; comparing the first feature vector to a plurality of reference feature matrices; and classifying an object depicted in the digital image as a member of a particular object class based at least in part on the comparing.

Claims

1. A method, comprising:

receiving a digital image captured by a mobile device; and

using a processor of the mobile device;

generating a first representation of the digital image, the first representation being characterized by a reduced resolution;

generating a first feature vector based on the first representation;

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

classifying an object depicted in the digital image as a member of a particular object class based at least in part on the comparing; and

binarizing the digital image based at least in part on one or more of the classifying and a result thereof;

wherein the binarizing comprises;

determining an object class color scheme;

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

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

2. The method as recited in claim 1, 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.

3. The method as recited in claim 1, further comprising defining 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 method as recited in claim 1, wherein the first representation is a color image.

5. The method as recited in claim 1, wherein one or more of the reference feature matrices comprises a plurality of feature vectors, and

wherein each feature vector of the reference feature matrices corresponds to at least one characteristic of an object.

6. The method as recited in claim 1, wherein the comparing comprises an N-dimensional comparison, and

wherein N is greater than 50.

7. The method as recited in claim 1, wherein the first feature vector is characterized by a feature vector length greater than 500.

8. The method as recited in claim 1, further comprising:

determining one or more object features of the object based at least in part on the particular object class.

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

10. The method as recited in claim 1, further comprising predicting an identity of text depicted on the object using optical character recognition (OCR), wherein the predicting is based at least in part on the particular object class.

11. The method as recited in claim 10, further comprising modifying 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.

12. The method as recited in claim 1, further comprising one or more of:

detecting the object depicted in the digital image based at least in part on one or more of the classifying and a result thereof;

cropping the digital image based at least in part on one or more of the classifying and the result thereof;

rectangularizing the object depicted in the digital image based at least in part on one or more of the classifying and the result thereof.

13. The method as recited in claim 1, wherein the binarizing comprises:

determining an 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 method as recited in claim 13, wherein the binarizing further comprises:

adjusting one or more binarization parameters based on the object class mask; and

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

15. The method as recited in claim 1, further comprising:

determining a geographical location associated with the mobile device,

wherein the classifying is further based at least in part on the geographical location.

16. The method as recited in claim 1, further comprising:

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

receiving 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.

17. The method as recited in claim 1, further comprising:

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

detecting 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.

18. A method, comprising:

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

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

classifying an object depicted in the digital image as a member of a particular object class based at least in part on the comparing; and

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

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

detecting 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.

19. The method as recited in claim 18, wherein the one or more object features comprise an object color scheme, and

wherein the method comprises binarizing 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.

20. The method as recited in claim 18, wherein the one or more object features comprise an object class mask, and

wherein the method comprises binarizing 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.

21. The method as recited in claim 20, wherein the one or more object features further comprise an object color scheme, and

wherein the binarizing 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.

22. A system, comprising:

a processor; and

logic in and/or executable by the processor to cause the processor to;

generate a first representation of a digital image captured by a mobile device;

generate a first feature vector based on the first representation;

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.

23. A computer program product comprising:

a non-transitory computer readable storage medium having program code embodied therewith, the program code readable/executable by a processor to;

generate a first representation of a digital image captured by a mobile device;

generate a first feature vector based on the first representation;

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;

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

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