Describing objects using edge-pixel-feature descriptors

1. A computer-system-implemented method for generating at least one descriptor for an object, the method comprising:

receiving extracted line segments aligned with edge pixels associated with the object in an image;

determining orientations for the line segments;

identifying a subset of the line segments, wherein the subset includes an integer number k of the line segments that are proximate to a given line segment in the line segments;

using the computer system, calculating pairs of angles representing relative displacement and relative orientation of pairs of line segments in the subset, wherein a given pair of angles for a given pair of line segments in the subset includes: a first angle at a location on a first line segment in the given pair of line segments between the first line segment and a line t connected between the first line segment and a second line segment in the given pair of line segments, and a second angle at a location on the second line segment between the second line segment and the line; and

generating the at least one descriptor for the object by computing a 2-dimensional (2D) histogram of the pairs of angles based on cells defined by angular quantization values.

2. The method of claim 1 , wherein the method further comprises:

receiving the image that includes the object; and

extracting the line segments aligned with the edge pixels associated with the object.

3. The method of claim 1 , wherein receiving the line segments includes accessing the line segments in a computer-readable memory.

4. The method of claim 1 , wherein at least some of the line segments are not coupled to a remainder of the extracted line segments.

5. The method of claim 1 , wherein the orientations are determined based on contrast polarity of the line segments.

6. The method of claim 1 , wherein an orientation of the given line segment includes a direction aligned with the given line segment, and a sign computed by taking a cross product of a gradient at the location on the given line segment and the direction.

7. The method of claim 1 , wherein the k line segments in the subset are nearest neighbors to the given line segment.

8. The method of claim 1 , wherein, prior to determining the orientations, the method further comprises filtering the line segments based on lengths of the line segments and a threshold value.

9. The method of claim 1 , wherein the location on the first line segment and the location on the second line segment include one of: midpoints of the first line segment and the second line segment; end points of the first line segment and the second line segment; and/or points on the first line segment and the second line segment separated by a minimum distance between the first line segment and the second line segment.

10. The method of claim 1 , wherein calculating the given pair of angles includes an operation other than calculating a dot product between the first line segment and the second line segment.

11. The method of claim 1 , wherein calculating the first angle includes rotating the first line segment clockwise over a connecting line segment that couples the first line segment and the second line segment; and

wherein calculating the second angle includes rotating the second line segment clockwise over the connecting line segment.

12. The method of claim 1 , wherein, for the given pair of angles, the 2D histogram of the pairs of angles includes weights in neighboring, adjacent cells to a cell that includes the given pair of angles;

wherein the weights are determined using bilinear interpolation; and

wherein a sum of the weights equals a constant.

13. The method of claim 1 , wherein the method further comprises storing the 2D histogram of the pairs of angles in a computer-readable memory that includes a set of descriptors of objects; and

wherein, for a given object, the set of descriptors includes at least one descriptor.

14. The method of claim 1 , wherein the method further comprises generating a set of descriptors for the object by repeating the operations in the method for multiple subsets of the line segments.

15. The method of claim 1 , wherein the method further comprises generating a set of descriptors for the object by repeating the operations in the method for multiple images of the object from different vantage points.

16. The method of claim 1 , wherein the method further comprises generating a set of descriptors for the object by repeating the operations in the method for different values of k.

17. The method of claim 16 , wherein the method further comprises:

comparing the set of descriptors to a predefined set of descriptors associated with a second object, and

identifying the object as the second object based on a group of matches in the comparisons.

18. The method of claim 17 , wherein the object is identified as the second object without using lengths of the line segments in the comparison.

19. The method of claim 1 , wherein the method further comprises associating at least a portion of the image with an object category based on an object classifier.

20. The method of claim 1 , wherein the method further comprises:

comparing the descriptor to predefined sets of descriptors that are associated with a set of objects; and

identifying the object as one of the set of objects based on a group of match scores determined in the comparisons.

21. The method of claim 20 , wherein the comparing includes:

determining match scores between the descriptor and the predefined sets of descriptors based on Euclidean distances between the descriptor and the predefined sets of descriptors; and

selecting a subset of matches based on the match scores using one of: a Generalized Hough Transformation and robust homography estimation.