Methods and systems for recognizing objects based on one or more stored training images
First Claim
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1. An object recognition system, comprising of:
- a processor;
a non-transitory storage element coupled to the processor;
encoded instructions stored in the non-transitory storage element,wherein the encoded instructions when implemented by the processor, configure the object recognition system to;
generate a signature for an input image of an object by an image signature generation unit, wherein the image signature generation unit is further comprising of;
a feature detection unit configured to detect one or more feature points in the input image; and
a feature description unit configured to compute a description for each feature point of the one or more the feature points, wherein the feature description unit is further configured to;
identify a dominant gradient direction in a region around the feature point, wherein an angle of the dominant gradient direction is α
;
center a patch around the feature point, wherein the patch is tilted at the angle α
;
divide the patch in R segments;
compute a vector of length N for each segment of the R segments, wherein the vector is computed based on a horizontal gradient (dx) and a vertical gradient (dy) corresponding to each pixel in the segment;
compute a consolidated vector of length R*N by consolidating vectors computed for all the R segments; and
compute a byte vector of length R*N, wherein the byte vector is computed by normalizing the consolidated vector, wherein the byte vector is the description of the feature point;
whereby, the signature of the input image comprises the description corresponding to each of the one or more feature points in the input image;
store the set of training images in a data storage, wherein each training image of the set of training images is associated with one or more training feature descriptors, the data storage further comprising;
an index mapping unit configured to create an index mapping based on training feature descriptors; and
identify a matching image of the set of training images by a search engine comparing the signature of the input image with the training feature descriptors using the index mapping.
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Abstract
The present invention discloses methods and systems for recognizing an object in an input image based on stored training images. An object recognition system the input image, computes a signature of the input image, compares the signature with one or more stored signatures and retrieves one or more matching images from the set of training images. The matching images are then displayed to the user for further action.
36 Citations
19 Claims
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1. An object recognition system, comprising of:
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a processor; a non-transitory storage element coupled to the processor; encoded instructions stored in the non-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the object recognition system to; generate a signature for an input image of an object by an image signature generation unit, wherein the image signature generation unit is further comprising of; a feature detection unit configured to detect one or more feature points in the input image; and a feature description unit configured to compute a description for each feature point of the one or more the feature points, wherein the feature description unit is further configured to; identify a dominant gradient direction in a region around the feature point, wherein an angle of the dominant gradient direction is α
;center a patch around the feature point, wherein the patch is tilted at the angle α
;divide the patch in R segments; compute a vector of length N for each segment of the R segments, wherein the vector is computed based on a horizontal gradient (dx) and a vertical gradient (dy) corresponding to each pixel in the segment; compute a consolidated vector of length R*N by consolidating vectors computed for all the R segments; and compute a byte vector of length R*N, wherein the byte vector is computed by normalizing the consolidated vector, wherein the byte vector is the description of the feature point; whereby, the signature of the input image comprises the description corresponding to each of the one or more feature points in the input image; store the set of training images in a data storage, wherein each training image of the set of training images is associated with one or more training feature descriptors, the data storage further comprising; an index mapping unit configured to create an index mapping based on training feature descriptors; and identify a matching image of the set of training images by a search engine comparing the signature of the input image with the training feature descriptors using the index mapping. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for recognizing an object in one or more input images based on one or more training images stored in a data storage, the method comprising;
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generating a signature for an input image of the object comprising; detecting one or more feature points in the input image; and computing the description for each feature point of the one or more feature points, comprising; identifying a dominant gradient direction in a region around a feature point, wherein an angle of the dominant gradient direction is α
;centering a patch around the feature point, wherein the patch is tilted at the angle α
;dividing the patch in R segments; computing a vector of length N for each segment of the R segments, wherein the vector is computed based on at least a horizontal gradient (dx) and at least a vertical gradient (dy) corresponding to each pixel in the segment computing a consolidated vector of length R*N by consolidating vectors computed for all the R segments; and computing a byte vector of length R*N, wherein the byte vector is computed by normalizing the consolidated vector, wherein the byte vector is the description of the feature point; whereby, the signature of the input image comprises the description corresponding to each of the one or more feature points in the input image; and identifying a matching image of the set of training images by comparing the signature of the input image with training feature descriptors of the set of training images using an index mapping. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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Specification