Computer Vision Methods And Systems To Recognize And Locate An Object Or Objects In One Or More Images
First Claim
1. A computer-implemented method for identifying an object in an image, comprising:
- segmenting a first image of an object into one or more superpixels;
extracting local descriptors from the first image, each of the descriptors having an interest point with a location;
correlating the local descriptors to the superpixels based on locations of the local descriptors and superpixels;
determining a probability for an object label for each of a set of the superpixels using the correlated local descriptors; and
assigning an object label to each of the set of the superpixels based on the probability and a smoothness factor, the smoothness factor being weighted by an angular distance between superpixels.
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Abstract
Embodiments of the present invention include systems and methods for identifying and locating an object in an image. In embodiments, an object in an image may be identified by segmenting a first image of an object into one or more superpixels; extracting local descriptors from the first image, each of the descriptors having an interest point with a location; correlating the local descriptors to the superpixels based on locations of the local descriptors and superpixels; determining a probability for an object label for each of a set of the superpixels; and assigning an object label to each of the set of the superpixels based on the probability and a smoothness factor that includes weighting in terms of one or more of spatial, colors, angular distances between superpixels. The superpixels of an image may be concatenated to predict an object label for the image and to determine the location of the image.
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Citations
20 Claims
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1. A computer-implemented method for identifying an object in an image, comprising:
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segmenting a first image of an object into one or more superpixels; extracting local descriptors from the first image, each of the descriptors having an interest point with a location; correlating the local descriptors to the superpixels based on locations of the local descriptors and superpixels; determining a probability for an object label for each of a set of the superpixels using the correlated local descriptors; and assigning an object label to each of the set of the superpixels based on the probability and a smoothness factor, the smoothness factor being weighted by an angular distance between superpixels. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method for identifying an object in an image, comprising:
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segmenting a first image of an object into one or more superpixels; extracting local descriptors from the first image, each of the descriptors having an interest point with a location; correlating the local descriptors to the superpixels based on locations of the local descriptors and superpixels; performing a first tree-based voting to determine probabilities for object label groups for each of a set of the superpixels using the correlated local descriptors, each of the object label groups including one or more object labels; predicting, based on the probabilities, an object label group for each of the set of the superpixels; and if the predicted object label group for a particular superpixel includes multiple object labels, performing a second tree-based voting to determine probabilities for the multiple object labels for the particular superpixel. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A system for identifying an object in an image, comprising:
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a local descriptor extractor for extracting local descriptors from the image, each of the descriptors having an interest point with a location; an image segmenter coupled to the local descriptor extractor and operative to segment the image into one or more superpixels; a superpixel group generator coupled to the local descriptor extractor and image segmenter and operative to correlate the local descriptors to the superpixels based on locations of the local descriptors and superpixels; a tree-based voter coupled to the superpixel group generator and operative to determine a probability for an object label for each of a set of the superpixels; and a graph-based global decision maker coupled to the tree-based voter and operative to assign an object label to each of the set of the superpixels based on the probability and a smoothness factor and weight the smoothness factor by at least an angular distance between superpixels. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification