Multiple centroid condensation of probability distribution clouds
First Claim
1. A method of condensing centroids from a probability distribution cloud representing a plurality of parts of an object, comprising:
- a) exemplar processing of a data sample set to assign a plurality of probabilities to each of the samples of said set, each assigned probability relating to the probability that one of said samples belongs to a respective one of said parts of the object;
b) combining the samples'"'"' positions with the assigned probabilities to produce a probability distribution cloud;
c) clustering one or more subgroups of the samples together to form one or more candidate centroids for the parts of the object, the clustering of one or more subgroups in said step c) depending on a proximity between the samples and the assigned probabilities.
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Abstract
Systems and methods are disclosed for identifying objects captured by a depth camera by condensing classified image data into centroids of probability that captured objects are correctly identified entities. Output exemplars are processed to detect spatially localized clusters of non-zero probability pixels. For each cluster, a centroid is generated, generally resulting in multiple centroids for each differentiated object. Each centroid may be assigned a confidence value, indicating the likelihood that it corresponds to a true object, based on the size and shape of the cluster, as well as the probabilities of its constituent pixels.
198 Citations
20 Claims
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1. A method of condensing centroids from a probability distribution cloud representing a plurality of parts of an object, comprising:
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a) exemplar processing of a data sample set to assign a plurality of probabilities to each of the samples of said set, each assigned probability relating to the probability that one of said samples belongs to a respective one of said parts of the object; b) combining the samples'"'"' positions with the assigned probabilities to produce a probability distribution cloud; c) clustering one or more subgroups of the samples together to form one or more candidate centroids for the parts of the object, the clustering of one or more subgroups in said step c) depending on a proximity between the samples and the assigned probabilities. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. In a system comprising a computing environment coupled to a capture device for capturing depth images of a body, a method of identifying real world objects, comprising:
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a) determining depth information for a group of pixels captured by the capture device; b) receiving exemplar classification results assigning a plurality of probabilities to said group of captured pixels, wherein each of said probabilities indicates that a group of captured pixels belongs to a respective one of said real world objects; c) assigning each of a plurality of confidence scores to each of a plurality of candidates, the candidates being pixels in the group of captured pixels or centroids formed from pixels in the group of captured pixels, the confidence scores based in part on the exemplar classification in said step b); and d) iteratively evaluating arrays of the candidates for merging together to form a next candidate set based on the candidates'"'"' proximity to each other in space and their confidence scores determined in said step c). - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. In a system comprising a computing environment coupled to a capture device for capturing depth images of a body, a method of identifying real world objects, comprising:
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a) determining a plurality of depth patches by segmenting the depth image into regions of approximately constant depth; b) assigning to each patch a list of the patches adjacent to said each patch to define a neighborhood graph; c) processing exemplars by computing an average probability score assigned by an exemplar process to pixels in each of said patches; d) identifying seed clusters as the set of said patches that have a higher score than all of their neighbors; and e) growing clusters outward from the seed clusters. - View Dependent Claims (19, 20)
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Specification