Crandall et al., 2006 - Google Patents
Weakly supervised learning of part-based spatial models for visual object recognitionCrandall et al., 2006
View PDF- Document ID
- 18410620273856493536
- Author
- Crandall D
- Huttenlocher D
- Publication year
- Publication venue
- European conference on computer vision
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Snippet
In this paper we investigate a new method of learning part-based models for visual object recognition, from training data that only provides information about class membership (and not object location or configuration). This method learns both a model of local part …
- 230000000007 visual effect 0 title abstract description 4
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
- G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
- G06K9/6206—Shifting or otherwise transforming the patterns to accommodate for positional errors involving a deformation of the sample or reference pattern; Elastic matching
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