System and method for detection of multi-view/multi-pose objects
First Claim
1. A computer implemented method of training an image classifier comprising:
- segmenting a candidate exemplar into a number of grid cell image regions;
computing a gradient orientation histogram for at least one of the grid cell image regions of the candidate exemplar, where the candidate exemplar comprises at least one image of an object to be detected;
computing a gradient orientation histogram for a plurality of training samples;
calculating a distance value between the gradient orientation histogram for at least one of the grid cell image regions of the candidate exemplar and the gradient orientation histogram for the plurality of training samples; and
training the image classifier based on the distance value.
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Abstract
The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.
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Citations
19 Claims
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1. A computer implemented method of training an image classifier comprising:
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segmenting a candidate exemplar into a number of grid cell image regions; computing a gradient orientation histogram for at least one of the grid cell image regions of the candidate exemplar, where the candidate exemplar comprises at least one image of an object to be detected; computing a gradient orientation histogram for a plurality of training samples; calculating a distance value between the gradient orientation histogram for at least one of the grid cell image regions of the candidate exemplar and the gradient orientation histogram for the plurality of training samples; and training the image classifier based on the distance value. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer implemented method of training and using a plurality of weak classifiers to form an image classifier comprising:
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segmenting a candidate exemplar into a plurality of grid cell image regions, where the candidate exemplar comprises at least one image of an object to be detected; computing a gradient orientation histogram for at least one of the grid cell image regions of the candidate exemplar; computing a gradient orientation histogram for a plurality of training samples; calculating a distance value between the gradient orientation histogram for the at least one of the grid cell image regions of the candidate exemplar and the gradient orientation histogram for the plurality of training samples; and training the plurality weak classifiers based on the distance value. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. Apparatus for training and using a plurality of weak classifiers to form an image classifier comprising:
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a plurality of weak classifiers, where each weak classifier in the plurality of weak classifiers is trained using a first gradient orientation histogram representation of a candidate exemplar and a second gradient orientation histogram representing a plurality of training samples, where the candidate exemplar comprises positive samples of images of an object to be detected; and a combiner, coupled to the plurality of weak classifiers, for linearly combining the plurality of weak classifiers to form a final classifier. - View Dependent Claims (18, 19)
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Specification