Detection, recognition and coding of complex objects using probabilistic eigenspace analysis
First Claim
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1. A method for detecting selected features in digitally represented input images, the method comprising the steps of:
- a. representing a training set of instances of the selected feature as a set of eigenvectors in a multidimensional image space;
b. representing portions of the input image as input vectors in the image space;
c. performing a density-estimation analysis on the input vectors to estimate, for each input vector, a probability level indicative of the likelihood that the input vector corresponds to an image portion containing an instance of the selected feature, wherein said density estimation analysis is based on all vector components; and
d. identifying image portions having the highest associated probability levels.
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Abstract
Methods and apparatus for detecting instances of a selected object or object feature in a digitally represented scene utilize analysis of probability densities to determine whether an input image (or portion thereof) represents such an instance. The invention filters images of objects that, although in some ways similar to the object under study, fail to qualify as typical instances of that object. The invention is useful in the detection and recognition of virtually any multifeatured entity such as human faces, features thereof (e.g., eyes), as well as non-rigid and articulated objects such as human hands.
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Citations
20 Claims
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1. A method for detecting selected features in digitally represented input images, the method comprising the steps of:
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a. representing a training set of instances of the selected feature as a set of eigenvectors in a multidimensional image space; b. representing portions of the input image as input vectors in the image space; c. performing a density-estimation analysis on the input vectors to estimate, for each input vector, a probability level indicative of the likelihood that the input vector corresponds to an image portion containing an instance of the selected feature, wherein said density estimation analysis is based on all vector components; and d. identifying image portions having the highest associated probability levels. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus for detecting selected features in digitally represented input images comprising:
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a. a computer memory for storing the input images; b. means for representing a training set of instances of the selected feature as a set of eigenvectors in a multidimensional image space; c. means for isolating at least a portion of the stored input image and representing it as an input vector in the image space; and d. image processing means for performing a density-estimation analysis and analyzing an input vector to estimate, for each input vector, a probability level indicative of the likelihood that the input vector corresponds to an image portion containing an instance of the selected feature, wherein said density estimation analysis is based on all vector components. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification