METHOD AND APPARATUS FOR DETERMINING A CLASSIFICATION BOUNDARY FOR AN OBJECT CLASSIFIER
First Claim
1. A method for determining a classification boundary between an object and a background, comprising:
- recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;
classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;
determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;
calculating a confidence probability distribution function for an object in the plurality of object images, wherein the confidence probability distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;
calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and
determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for the object in the plurality of object image and the background in the plurality of background images.
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Abstract
The invention relates to a method and apparatus for determining a classification boundary between an object, such as a vehicle, and a background, using an object classifier. In an embodiment of the invention, a trained classifier is configured to classify and recognize each a plurality of object images and a plurality of background images. Next, a confidence probability density distribution function is calculated for the vehicle and the background using the determined confidence values for the vehicle images and background images. Once the probability density distribution functions for the vehicle and the background are calculated, the classification boundary between the vehicle and the background is determined using the probability density distribution functions for the vehicle or the background, or both, and a predefined model that is appropriate for the application.
16 Citations
20 Claims
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1. A method for determining a classification boundary between an object and a background, comprising:
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recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images; classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images; determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images; calculating a confidence probability distribution function for an object in the plurality of object images, wherein the confidence probability distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images; calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for the object in the plurality of object image and the background in the plurality of background images. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. An apparatus for determining a classification boundary between an object and a background, comprising:
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a recognizing unit, configured to classify and recognize each of a plurality of object images and background images, using a trained classifier, in order to determine a confidence value for each of the plurality of object images and background images; a calculating unit, configured to calculate a confidence probability density distribution functions for (1) an object in the object images and (2) a background in the background images, wherein the calculation of the confidence probability density distribution function for the object in the object images is based on the confidence value determined for each object image, and the confidence probability density distribution function for the background in the background images is based on the confidence values determined for each background in the background images; and a determining unit, configured to determine a classification boundary between the object in the object images and the background in the background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the object images or the background in the background images, or the calculated confidence probability density distribution functions for both the object in the object images and the background in the background images. - View Dependent Claims (15, 16, 17, 18, 19)
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20. A computer readable medium comprising computer executable instructions adapted to perform the steps of:
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recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images; classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images; determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images; calculating a confidence probability distribution function for an object in the plurality of object images, wherein the confidence probability distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images; calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for the object in the plurality of object image and the background in the plurality of background images.
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Specification