Object recognition system and process for identifying people and objects in an image of a scene
First Claim
1. A computer-implemented process for identifying a person or object in an image of a scene, said process comprising using a computer to perform the following process actions:
- creating model histograms of people and objects that it is desired to identify in said image of the scene;
segmenting said image to extract regions which correspond to at least one person or object whose identity it is desired to determine;
for each region extracted from the image, computing a histogram for the extracted region, and respectively producing an indicator of the degree of similarity between the extracted region histogram and each of said model histograms;
forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram;
computing a combined degree of similarity value for each of said indicator combinations;
identifying the largest combined degree of similarity value; and
designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators.
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Abstract
An object recognition system and process that identifies people and objects depicted in an image of a scene. In general, this system and process entails first creating model histograms of the people and objects that it is desired to identify in the image. Then, the image is segmented to extract regions which likely correspond to the people and objects being identified. A histogram is computed for each of the extracted regions, and the degree of similarity between each extracted region histogram and each of the model histograms is assessed. The extracted regions having a histogram that exhibits a degree of similarity to one of the model histograms which exceeds a prescribed threshold is designated as corresponding to the person or object associated with that model histogram. Finally, the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram can be stored as an additional model histogram associated with that person or object. Preferably, the foregoing general system and process is repeated for subsequently generated images of the scene, so that the identity of people and objects can be monitored over time as they move into and about the scene. In addition, preferably color images of the scene and color histograms are employed in the object recognition system and process.
80 Citations
57 Claims
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1. A computer-implemented process for identifying a person or object in an image of a scene, said process comprising using a computer to perform the following process actions:
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creating model histograms of people and objects that it is desired to identify in said image of the scene;
segmenting said image to extract regions which correspond to at least one person or object whose identity it is desired to determine;
for each region extracted from the image, computing a histogram for the extracted region, and respectively producing an indicator of the degree of similarity between the extracted region histogram and each of said model histograms;
forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram;
computing a combined degree of similarity value for each of said indicator combinations;
identifying the largest combined degree of similarity value; and
designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented process for identifying a person or object in an image of a scene, said process comprising using a computer to perform the following process actions:
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creating model histograms of people and objects that it is desired to identify in said image of the scene;
dividing the image into a plurality of cells;
assigning each model histogram to one of the image cells;
segmenting said image to extract regions which correspond to at least one person or object whose identity it is desired to determine;
for each region extracted from the image, computing a histogram for the extracted region, determining the centroid of the extracted region and identifying the cell in which it resides, for each of a set of one or more model histograms associated with the same person or object, ascertaining the closest image cell to the identified cell, including the identified cell itself, that has a histogram associated with that person or object assigned thereto, respectively assessing the degree of similarity between the histogram computed for the extracted region and each of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region, determining whether the extracted region'"'"'s histogram exhibits a degree of similarity to one of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region which exceeds a prescribed threshold, and whenever the extracted region'"'"'s histogram exhibits a degree of similarity to one of said previously ascertained model histograms which exceeds the prescribed threshold, designating the extracted region as corresponding to the person or object associated with that model histogram. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. An object recognition system for identifying a person or object in an image of a scene, comprising:
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a general purpose computing device;
a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to, (a) create model histograms of people and objects that it is desired to identify in said image of the scene, (b) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine, (c) compute a histogram for each of region extracted from the image, (d) respectively producing an indicator of the degree of similarity between each extracted region histogram and each of said model histograms, (e) forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram, (f) computing a combined degree of similarity value for each of said indicator combinations, (g) identifying the largest combined degree of similarity value, and (h) designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29)
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30. An object recognition system for identifying a person or object in an image of a scene, comprising:
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a general purpose computing device;
a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to, (a) create model histograms of people and objects that it is desired to identify in said image of the scene, (b) divide the image into a plurality of cells, (c) assign each model histogram to one of the image cells, (d) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine, (e) for each region extracted from the image, compute a histogram for the extracted region, determine the centroid of the extracted region and identifying the cell in which it resides, for each of a set of one or more model histograms associated with the same person or object, ascertain the closest image cell to the identified cell, including the identified cell itself, that has a histogram associated with that person or object assigned thereto, respectively assess the degree of similarity between the histogram computed for the extracted region and each of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region, determine whether the extracted region'"'"'s histogram exhibits a degree of similarity to one of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region which exceeds a prescribed threshold, and whenever the extracted region'"'"'s histogram exhibits a degree of similarity to one of said previously ascertained model histograms which exceeds the prescribed threshold, designate the extracted region as corresponding to the person or object associated with that model histogram. - View Dependent Claims (31, 32, 33, 34, 35, 36, 37, 38)
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39. A computer-readable memory for causing a computer to perform an object recognition procedure for identifying a person or object in an image of a scene, comprising:
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a general purpose computing device;
a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to, (a) create model histograms of people and objects that it is desired to identify in said image of the scene, (b) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine, (c) compute a histogram for each of region extracted from the image, (d) respectively producing an indicator of the degree of similarity between each extracted region histogram and each of said model histograms, (e) forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram, (f) computing a combined degree of similarity value for each of said indicator combinations, (g) identifying the largest combined degree of similarity value, and (h) designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators. - View Dependent Claims (40, 41, 42, 43, 44, 45, 46, 47, 48)
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49. A computer-readable memory for causing a computer to perform an object recognition procedure for identifying a person or object in an image of a scene, comprising:
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a general purpose computing device;
a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to, (a) create model histograms of people and objects that it is desired to identify in said image of the scene, (b) divide the image into a plurality of cells, (c) assign each model histogram to one of the image cells, (d) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine, (e) for each region extracted from the image, compute a histogram for the extracted region, determine the centroid of the extracted region and identifying the cell in which it resides, for each of a set of one or more model histograms associated with the same person or object, ascertain the closest image cell to the identified cell, including the identified cell itself, that has a histogram associated with that person or object assigned thereto, respectively assess the degree of similarity between the histogram computed for the extracted region and each of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region, determine whether the extracted region'"'"'s histogram exhibits a degree of similarity to one of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region which exceeds a prescribed threshold, and whenever the extracted region'"'"'s histogram exhibits a degree of similarity to one of said previously ascertained model histograms which exceeds the prescribed threshold, designate the extracted region as corresponding to the person or object associated with that model histogram. - View Dependent Claims (50, 51, 52, 53, 54, 55, 56, 57)
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Specification