Creating a model tree using group tokens for identifying objects in an image
First Claim
1. A computer-implemented method for identifying objects in images, wherein the method is performed by a processor, comprising:
- computing one or more interest points in each of a plurality of training images including one or more objects, wherein each interest point represents one pixel, and wherein the one or more interest points in a training image represent a subset of the pixels of the training image;
extracting tokens associated with the interest points, wherein a token associated with an interest point comprises an image feature of an image region surrounding the interest point;
comparing tokens associated with an interest point in a first training image with tokens associated with an interest point in a second training image to find matched tokens, wherein a matched token comprises a first token in the first training image and a second token in the second training image, and wherein the first token is related to the second token;
grouping the matched tokens into sets, wherein a set comprises related matched tokens;
computing a group token to represent each set of matched tokens; and
creating a model tree using the group tokens, where each node of the tree represents an object model for identifying objects in images.
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Abstract
Object recognition techniques are disclosed that provide both accuracy and speed. One embodiment of the present invention is an identification system. The system is capable of locating objects in images by searching for local features of an object. The system can operate in real-time. The system is trained from a set of images of an object or objects. The system computes interest points in the training images, and then extracts local image features (tokens) around these interest points. The set of tokens from the training images is then used to build a hierarchical model structure. During identification/detection, the system, computes interest points from incoming target images. The system matches tokens around these interest points with the tokens in the hierarchical model. Each successfully matched image token votes for an object hypothesis at a certain scale, location, and orientation in the target image. Object hypotheses that receive insufficient votes are rejected.
16 Citations
20 Claims
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1. A computer-implemented method for identifying objects in images, wherein the method is performed by a processor, comprising:
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computing one or more interest points in each of a plurality of training images including one or more objects, wherein each interest point represents one pixel, and wherein the one or more interest points in a training image represent a subset of the pixels of the training image; extracting tokens associated with the interest points, wherein a token associated with an interest point comprises an image feature of an image region surrounding the interest point; comparing tokens associated with an interest point in a first training image with tokens associated with an interest point in a second training image to find matched tokens, wherein a matched token comprises a first token in the first training image and a second token in the second training image, and wherein the first token is related to the second token; grouping the matched tokens into sets, wherein a set comprises related matched tokens; computing a group token to represent each set of matched tokens; and creating a model tree using the group tokens, where each node of the tree represents an object model for identifying objects in images. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A machine-readable medium encoded with instructions, that when executed by a processor, cause the processor to carry out a process for identifying objects in images, comprising:
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computing one or more interest points in each of a plurality of training images including one or more objects, wherein each interest point represents one pixel, and wherein the one or more interest points in a training image represent a subset of the pixels of the training image; extracting tokens associated with the interest points, wherein a token associated with an interest point comprises an image feature of an image region surrounding the interest point; comparing tokens associated with an interest point in a first training image with tokens associated with an interest point in a second training image to find matched tokens, wherein a matched token comprises a first token in the first training image and a second token in the second training image, and wherein the first token is related to the second token; grouping the matched tokens into sets, wherein a set comprises related matched tokens; computing a group token to represent each set of matched tokens; and creating a model tree using the group tokens, where each node of the tree represents an object model for identifying objects in images. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A hardware system for identifying objects in images, comprising:
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an interest point locator module for computing one or more interest points in each of a plurality of training images including one or more objects, wherein each interest point represents one pixel, and wherein the one or more interest points in a training image represent a subset of the pixels of the training image; a token extraction module for extracting tokens associated with the interest points, wherein a token associated with an interest point comprises an image feature of an image region surrounding the interest point; a token grouping module for comparing tokens associated with an interest point in a first training image with tokens associated with an interest point in a second training image to find matched tokens, wherein a matched token comprises a first token in the first training image and a second token in the second training image, and wherein the first token is related to the second token, for grouping the matched tokens into sets, wherein a set comprises related matched tokens, and for computing a group token to represent each set of matched tokens; and a model tree creator module for creating a model tree using the group tokens, where each node of the tree represents an object model for identifying objects in images. - View Dependent Claims (16, 17, 18)
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19. A hardware system for identifying objects in images, comprising:
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a means for computing one or more interest points in each of a plurality of training images including one or more objects, wherein each interest point represents one pixel, and wherein the one or more interest points in a training image represent a subset of the pixels of the training image; a means for extracting tokens associated with the interest points, wherein a token associated with an interest point comprises an image feature of an image region surrounding the interest point; a means for comparing tokens associated with an interest point in a first training image with tokens associate with an interest point in a second training image to find matched tokens, wherein a matched token comprises a first token in the first training image and a second token in the second training image, and wherein the first token is related to the second token, for grouping the matched tokens into sets, wherein a set comprises related matched tokens, and for computing a group token to represent each set of matched tokens; and a means for creating a model tree using the group tokens, where each node of the tree represents an object model for identifying objects in images. - View Dependent Claims (20)
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Specification