Method and apparatus for three-dimensional shape estimation using constrained disparity propagation
First Claim
1. A method for three-dimensional shape estimation using constrained disparity propagation, the method comprising an act of causing a processor to perform operations of extracting image features, wherein the operations of extracting image features comprises the acts of:
- receiving a stereoscopic pair of images of an area occupied by at least one object;
detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter;
generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images;
using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints;
iteratively using the subsequent estimate as the initial estimate in the act of using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities;
processing the final estimate to provide signals, wherein the signals comprise enable and disable signals of a vehicle;
generating a disparity map of the area occupied by at least one object from the final estimate of the three-dimensional shape; and
processing the disparity map with at least one classification algorithm to produce object class confidence data, said classification algorithm is selected from the group consisting of a trained C5 decision tree, a trained Nonlinear Discriminant Analysis network, and a trained Fuzzy Aggregation Network.
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Abstract
A method, an apparatus, and a computer program product for three-dimensional shape estimation using constrained disparity propagation are presented. An act of receiving a stereoscopic pair of images of an area occupied by at least one object is performed. Next, pattern regions and non-pattern regions are detected in the images. An initial estimate of śpatial disparities between the pattern regions in the images is generated. The initial estimate is used to generate a subsequent estimate of the spatial disparities between the non-pattern regions. The subsequent estimate is used to generate further subsequent estimates of the spatial disparities using the disparity constraints until there is no change between the results of subsequent iterations, generating a final estimate of the spatial disparities. A disparity map of the area occupied by at least one object is generated from the final estimate of the three-dimensional shape.
41 Citations
20 Claims
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1. A method for three-dimensional shape estimation using constrained disparity propagation, the method comprising an act of causing a processor to perform operations of extracting image features, wherein the operations of extracting image features comprises the acts of:
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receiving a stereoscopic pair of images of an area occupied by at least one object; detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter; generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images; using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints; iteratively using the subsequent estimate as the initial estimate in the act of using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities; processing the final estimate to provide signals, wherein the signals comprise enable and disable signals of a vehicle; generating a disparity map of the area occupied by at least one object from the final estimate of the three-dimensional shape; and processing the disparity map with at least one classification algorithm to produce object class confidence data, said classification algorithm is selected from the group consisting of a trained C5 decision tree, a trained Nonlinear Discriminant Analysis network, and a trained Fuzzy Aggregation Network. - View Dependent Claims (2, 3, 4, 5)
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6. An apparatus for object detection comprising a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving images, and an output coupled with the processor for outputting information based on an object estimation, wherein the computer system further comprises means for:
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receiving a stereoscopic pair of images of an area occupied by at least one object; detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter; generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images; using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints; iteratively using the subsequent estimate as the initial estimate in the means for using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities; processing the final estimate to provide signals, wherein the signals comprise enable and disable signals of a vehicle; generating a disparity map of the area occupied by at least one object from the final estimate of the three-dimensional shape; and processing the disparity map with at least one classification algorithm to produce object class confidence data, said classification algorithm is selected from the group consisting of a trained C5 decision tree, a trained Nonlinear Discriminant Analysis network, and a trained Fuzzy Aggregation Network. - View Dependent Claims (7, 8, 9, 10)
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11. An apparatus for object detection comprising a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving images, and an output coupled with the processor for outputting information based on an object estimation, wherein the computer system further comprises:
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a receiving module for receiving a stereoscopic pair of images of an area occupied by at least one object; a pattern region detector for detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter; an estimator for generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images; a subsequent estimator using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints; an iterator for iteratively using the subsequent estimate as the initial estimate in the subsequent estimator to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities; an estimate processor for processing the final estimate to provide signals, wherein the signals comprise enable and disable signals of a vehicle; a map generator for generating a disparity map of the area occupied by at least one object from the final estimate of the three-dimensional shape; and an object class confidence data generator for processing the disparity map with at least one classification algorithm to produce object class confidence data, said classification algorithm is selected from the group consisting of a trained C5 decision tree, a trained Nonlinear Discriminant Analysis network, and a trained Fuzzy Aggregation Network. - View Dependent Claims (12, 13, 14, 15)
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16. A computer program product for object detection encoded on a computer-readable medium, having encoded therein, means for:
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receiving a stereoscopic pair of images of an area occupied by at least one object; detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter; generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images; using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints; iteratively using the subsequent estimate as the initial estimate in the means for using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities; processing the final estimate to provide signals, wherein the signals comprise enable and disable signals of a vehicle; generating a disparity map of the area occupied by at least one object from the final estimate of the three-dimensional shape; and processing the disparity map with at least one classification algorithm to produce object class confidence data, said classification algorithm is selected from the group consisting of a trained C5 decision tree, a trained Nonlinear Discriminant Analysis network, and a trained Fuzzy Aggregation Network. - View Dependent Claims (17, 18, 19, 20)
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Specification