System and method for object recognition
First Claim
1. A method for recognizing model object in a first image comprising the steps of:
- (a) acquiring in electronic memory the first image of the model object;
(b) transforming the first image of the model object into a multi-level representation consistent with a recursive subdivision of a search space, said multi-level representation including at least the first image;
(c) generating at least one precomputed model of the model object for each level of discretization of the search space, said precomputed model consisting of a plurality of model points with corresponding direction vectors, said model points and direction vectors being generated by an image processing operation that returns a direction vector for at least each model point;
(d) acquiring in electronic memory a current image;
(e) transforming the current image into a multi-level representation consistent with a recursive subdivision of the search space, said multi-level representation including at least the current image;
(f) performing an image processing operation on each transformed image of the multi-level representation that returns a direction vector for a subset of model points within said current image that corresponds to the range of translations for which the at least one precomputed model should be searched;
(g) computing a match metric that uses the direction information of the at least one precomputed model and the transformed image for all possible model poses of the at least one precomputed model in the coarsest discretization level of the search space;
(h) determining those model poses whose match metric exceeds a user-selectable threshold and whose match metric is locally maximal, and generating a list of instances of the at least one precomputed model in the coarsest discretization level of the search space from said model poses and said match metrics;
(i) tracking said instances of the at least one precomputed model in the coarsest discretization level of the search space through the recursive subdivision of the search space until a finest level of discretization is reached; and
(j) providing the model pose of the instances of the model object on the finest level of discretization.
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Abstract
A system and method recognize a user-defined model object within an image. The system and method recognize the model object with occlusion when the model object to be found is only partially visible. The system and method also recognize the model object with clutter when there may be other objects in the image, even within the model object. The system and method also recognize the model object with non-linear illumination changes as well as global or local contrast reversals. The model object to be found may have been distorted, when compared to the user-defined model object, from geometric transformations of a certain class such as translations, rigid transformations by translation and rotation, arbitrary affine transformations, as well as similarity transformations by translation, rotation, and scaling.
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Citations
20 Claims
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1. A method for recognizing model object in a first image comprising the steps of:
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(a) acquiring in electronic memory the first image of the model object;
(b) transforming the first image of the model object into a multi-level representation consistent with a recursive subdivision of a search space, said multi-level representation including at least the first image;
(c) generating at least one precomputed model of the model object for each level of discretization of the search space, said precomputed model consisting of a plurality of model points with corresponding direction vectors, said model points and direction vectors being generated by an image processing operation that returns a direction vector for at least each model point;
(d) acquiring in electronic memory a current image;
(e) transforming the current image into a multi-level representation consistent with a recursive subdivision of the search space, said multi-level representation including at least the current image;
(f) performing an image processing operation on each transformed image of the multi-level representation that returns a direction vector for a subset of model points within said current image that corresponds to the range of translations for which the at least one precomputed model should be searched;
(g) computing a match metric that uses the direction information of the at least one precomputed model and the transformed image for all possible model poses of the at least one precomputed model in the coarsest discretization level of the search space;
(h) determining those model poses whose match metric exceeds a user-selectable threshold and whose match metric is locally maximal, and generating a list of instances of the at least one precomputed model in the coarsest discretization level of the search space from said model poses and said match metrics;
(i) tracking said instances of the at least one precomputed model in the coarsest discretization level of the search space through the recursive subdivision of the search space until a finest level of discretization is reached; and
(j) providing the model pose of the instances of the model object on the finest level of discretization. - View Dependent Claims (3, 5, 20)
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2. A method for recognizing a model object in a first image comprising the steps of:
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(a) acquiring in electronic memory the first image of the model object;
(b) transforming the first image of the model object into a multi-level representation consistent with a recursive subdivision of a search space, said multi-level representation including at least the first image;
(c) generating at least one precomputed model of the model object for each level of discretization of the search space, said precomputed model consisting of a plurality of model points with corresponding direction vectors, said model points and direction vectors being generated by an image processing operation that returns a direction vector for at least each model point;
(d) acquiring in electronic memory a current image;
(e) transforming the current image into a multi-level representation consistent with a recursive subdivision of the search space, said multi-level representation including at least the current image;
(f) performing an image processing operation on each transformed image of the multi-level representation that returns a direction vector for a subset of model points within said current image that corresponds to the range of translations for which the at least one precomputed model should be searched;
(g) computing a match metric that uses the direction information of the at least one precomputed model and the transformed image for all possible model poses of the at least one precomputed model in the coarsest discretization level of the search space;
(h) determining those model poses whose match metric exceeds a user-selectable threshold and whose match metric is locally maximal, and generating a list of instances of the at least one precomputed model in the coarsest discretization level of the search space from said model poses and said match metrics;
(i) tracking said instances of the at least one precomputed model in the coarsest discretization level of the search space through the recursive subdivision of the search space until a finest level of discretization is reached; and
(j) providing the model pose of the instances of the model object on the finest level of discretization;
wherein in step (c) for each level of discretization according to step (b), and for each transformation in the discretized search space at the current level of discretization according to step (b) the following steps are performed; (c1) transforming the first image of the current level of discretization by the current transformation using anti-aliasing methods;
(c2) performing feature extraction in the transformed image to generate at least one feature points; and
(c3) adding any segmented feature points along with their direction vectors to the list of instances of the at least one precomputed model. - View Dependent Claims (4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A system for recognizing model object in a first image comprising:
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(a) means for acquiring in electronic memory a first image of the model object;
(b) means for transforming the fist image of the model object into a multi-level representation consistent with a recursive subdivision of a search space, said multi-level representation including at least the first image;
(c) means for generating at least one precomputed model of the model object for each level of discretization of the search space, said precomputed model consisting of a plurality of model points with corresponding direction vectors, said model points and direction vectors being generated by an image processing operation that returns a direction vector for at least each model point;
(d) means for acquiring in electronic memory a current image;
(e) means for transforming the current image into a multi-level representation consistent with a recursive subdivision of the search space, said multi-level representation including at least the current image;
(f) means for performing an image processing operation on each transformed image of the multi-level representation that returns a direction vector for a subset of model points within said current image that corresponds to the range of translations for which the at least one precomputed model should be searched;
(g) means for computing a match metric that uses the direction information of the at least one precomputed model and the transformed image for all possible model poses of the at least one precomputed model in the coarsest discretization level of the search space;
(h) means for determining those model poses whose match metric exceeds a user-selectable threshold and whose match metric is locally maximal, and generating a list of instances of the at least one precomputed model in the coarsest discretization level of the search space from said model poses and said match metrics;
(i) means for tracking said instances of the at least one precomputed model in the coarsest discretization level of the search space through the recursive subdivision of the search space until a finest level of discretization is reached; and
(j) means for providing the model pose of the instances of the model object on the finest level of discretization.
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Specification