Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Model-based recognition in robot vision
ACM Computing Surveys (CSUR)
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional, model-based, boundary matching using footprints
International Journal of Robotics Research
Computing the Aspect Graph for Line Drawings of Polyhedral Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Parallel Models of Associative Memory
Parallel Models of Associative Memory
Parameter networks: towards a theory of low-level vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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This paper presents an efficient and homogeneous paradigm for automatic acquisition and recognition of nonparametric shapes. Acquisition time varies from linear to cubic in the number of object features. Recognition time is linear to cubic in the number of features in the image and grows slowly with the number of stored models. Nonparametric shape representation is achieved by spatial autocorrelation transforms. Both acquisition and recognition are two-step processes. In the first phase, spatialautocorrelationoperators are applied to the image data to perform local shape analysis. Then, spatial autocorrelation operators are applied to the local shape descriptors to either create entries (acquisition) or index (recognition) into a table containing the distributed shape information. The output of the table is used to generate a density function on the space of possible shapes with peaks corresponding to high confidence in the presence of a particular shape instance. The behavior of the system on a set of complex shapes is shown with respect to occlusion, geometric transformation, and cluttered scenes.