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Journals ACM Computing Surveys (CSUR) Volume 25 Issue 1

Model-Based Object Images ” A Review FARSHID Siemens Recognition in Dense-Range ARMAN Research, 755 College Road East, Princetonj NJ 08540 Corporate J. K. AGGARVVAL Computer Texas and Vision Austin, Research Center, Department of Electrical and Computer Engineering, University of at Austin, TX 78712 The goal in computer vision systems is to analyze data collected from the environment and derive an interpretation to complete a specified task. Vision system tasks may be divided into data acquisition, low-level processing, representation, model construction, and matching subtasks. This paper presents a comprehensive survey of model-based vision systems using dense-range images. A comprehensive survey of the recent publications in each subtask pertaining to dense-range image object recognition is presented. Intelligence]: Robotics ”sensors: Categories and Subject Descriptors: 1.2.9 [Artificial 1.2.10 [Artificial Intelligence]: Vision and Scene Understanding ”architecture and control strategies; modeling data and recouery and ofph-vsical attributes; surface, perceptual solid, and reasoning: Graphics]: object representations, representations; pixel classification; structures, transforms; 1.4.6 [Image partitioning; Processing]: 1.5.2 [Pattern shape; 1.3.5 [Computer Computational Geometry and Object Modeling ”curue, modeling region data; packages; growing; 1.4.9 [Image Processing]: 1.4.8 [Image Segmentation ” Processing]: Scene Analysis ”range Recognition]: Models ”geometric; Applications; 1.5.1 [Pattern Recognition]: Design 1.5.3 [Pattern Recognition]: Applications]: design

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Model-based object recognition in dense-range images—a review

Arman, Farshid; Aggarwal, J. K.
ACM Computing Surveys (CSUR) , Volume 25 (1)
Association for Computing Machinery – Mar 1, 1993

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