Journal ArticleDOI
Model-based recognition in robot vision
Roland T. Chin,Charles R. Dyer +1 more
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TLDR
This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision, and an evaluation and comparison of existing industrial part- recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.Abstract:
This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.read more
Citations
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Journal ArticleDOI
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Journal ArticleDOI
Comparing images using the Hausdorff distance
TL;DR: Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented and it is shown that the method extends naturally to the problem of comparing a portion of a model against an image.
Journal ArticleDOI
Neural network-based face detection
TL;DR: A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Journal ArticleDOI
Alignment by Maximization of Mutual Information
Paul A. Viola,William M. Wells +1 more
TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.
Proceedings ArticleDOI
Method for registration of 3-D shapes
Paul J. Besl,Neil David Mckay +1 more
TL;DR: In this paper, the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
References
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Book ChapterDOI
Present Industrial Use of Vision Sensors for Robot Guidance
TL;DR: The practical application of vision to 100% part inspection, robot guidance for locating and taking up parts, and the application ofVision to provide servo position feedback for arc welding are discussed.
Book ChapterDOI
Image Feature Extraction
TL;DR: This chapter deals with the problem of extracting features from two-dimensional image data and three-dimensional features from range data.
Book ChapterDOI
Computer Vision in Industry
Lothar Rossol,Lothar Rossol +1 more
TL;DR: The two specific industrial vision systems to be described in this paper, KEYSIGHT and CONSIGHT, will give a general indication of the state of the technology.
Book ChapterDOI
Industrial Objects and Machine Parts Recognition
TL;DR: The grammatical-structural method proves to be the most powerful device for pattern recognition of industrial objects combined pragmatically with other ones.
Book ChapterDOI
Computer Vision Systems for Industry: Comparisons
TL;DR: In this article, the authors present a survey of automatic, computer-based pattern recognition methods in the light of opportunities offered by silicon chip technology, and present a set of techniques for pattern recognition.