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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Journal ArticleDOI
An iterative hough procedure for three-dimensional object recognition
TL;DR: An iterative Hough procedure for recognizing images of three-dimensional objects by finding the parameters of a viewing transformation of a three- dimensional model consisting of line segments is described.
Journal ArticleDOI
Syntactic characterization of machine parts shapes
TL;DR: A general purpose parts description language seems to be particularly adequate for the automatic processing of the CAD/CAM system and is associated not only with individual parts but also with their classes.
Proceedings ArticleDOI
Robust Feature Matching Through Maximal Cliques
TL;DR: A technique that uses the relative positions and orientations of the features to determine the correspondence between features of an object model and features observed in a picture and is a robust, general-purpose way to match structures.
Picking up an Object from a Pile of Objects.
TL;DR: A hand-eye system developed to perform the bin-picking task with the photometric stereo method and the extended Gaussian image, which can successfully pick an object from a pile based on the information from the vision part.