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Journal ArticleDOI

Model-based recognition in robot vision

Roland T. Chin, +1 more
- 01 Mar 1986 - 
- Vol. 18, Iss: 1, pp 67-108
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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.

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Citations
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Journal ArticleDOI

A method for registration of 3-D shapes

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

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

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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Proceedings Article

Recognition of Occluded Objects.

Bir Bhanu
Proceedings ArticleDOI

Geometric Modeling In Vision For Manufacturing

TL;DR: A geometric modeling and geometric reasoning system which is being developped to model real world industrial parts and to carry out examples of programmable inspection and picking parts from bins is described.
Proceedings ArticleDOI

Overview Of Applications Of Image Understanding To Industrial Automation

TL;DR: An overview of possible applications of image-understanding research to industrial automation problems is presented and a selection of IU techniques and their potential for increasing the competence of IA systems are briefly described.
Book ChapterDOI

Evaluating Vision System Performance

C. A. Rosen, +1 more
TL;DR: In this article, a simple method is proposed for comparing the relative performance of machine vision systems designed for programmable automation, and some major performance criteria are the degree of discrimination between patterns and the execution time required to determine the identity, the state, and the position of workpieces.
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