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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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Parameterizing Arbitrary Shapes via Fourier Descriptors for Evidence-Gathering Extraction

TL;DR: A novel approach for arbitrary shape extraction which combines the analytic representation of shapes with the generality of the characterization by Fourier descriptors and extends the descriptional power of the Hough Transform beyond simple shapes, thus avoiding the use of tables.

Image-based transformation of viewpoint and scene appearance

TL;DR: This thesis addresses the problem of synthesizing images of real scenes under threedimensional transformations in viewpoint and appearance and proves that all views on the line segment between the two camera centers are uniquely determined from two uncalibrated views of a scene.
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Medical image collection indexing: Shape-based retrieval using KD-trees

TL;DR: A similarity measure that computes a non-rigid mapping between curves and uses this mapping to compare oriented shape and it is shown how KD-trees can index curves so that retrieval with the similarity measure is efficient.
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On geometric hashing and the generalized Hough transform

TL;DR: The generalized Hough transform and geometric hashing are two contemporary paradigms for model-based object recognition that are put in perspective and differences and similarities are examined.
Proceedings ArticleDOI

Partial surface and volume matching in three dimensions

TL;DR: This paper presents a new technique for partial surface and volume matching of images in three dimensions that treats separately the rotation and the translation components of the Euclidean motion that it seeks, and compares favorably with previous techniques.
References
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Book

Computer vision

Journal ArticleDOI

Generalizing the hough transform to detect arbitrary shapes

TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
Book

Robot Vision

TL;DR: Robot Vision as discussed by the authors is a broad overview of the field of computer vision, using a consistent notation based on a detailed understanding of the image formation process, which can provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.
Journal ArticleDOI

Fourier Descriptors for Plane Closed Curves

TL;DR: It is established that the Fourier series expansion is optimal and unique with respect to obtaining coefficients insensitive to starting point and the amplitudes are pure form invariants as well as are certain simple functions of phase angles.
Journal ArticleDOI

The psychology of computer vision

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