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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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Feasibility Study for Pose Estimation of Small UAS in Known 3D Environment Using Geometric Hashing

TL;DR: A novel self-localization method for small Unmanned Aerial Systems (UAS) is presented, which automatically establishes correspondence between the First-Person View (FPV) video streamed from a UAS flying in an urban environment and its 3D building model.

Function-based object recognition

TL;DR: The proposed scheme achieved high classification accuracy while using small training sets and reuse of functional primitives in different classes enables easy expansion to new categories.
Book ChapterDOI

PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems

TL;DR: In this article, the authors introduce the beginnings of such a framework inspired by the principles of Perceptual Control Theory (PCT), which is concluded that, if behaviour is the control of perception, then perception is the simulation of behaviour.
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Subspace-based clustering and retrieval of 3-D objects

TL;DR: A new method within the framework of principal component analysis is proposed to retrieve 3-D objects using Gaussian clusters to evaluate the similarity between a query image and signatures and the eigenspaces and signatures constitute the object descriptors.
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Eliciting visual primitives for detection of elongated shapes

TL;DR: A new visual primitive obtained by combining in a suitable correlation, a basic set of standard local features, is proposed, able to synthesize the information associated with local features and enhance detection in the case of non-ideal and noisy images.
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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