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 Genetic-Based Vision System for Cross-Functional Integration in Flexible Manufacturing: A Tutorial and Application
TL;DR: In this article, a tutorial is presented that explains how a genetic algorithm can be applied to vision systems for shape analysis and quality assessment, and the control parameters for the algorithm are optimized by conducting experiments of Taguchi's approach to parameter design.
Proceedings ArticleDOI
Deep Manifold Embedding for 3D Object Pose Estimation
Hiroshi Ninomiya,Yasutomo Kawanishi,Daisuke Deguchi,Ichiro Ide,Hiroshi Murase,Norimasa Kobori,Nakano Yusuke +6 more
TL;DR: This paper proposes a method that inroduces “Deep Manifold Embedding” which maximizes the pose variation directly and constructs a manif old rom features extracted from Deep Convolutional Neural Networks trained with pose informati on.
Proceedings ArticleDOI
Object recognition: the utopian method is dead; the time for combining simple methods has come
TL;DR: The premise of the paper is that it would be possible to recognize more objects if the results from differing methods were combined, and the combination is not only to widen the domain of discourse of objects to be recognized but also to improve the quality of the recognition.
Journal ArticleDOI
Stereoscopic neuro-vision for three-dimensional object recognition
Mark B. Lynch,Cihan H. Dagli +1 more
TL;DR: In this paper, several different types of neural networks can be combined with a rule base and conventional processing techniques for the creation of a powerful 3D object recognition system, which is tested on several simple objects and the results are presented.
Book ChapterDOI
Free-Form Surface Description in Multiple Scales: Extension to Incomplete Surfaces
TL;DR: A novel technique for multi-scale smoothing of a free-form 3-D surface is presented and it is argued that the proposed technique is preferable to volumetric smoothing or level set methods since it is applicable to incomplete surface data which occurs during occlusion.
References
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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
Charles T. Zahn,Ralph Roskies +1 more
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.