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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Book ChapterDOI
Generic 3-D Shape Model: Acquisitions and Applications
Xinquan Shen,David C. Hogg +1 more
TL;DR: The paper describes a method for generating a generic deformable model from a training set of shapes depicted in a corpus of image sequences, derived by principal component analysis on the aligned training shapes.
Proceedings ArticleDOI
Staged training of Neocognitron by evolutionary algorithms
TL;DR: It is demonstrated that evolutionary algorithms can successfully train the Neocognitron to perform image recognition on real world problems.
Surface modelling and surface following for robots equipped with range sensors
TL;DR: Techniques are presented for incrementally updating the surface model using sets of sensor points using a planar polyhedral surface model that is amenable to incremental surface modelling.
A blackboard infrastructure for object-based image interpretation
TL;DR: The paper presents a blackboard infrastructure for the development of object-based image interpretation applications based on an abstract deenition of important concepts in image interpretation: data objects, relationships, algorithms, strategies, and models.
Proceedings ArticleDOI
Recognizing partially occluded 2D parts based on the tracing of feature points
J.Z.C. Lai,J.M. Lin +1 more
TL;DR: The authors present a hierarchical problem-solving technique to match models to objects in the image using a linear function of the number of feature points and find that the storage requirements are less than those of other systems.
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.