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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
On the verification of hypothesized matches in model-based recognition
TL;DR: The authors derive an expression for the probability that a randomly occurring match will account for a given fraction of the features of a particular object, a function of the number of model features, theNumber of data features, and bounds on the degree of sensor noise.
Journal ArticleDOI
Conundrum of combinatorial complexity
TL;DR: This paper examines fundamental problems underlying difficulties encountered by pattern recognition algorithms, neural networks, and rule systems as combinatorial complexity of algorithms, of their computational or training requirements, and the potential role of the fuzzy logic in overcoming current difficulties.
Journal ArticleDOI
Model-Based Localisation and Recognition of Road Vehicles
TL;DR: A form of the generalised Hough transform is used in conjuction with explicit probability-based voting models to find consistent matches and to identify the approximate poses of vehicles in traffic scenes, which under normal conditions stand on the ground-plane.
Journal ArticleDOI
CAD-based computer vision: from CAD models to relational graphs
Patrick J. Flynn,Anil K. Jain +1 more
TL;DR: It is argued that a system to infer automatically a model appropriate for vision tasks from the manufacturing model is needed to efficiently create a large database (more than 100 objects) of 3-D models to evaluate matching strategies.
Model-Based Object Recognition - A Survey of Recent Research
TL;DR: There is still much room for improvement in the scope, robustness, and efficiency of object recognition methods, and what are the ways improvements will be achieved are identified.
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.