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
Statistical Approaches to Feature-Based Object Recognition
TL;DR: Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations, which motivates the development of new maximum-likelihood and MAP recognition formulations which are based on normal feature models.
Journal ArticleDOI
Multidimensional indexing for recognizing visual shapes
Andrea Califano,Rakesh Mohan +1 more
TL;DR: An analytical framework for studying some properties of model acquisition and recognition techniques based on indexing is introduced and a practical example of high-dimensional global invariants are introduced and used to implement a 2-D shape acquisition/recognition system.
Journal ArticleDOI
Sensor planning for object search
John K. Tsotsos,Yiming Ye +1 more
TL;DR: In this thesis, the task of sensor planning for object search is formulated as an optimization problem and an approximate solution employing a one step look-ahead strategy is proposed, equivalent to the optimal solution under certain conditions.
Book ChapterDOI
Model-based object recognition by geometric hashing
TL;DR: Extensions of the basic paradigm which reduce its worst case recognition complexity are discussed, and the Geometric Hashing with the Hough Transform and the alignment techniques are compared.
Book ChapterDOI
3-D Computer Vision Using Structured Light: Design, Calibration, and Implementation Issues
Fred W. DePiero,Mohan M. Trivedi +1 more
TL;DR: This work provides thorough discussions of design issues, calibration methodologies, and implementation schemes for SL sensors, and a novel SL sensor, PRIME, the PRofile Imaging ModulE recently has been developed.
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.