Journal ArticleDOI
Model-based recognition in robot vision
Roland T. Chin,Charles R. Dyer +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Efficient Pose Clustering Using a Randomized Algorithm
TL;DR: This work shows that pose clustering can have equivalent performance for this case when examining only O(mn) poses, if the authors are given two correct matches between model features and image features, and uses recursive histograming techniques to perform clustering in time and space that is guaranteed to be linear in the number of poses.
Automatic Modeling and Localization for Object Recognition
TL;DR: Novel algorithms to automatically construct object-localization models from many images of the object are presented, and a consensus-search approach to determine which parts of the image justifiably constitute inclusion in the model is presented.
Journal ArticleDOI
Multi-scale free-form 3D object recognition using 3D models
TL;DR: The recognition of free-form 3D objects using 3D models under different viewing conditions based on the geometric hashing algorithm and global verification is presented and results indicate that the technique is invariant to those transformations.
Journal ArticleDOI
Parallel Multithreaded Satisfiability Solver
TL;DR: The design and implementation of a highly optimized, multithreaded algorithm for the propositional satisfiability problem, based on the Davis-Putnam-Logemann-Loveland sequential algorithm, but includes many of the optimization techniques introduced in recent years.
References
More filters
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.