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 Article
Face Detection and Auto Positioning for Robotic Vision System
TL;DR: A face detection technique is implemented and it is performed using skin color segmentation with two color space which are YCbCr and HSV and it was found that this system results a good performance.
Multi-pass Feedback Control for Object Recognition
TL;DR: A system for focus of attention is described based on feedback strategies combining low-level features, and a high-level object model to recognise the object and to direct the search for missing information by applying complex feedback strategies to recognise generic classes of objects.
Book ChapterDOI
XFF: A Simple Method to eXtract Fractural Features for 2D Object Recognition
TL;DR: XFF is a new method for representing 2-D images, based on the extraction of a set of fractal features which exploits the approximation of an image with an Iterated Function System, a technique that is already at the basis of many successful image compression tools.
Proceedings ArticleDOI
A connectionist approach to multiple-view based 3-D object recognition
TL;DR: The authors propose a hierarchical approach to solving the surface and the vertex correspondence problems in multiple-view based 3-D object recognition systems and provide a more general and compact formulation of the problem and a solution more suitable for parallel implementation.
Multiple kernel and multi-label learning for image categorization
TL;DR: This dissertation proposes a multiple kernel multi-label ranking method that learns a shared sparse kernel combination that benefits all image classes and integrates the proposed MLR-L1 algorithm with an efficient semi-infinite linear programming (SILP) based MKL solver and develops a computationally efficient wrapper algorithm, termed MK-MLR.
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.