Journal ArticleDOI
Polygonal approximation using a competitive Hopfield neural network
TLDR
A parallel method using a Competitive Hopfield Neural Network (CHNN) is proposed for polygonal approximation and is compared to several existing methods by the approximation error norms L2 and L∞ with the result that promising approximation polygons are obtained.About:
This article is published in Pattern Recognition.The article was published on 1994-11-01. It has received 99 citations till now. The article focuses on the topics: Hopfield network & Approximation error.read more
Citations
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Journal ArticleDOI
Image processing with neural networks–a review
TL;DR: The various applications of neural networks in image processing are categorised into a novel two-dimensional taxonomy for image processing algorithms and their specific conditions are discussed in detail.
Journal ArticleDOI
A survey of shape analysis techniques
TL;DR: This paper provides a review of shape analysis methods, which play an important role in systems for object recognition, matching, registration, and analysis.
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Techniques for assessing polygonal approximations of curves
TL;DR: A measure which combines the relative fidelity and efficiency of a curve segmentation is described, and this measure is used to compare the application of 23 algorithms to a curve first used by Teh and Chin (1989).
Journal ArticleDOI
The application of competitive Hopfield neural network to medical image segmentation
TL;DR: In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation, a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism.
Journal ArticleDOI
An efficient algorithm for the optimal polygonal approximation of digitized curves
TL;DR: This work addresses the same problem using the framework of heuristic search strategies to find the shortest path in a graph and shows that the complexity of the algorithm is close to O( P 2 ).
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
Journal ArticleDOI
Some informational aspects of visual perception.
TL;DR: Special types of lawfulness which may exist in space at a fixed time, and which seem particularly relevant to processes of visual perception are focused on.