Journal ArticleDOI
Boundary-based corner detection using neural networks
TLDR
All features of corners, tangent points and inflection points can be extracted from the boundary of any arbitrary shape by using both artificial neural networks.About:
This article is published in Pattern Recognition.The article was published on 1997-01-01. It has received 61 citations till now. The article focuses on the topics: Inflection point & Curvature.read more
Citations
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Book
Local Invariant Feature Detectors: A Survey
TL;DR: An overview of invariant interest point detectors can be found in this paper, where an overview of the literature over the past four decades organized in different categories of feature extraction methods is presented.
Journal ArticleDOI
Robust image corner detection through curvature scale space
F. Mokhtarian,R. Suomela +1 more
TL;DR: In this paper, the authors proposed a novel method for image corner detection based on the curvature scale-space (CSS) representation. And the method is robust to noise, and they believe that it performs better than the existing corner detectors.
Journal ArticleDOI
Performance evaluation of corner detectors using consistency and accuracy measures
TL;DR: This paper evaluates the performance of several popular corner detectors using two newly defined criteria, consistency and accuracy, which show that the enhanced CSS corner detector performs better according to these criteria.
Journal ArticleDOI
Boundary-based corner detection using eigenvalues of covariance matrices
TL;DR: A new measure for corner detection based on the eigenvalues of the covariance matrix of boundary points over a small region of support that avoids false alarms for superfluous corners on circular arcs is presented.
Journal ArticleDOI
A new algorithm for dominant points detection and polygonization of digital curves
Majed Marji,Pepe Siy +1 more
TL;DR: A new algorithm for detecting dominant points and polygonal approximation of digitized closed curves is presented, which uses an optimal criterion for determining the region-of-support of each boundary point, and a new mechanism for selecting the dominant points.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Book
Pattern Recognition: Statistical, Structural and Neural Approaches
TL;DR: This chapter discusses supervised learning using Parametric and Nonparametric Approaches and unsupervised Learning in NeurPR, and discusses feedforward Networks and Training by Backpropagation.
Journal ArticleDOI
On the detection of dominant points on digital curves
C.-H. Teh,Roland T. Chin +1 more
TL;DR: A parallel algorithm for detecting dominant points on a digital closed curve is presented, which leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance, but also on the precise determination of the region of support.