Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Autoregressive Model Approach to Two-Dimensional Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Estimation of Contour Properties by Cross-Validated Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric dominant point detection
Pattern Recognition
Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVGIP: Image Understanding
A nonlinear algorithm for shape representation
A nonlinear algorithm for shape representation
A multiple point boundary smoothing algorithm
Pattern Recognition Letters
Fast Decomposition of Digital Curves into Polygons Using the Haar Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corner Detection and Interpretation on Planar Curves Using Fuzzy Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
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Content-Based Image Retrieval at the End of the Early Years
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Improving fitting quality of polygonal approximation by using the dynamic programming technique
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Learning Shape Models from Examples
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Automatic Generation of Artistic Chinese Calligraphy
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Polygonal approximation of closed discrete curves
Pattern Recognition
Optimized polygonal approximation by dominant point deletion
Pattern Recognition
Dominant point detection by reverse polygonization of digital curves
Image and Vision Computing
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Contour simplification using a multi-scale local phase analysis
Image and Vision Computing
A general shape context framework for object identification
Computer Vision and Image Understanding
A New Algorithm for Dominant Point Detection by Quasi-collinear Break Points Supression
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Feature Based Affine Invariant Watermarking Robust to Geometric Distortions
Fundamenta Informaticae
Polygonal approximation of digital planar curves through break point suppression
Pattern Recognition
Automatic generation of artistic chinese calligraphy
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Dominant point detection: A new proposal
Image and Vision Computing
Data reduction of large vector graphics
Pattern Recognition
Arabic letters compression using new algorithm of trapezoid method
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
Arabic letters compression using new algorithm of trapezoid method
ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
Improved stochastic competitive Hopfield network for polygonal approximation
Expert Systems with Applications: An International Journal
Method for polygonal approximation through dominant points deletion
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Feature Based Affine Invariant Watermarking Robust to Geometric Distortions
Fundamenta Informaticae
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In this paper, we present a nonlinear algorithm for critical point detection (CPD) of 2D digital shapes. The algorithm eliminates the problems arising from curvature approximation and Gaussian filtering in the existing algorithms. Based on the definition of 驴critical level,驴 we establish a set of criteria for the design of an effective CPD algorithm for the first time. By quantifying the critical level to the modified area confined by three consecutive 驴pseudocritical points,驴 a simple but very effective algorithm is developed. The comparison of our experimental results with those of many other CPD algorithms shows that the proposed algorithm is superior in that it provides a sequence of figures at every detail level, and each has a smaller integral error than the others with the same number of critical points. The experimental results on shapes with various complexities also show the algorithm is reliable and robust with regard to noise.