Optimum Uniform Piecewise Linear Approximation of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Finding Trajectories of Feature Points in a Monocular Image Sequence
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
Detection of significant points and polygonal approximation of digitized curves
Pattern Recognition Letters
An algorithm for detection of dominant points and polygonal approximation of digitized curves
Pattern Recognition Letters
Pattern Recognition Letters
Matching Point Features with Ordered Geometric, Rigidity, and Disparity Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
Techniques for Assessing Polygonal Approximations of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
A boundary concavity code to support dominant point detection
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Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On Critical Point Detection of Digital Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Based Corner Detecion and Localization Using New 'Cornerity' Index: A Robust Approach
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Piecewise Polygonal Approximation of Digital Curves
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Attributed String Matching with Merging for Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A fast iterative method for dominant points detection of digital curves
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
A new iterative approach for dominant points extraction in planar curves
WSEAS Transactions on Computers
Polygonal approximation of digital planar curves through break point suppression
Pattern Recognition
Fast and robust dominant points detection on digital curves
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A discrete geometry approach for dominant point detection
Pattern Recognition
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
A non-heuristic dominant point detection based on suppression of break points
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Image and Vision Computing
A novel framework for making dominant point detection methods non-parametric
Image and Vision Computing
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A polygonal approximation technique using reverse polygonization is presented in this paper. The reverse polygonization starts from an initial set of dominant points i.e. break points. In that, dominant points are deleted (one in each iteration) such that the maximal perpendicular distance of approximating straight line from original curve is minimal. A comparative study with some commonly referred algorithms is also presented, which shows that this technique can produce better approximation results. The algorithm has additional advantages like simplicity, polygonal approximation with any number of dominant points and up to any error value, and computational efficiency.