On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the theory of neural computation
Introduction to the theory of neural computation
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Improving Back-Propagation: Epsilon-Back-Propagation
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
A Fast Algorithm for Dominant Point Detection on Chain-Coded Contours
CAIP '93 Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns
A Neural Network-Based Algorithm to Detect Dominant Points from the Chain-Code of a Contour
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
A Corner-Finding Algorithm for Chain-Coded Curves
IEEE Transactions on Computers
An Improved Method of Angle Detection on Digital Curves
IEEE Transactions on Computers
Angle Detection on Digital Curves
IEEE Transactions on Computers
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Dominant Point Detection (DPD) is one of the tasks in image analysis; it aims making polygonal approximations through the search of a set of points of relevance in a contour, reducing the amount of information. In this work, the ability of neural networks to learn the performance of several DPD algorithms is studied. For it a dynamic neural net that traverses the contour will be used, giving a relevance measurement for each point and detecting them through a simple post-processing phase. Different training sets and net configurations were used. The results of applying the neural algorithm to images of real objects show its validity, and also the ability of neural nets to learn previously unknown DPD algorithms.