Edge Detection Combined Entropy Threshold and Self-Organizing Map (SOM)

Authors:
Kun Wang;Liqun Gao;Zhaoyu Pian;Li Guo;Jianhua Wu
Affiliations:
College of Information Science & Engineering, Northeastern University, P.O. Box 135, 110004, Shenyang, China;College of Information Science & Engineering, Northeastern University, P.O. Box 135, 110004, Shenyang, China;College of Information Science & Engineering, Northeastern University, P.O. Box 135, 110004, Shenyang, China;College of Information Science & Engineering, Northeastern University, P.O. Box 135, 110004, Shenyang, China;College of Information Science & Engineering, Northeastern University, P.O. Box 135, 110004, Shenyang, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 7
Cited 1

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Self-Organizing Maps

Self-Organizing Maps
Image Segmentation by Unifying Region and Boundary Information

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic edge detection using 3 × 3 ideal binary pixel patterns and fuzzy-based edge thresholding

Pattern Recognition Letters
An edge detection method by combining fuzzy logic and neural network

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Automatic image segmentation by integrating color-edge extraction and seeded region growing

IEEE Transactions on Image Processing

HW/SW co-design of reconfigurable hardware-based genetic algorithm in FPGAs applicable to a variety of problems

Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

An edge detection method by combining image entropy and Self -Organizing Map (SOM) is proposed in this paper. First, according to information theory image entropy is used to curve up the smooth region and the region of gray level abruptly changed. Then we transform the gray level image to ideal binary pattern of pixels. We define six classes' edge and six edge prototype vectors. These edge prototype vectors are fed into input layer of the Self-Organizing Map (SOM). Classifying the type of edge through this network, the edge image is obtained. At last, the speckle edges are discarded from the edge image. Experimental results show that it gained better edge image compared with Canny edge detection method.