①Institute of Autormation hinese Academy of Sciences eijing 100080;Institute of Image Processing and Recognition Xi 'an Jiaotong University Xi' an 710049;③Information Institute Dalian Maritime University Dalian 116026
Abstract：Based on the scale and the state of intensity distribution in some neighborhoods of edge points, this paper proposes a measure vector of importance of point that consists of 4 components and corresponds to every point. For considering the background of application, this paper first trains a BP neural network using some samples that have classified by manual work, and then extracts more important edge points in a new image using the trained neural network. Because the image needs not be smoothed by some function in this algorithm, the edge deflection will not happen as usual, the location of gotten edge is in the accurate position. The effectiveness of this algorithm has been testified by some experiments.
田原; 杨海军; 梁德群; 王红光; 吴更石. 基于神经网络和点的重要性度量的边缘提取方法[J]. 电子与信息学报, 2000, 22(2): 247-252 .
Tian Yuan①;Yang Haijun②;Liang Dequn③;Wang Hongguan②; Wu Gengshi②. THE METHOD OF EDGE DETECTION BASED ON NEURAL NETWORK AND THE MEASURE OF IMPORTANCE OF POINTS. , 2000, 22(2): 247-252 .
2000, Vol. 22 
