Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 427878 8 pages doi 2010 427878 Research Article Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image Dawei Qi Peng Zhang Xuefei Zhang Xuejing Jin and Haijun Wu College of Science Northeast Forestry University Harbin 150040 China Correspondence should be addressed to Dawei Qi qidw9806@ Received 31 December 2009 Revised 14 April 2010 Accepted 13 May 2010 Academic Editor Joao Manuel R. S. Tavares Copyright 2010 DaweiQietal. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed and our method obtains more noiseless and vivid boundary than those of the traditional methods. 1. Introduction X-ray wood nondestructive testing is an effective method for accessing to internal information of wood. Comparing with other conventional wood nondestructive testing such as appearance judgment .
