Study on Wood Image Edge Detection Based on Hopfield Neural Network
A Hopfield neural network dynamic model with an improved energy function was presented for edge detection of log digital images in this paper. Different from the traditional methods, the edge detection problem in this paper was formulated as an optimization process that sought the edge points to minimize an energy function. The dynamics of Hopfield neural networks were applied to solve the optimization problem. An initial edge was first estimated by the method of traditional edge algorithm. The gray value of image pixel was described as the neuron state of Hopfield neural network. The state updated till the energy function touch the minimum value. The final states of neurons were the result image of edge detection. The novel energy function ensured that the network converged and reached a near-optimal solution. Taking advantage of the collective computational ability and energy convergence capability of the Hopfield network, the noises will be effectively removed. The experimental results showed that our method can obtain more vivid and more accurate edge than the traditional methods of edge detection.
Hopfield Neural Network Edge Detection Energy Function Dynamic Model Wood Image
Dawei Qi Peng Zhang Xuejing Jin Xuefei Zhang
Northeast Forestry University Harbin,150040,China Harbin Applied Vocational and Technical College Harbin,150040,China
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-5
2010-06-20（万方平台首次上网日期，不代表论文的发表时间）