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Here we present a novel method to detect the boundaries of wood defects in X-ray wood images using the Hopfield Neural Network (HNN) algorithm based on Matlab programming. An improved energy function was designed for the HNN to perform the boundary detection which was formulated, in this paper, as an optimization process that could locate the boundary points of wood defects. The gray value of each...
In this paper, We apply A Hopfield neural network dynamic model with an improved energy function to edge detection of log digital images. The edge detection problem in this paper was formulated as an optimization process that sought the edge points to minimize an energy function which is different from the traditional methods. The dynamics of Hopfield neural networks were applied to solve the optimization...
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...
Defected woods always influence wood processing. If they are not chosen precisely, the woods will be inferior in quality. In order to obtain contours and internal information of defected woods, they are detected. Images of them are processed for the scientific usage of woods and guarantee of the increasing using rate of woods. Mathematical morphology is a new subject established based on rigorous...
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