Abstract
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.
Highlights
X-ray wood nondestructive testing is an effective method for accessing to internal information of wood
We presented a novel approach to automatically detect wood defects boundaries using a modified Hopfield neural network with a specific cost function designed for wood defects image
An X-ray imaging technique was applied in wood nondestructive detection
Summary
X-ray wood nondestructive testing is an effective method for accessing to internal information of wood. The Canny operator [5, 6], another gradient operator, is used to determine a class of optimal filter for different types of boundaries All these operators detect boundary points by gray gradient change of the image pixels in the neighborhood; the disadvantage of these methods are sensitive to noise. Comparing with traditional edge detection methods, Hopfield neural network, which regarded an edge detection process as an optimization process, has been applied in the field of the low-level image processing of boundary detection in the recent years. We presented a novel approach to automatically detect wood defects boundaries using a modified Hopfield neural network with a specific cost function designed for wood defects image. We first discuss how to initiate defects boundaries, how to map the boundary detection problem into a Hopfield neural network, and a novel cost function for wood defects boundaries detection is described.
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