A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Edge detection improvement by ant colony optimization
Pattern Recognition Letters
A 2D approach to tomographic image reconstruction using a Hopfield-type neural network
Artificial Intelligence in Medicine
A method based on rank-ordered filter to detect edges in cellular image
Pattern Recognition Letters
A geometric active contour model without re-initialization for color images
Image and Vision Computing
Optical flow active contours with primitive shape priors for echocardiography
EURASIP Journal on Advances in Signal Processing - Image processing and analysis in biomechanics
Hi-index | 0.00 |
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.