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6 April 1995 Neural network approach to edge detection and noise reduction in low-contrast images
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Numerous vision applications rely upon efficient techniques for detecting edges in an image. Edge detection is especially difficult in low-contrast images which are characterized by the general lack of sharp variations in the gray-scale intensity values between objects of interest and their backgrounds. In low-contrast images, the application of commonly employed edge detection algorithms may result in excessive noise. This paper presents a neural network model which enhances edges and reduces noise in low-contrast gray-scale images. A neural element is associated with each pixel in an image. Each neuron receives weighted gray-scale inputs from its immediate neighbors. The weights associated with the gray-scale inputs are determined through a fuzzy compatibility function that grades the degree of similarity between the gray-scale intensity values of neighboring pixels. The neural element sums its weighted inputs and subjects the weighted sum to a sigmoid function that produces gray-scale outputs ranging between 0 and 255. The slope of the sigmoid function is chosen to force resulting pixel values away from mid-range values and closer to either 0 or 255. The resulting image is then subjected to the Sobel edge detection algorithm. The technique is illustrated by applying it to several low-contrast infrared images containing military vehicles. The results show significant noise reduction and edge enhancement.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher M. Johnson, Edward W. Page, and Gene A. Tagliarini "Neural network approach to edge detection and noise reduction in low-contrast images", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995);

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