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Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.5

ISSN Druckformat: 1065-3090
ISSN Online: 1940-4336

Journal of Flow Visualization and Image Processing

DOI: 10.1615/JFlowVisImageProc.2016017209
pages 151-163

CELLULAR NEURAL NETWORK IMAGE EDGE DETECTION BASED ON HYPERBOLIC TANGENT FUNCTION − FROM PHOTOGRAPHIC IMAGE TO FLOW VISUALIZATION

Xianhong Zhang
Northeast Forestry University
Chunrui Zhang
Department of Mathematics, Northeast Forestry University, China

ABSTRAKT

This paper proposes the architecture of cellular neural network (CNN) for image edge detection based on a hyperbolic tangent function. This network gives better results compared to other methods and is capable of application in real time due to the parallel processing nature of the CNN. The paper first discusses the equilibrium point of the hyperbolic tangent function, and thereafter the values of a template suitable for cellular neural network are determined. The method effectively defines the template of CNN and uses MATLAB simulation to verify the effectiveness of the edge detection of wood defect images and fluid images. The experimental results show that the improved CNN model can significantly reduce the number of iterations in the edge extraction, and can more accurately determine the edge.


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