Chen, Yuli Park, Sung-Kee Ma, Yide Ala, Rajeshkanna 2024-01-20T17:01:00Z 2024-01-20T17:01:00Z 2022-01-10 2011-06 1045-9227 https://pubs.kist.re.kr/handle/201004/130272 An automatic parameter setting method of a simplified pulse coupled neural network (SPCNN) is proposed here. Our method successfully determines all the adjustable parameters in SPCNN and does not need any training and trials as required by previous methods. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons, and then deduce the sub-intensity range expression of each segment based on the general formulae. Besides, we extract information from an input image, such as the standard deviation and the optimal histogram threshold of the image, and attempt to build a direct relation between the dynamic properties of neurons and the static properties of each input image. Finally, the experimental segmentation results of the gray natural images from the Berkeley Segmentation Dataset, rather than synthetic images, prove the validity and efficiency of our proposed automatic parameter setting method of SPCNN. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC COUPLED NEURAL-NETWORKS ROTATION LINKING SCALE A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation Article 10.1109/TNN.2011.2128880 1 IEEE TRANSACTIONS ON NEURAL NETWORKS, v.22, no.6, pp.880 - 892 IEEE TRANSACTIONS ON NEURAL NETWORKS 22 6 880 892 scie scopus 000291355700005 2-s2.0-79957971391 Computer Science, Artificial Intelligence Computer Science, Hardware & Architecture Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Article COUPLED NEURAL-NETWORKS ROTATION LINKING SCALE sub-intensity range Automatic parameter setting dynamic property general formulae image segmentation optimal histogram threshold simplified pulse coupled neural network standard deviation static property