By Yide Ma, Kun Zhan, Zhaobin Wang

Purposes of Pulse-Coupled Neural Networks explores the fields of photograph processing, together with photograph filtering, snapshot segmentation, picture fusion, photograph coding, photo retrieval, and biometric reputation, and the position of pulse-coupled neural networks in those fields. This e-book is meant for researchers and graduate scholars in synthetic intelligence, development acceptance, digital engineering, and machine technology. Prof. Yide Ma conducts examine on clever info processing, biomedical photo processing, and embedded process improvement on the tuition of data technology and Engineering, Lanzhou collage, China.

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For an image, let T be the threshold and it is in the range from the minimum gray-level to the maximum; the ratio of the number of pixels in object(s) to the total is WA and their average gray-level is μA ; the ratio of the number of pixels in a background to the total is WB and their average gray-level is μB ; the average gray-level of the whole image is μ. Then the between-cluster variance is defined as σ 2 (T ) = WA (μA − μ)2 + WB (μB − μ)2 . 4) 30 Chapter 3 Image Segmentation That is to say, the T , which results in the maximal between-cluster variance of the object(s) and background, is the best segmentation threshold.

Reference [1] Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River, NJ. [2] Ranganath HS, Kuntimad G, Johnson JL (1995) Pulse coupled neural networks for image processing. In: Proceedings of IEEE Southeast Conference, Raleigh, 26 – 29 March 1995 [3] Zhan K, Zhang HJ, Ma YD (2009) New spiking cortical model for invariant texture retrieval. IEEE Transactions on Neural Networks 20(12): 1980 – 1986 [4] Ma YD, Shi F, Li L (2003) A new kind of impulse noise filter based on PCNN.

Therefore, the ISRC gets a better subjective visual quality of the reconstructed image than the JPEG method in which there exists serious distortion in reconstructed image, for instance, blocking artifacts, as shown in Fig. 1(c). And it will be able to achieve a higher compression ratio than traditional methods. Fig. 1. (a) Original image; (b) block transform based coding; (c) blocking artifacts; (d) irregular segmented region coding A segmentation-based compression technique is mainly realized through three steps [8]: – preprocessing – segmentation – coding of contours and texture components The purpose of preprocessing is to eliminate some tiny regions that slightly affect the coding quality, to enhance the image and remove noise produced in the sampling process.

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