By Yide Ma, Kun Zhan, Zhaobin Wang
Functions of Pulse-Coupled Neural Networks explores the fields of photo processing, together with photograph filtering, photo segmentation, photo fusion, picture coding, photo retrieval, and biometric popularity, and the function of pulse-coupled neural networks in those fields. This e-book is meant for researchers and graduate scholars in man made intelligence, trend popularity, digital engineering, and machine technological know-how. Prof. Yide Ma conducts learn on clever info processing, biomedical photo processing, and embedded process improvement on the institution of data technological know-how and Engineering, Lanzhou college, China.
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Extra resources for Applications of Pulse-Coupled Neural Networks
3) where p1 and p0 are the probabilities of 1 and 0 in the segmented image respectively. 3 Image Segmentation Using Maximum Between-cluster Variance Variance is a measure of the gray-scale distribution homogeneity of images. The greater the variance is, the greater the diﬀerence between object(s) and a background is, while the diﬀerence between them will reduce when part of pixels in object(s) are divided into a background by error and vice versa, so the segmentation with maximal between-cluster variance means the smallest probability of the segmentation mistaken.
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  Zhan K, Zhang HJ, Ma YD (2009) New spiking cortical model for invariant texture retrieval. IEEE Transactions on Neural Networks 20(12): 1980 – 1986  Ma YD, Shi F, Li L (2003) A new kind of impulse noise ﬁlter based on PCNN. In: Proceedings of 2003 International Conference on Neural Networks and Signal Processing, Nanjing, 14 – 17 December 2003  Ma YD, Zhang HJ (2008) New image denoising algorithm combined PCNN with gray-scale morphology.
2) i=1 To minimize E by α, for ∀i, let ∂E/∂αi = 0, and the result is shown as follows: ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ ⎛ ⎜ ⎜ ⎜ ⎜ =⎜ ⎜ ⎜ ⎜ ⎝ ϕ1 (x, y) ϕ1 (x, y) ... y x y ϕ2 (x, y)ϕ2 (x, y) y x ϕN (x, y) ϕ1 (x, y) x ... y .. x ϕ1 (x, y) ϕ2 (x, y) ... y x f (x, y) ϕ1 (x, y) y x ⎞ y ⎞ ⎞ ⎟⎛ ⎟ α1 ⎟⎜ ⎟ ϕN (x, y)ϕ2 (x, y) ⎟ α ⎟ ⎟⎜ ⎟ ⎟⎜ . ⎟ ⎟⎜ .. ⎝ .. ⎠ ⎟ . ⎟ ⎠ αN ϕ2 (x, y) ϕN (x, y) x ⎟ ⎟ ⎟ f (x, y)ϕ2 (x, y) ⎟ ⎟. ⎟ .. ⎟ . 4) ∀i. 5) x and ϕi (x, y)ϕi (x, y) = 1, y x 48 Chapter 4 Image Coding Then αi can be obtained from αi = f (x, y)ϕi (x, y) y 1 i N.
Applications of Pulse-Coupled Neural Networks by Yide Ma, Kun Zhan, Zhaobin Wang