By Yide Ma, Kun Zhan, Zhaobin Wang
Purposes of Pulse-Coupled Neural Networks explores the fields of snapshot processing, together with snapshot filtering, snapshot segmentation, photo fusion, photo coding, snapshot retrieval, and biometric reputation, and the position of pulse-coupled neural networks in those fields. This ebook is meant for researchers and graduate scholars in synthetic intelligence, development popularity, digital engineering, and laptop technology. Prof. Yide Ma conducts examine on clever details processing, biomedical photo processing, and embedded method improvement on the institution of knowledge technology and Engineering, Lanzhou collage, China.
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Extra resources for Applications of Pulse-Coupled Neural Networks
Image binarization segmentation which is deﬁned as dividing an image into objects and background is the most fundamental and important processing step and common, basic and key technique in the research of object identiﬁcation, image understanding and computer vision. The performance of image segmentation will impact directly on the subsequent object identiﬁcation and image understanding. There are many methods of image segmentation and the simplest and most eﬀective one is the method based on the gray-level threshold, but it is very diﬃcult to select a appropriate threshold.
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. 3 Orthonormalizing Process of Basis Functions As proved in Ref. , in an N -dimensional subspace, a set of orthonormal basis functions always can be gotten from a set of linearly independent initial basis using the orthonormalization scheme of Gram-Schmidt.
The situation of (b) is almost opposite to (a). The entropy increases quickly from 0 to the maximum, nearly 1, when the iteration time is 10 and then decreases slowly. 3 Image Segmentation Using Simpliﬁed PCNN and GA Genetic Algorithm (GA) is a random optimization algorithm and was proposed drawing lessons from the natural selection and natural genetic mechanisms in the organic sphere. As a search algorithm which has the features of robustness, self-adaptive and parallelism, the GA has been widely used in the ﬁelds of image processing.