By Yide Ma, Kun Zhan, Zhaobin Wang
ISBN-10: 364213744X
ISBN-13: 9783642137440
Functions of Pulse-Coupled Neural Networks explores the fields of snapshot processing, together with photograph filtering, snapshot segmentation, photograph fusion, snapshot coding, picture retrieval, and biometric popularity, and the position of pulse-coupled neural networks in those fields. This e-book is meant for researchers and graduate scholars in man made intelligence, development acceptance, digital engineering, and laptop technology. Prof. Yide Ma conducts study on clever details processing, biomedical snapshot processing, and embedded approach improvement on the tuition of data technological know-how and Engineering, Lanzhou collage, China.
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Extra info for Applications of Pulse-Coupled Neural Networks
Example text
3, where the PCNNNI can be replaced by the ICM or SCM with no interconnection in this method. Fig. 3. Flow chart of Algorithm III 22 Chapter 2 Image Filtering In Fig. 3, E0 and L are a very small intensity and the possible maximum intensity (1 or 255). According to the fact that human visual system is insensitive to the slight variation in the image intensity, the preprocessing can reduce the image intensity range and shorten the iteration time of the PCNNNI. Δ is an intensity step length and is used to revise the value of pixels step by step.
The corresponding element value in the time matrix T remains unchanged, and the threshold of the neuron is determined by Eq. 8). (4) If all elements of T are not “0”, note down the iteration times N1 , and switch to (5); else, return to (3). (5) Slide a 3 × 3 window matrix K (each element is “1”) on T , deal with the nine elements overlaid by K of T in turn, and choose proper subsequent filtering algorithms to restore the preprocessed corrupted image. Rank the nine elements first: (a) If the nine elements are equivalent to each other, the mean filter is used to process the preprocessed image.
In the same way as in Chapter 2, in the Me4 and Me5, the PCNN is replaced by the SCM, too. The four evaluation criteria — gray-level uniformity measure (GU), gray-level contrast measure (GC), entropy (En), and cross-entropy (Ce) are presented for these methods. In order to evaluate the performance of these four methods more comprehensively, here we just suggest another evaluating method, the overall merit (OM). In fact, it is the arithmetic mean of the GU, GC, En, and (1 – Ce), determined by Eq.
Applications of Pulse-Coupled Neural Networks by Yide Ma, Kun Zhan, Zhaobin Wang
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