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.
Read Online or Download Applications of Pulse-Coupled Neural Networks PDF
Similar bioinformatics books
Genome Exploitation: info Mining the Genome is constructed from the twenty third Stadler Genetic Symposium. This quantity discusses and illustrates how scientists are going to signify and utilize the big quantity of data being amassed in regards to the plant and animal genomes. Genome Exploitation: info Mining the Genome is a cutting-edge photograph on mining the Genome databases.
The second one variation of Adhesion Protein Protocols combines conventional ideas with state-of-the-art and novel recommendations that may be tailored simply to assorted molecules and cellphone varieties. the themes mentioned during this up to date moment variation comprise novel recommendations for learning cell-cell adhesion, neutrophil chemotaxis, in vitro assays used to check leukocyte migration via monolayers of cultured endothelial cells, and novel concepts to purify pseudopodia from migratory cells.
This ebook introduces a discrete optimisation method in 4 functions: vintage vacationer shop clerk challenge (TSP), Multilocus Genetic Mapping, Multilocus Consensus Genetic Mapping, and actual Mapping. all of the 4 sections comprises the matter formula, description of the set of rules, and experimental effects.
Inferring definitely the right destinations and splicing styles of genes in DNA is a tricky yet vital job, with vast functions to biomedicine. The mathematical and statistical ideas which have been utilized to this challenge are surveyed and arranged right into a logical framework in accordance with the speculation of parsing.
- Biocomputation and biomedical informatics: Case studies and applications
- Modern Methods of Drug Discovery
- Nucleic Acid Research and Molecular Biology
- Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
- Advances in Molecular and Cell Biology
- Statistical bioinformatics
Additional info for Applications of Pulse-Coupled Neural Networks
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  Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River, NJ.  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.
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 : – 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.