Download e-book for iPad: Advanced Computing: First International Conference on by Aravinthan Parthibarajan, Gopalakrishnan Narayanamurthy,

By Aravinthan Parthibarajan, Gopalakrishnan Narayanamurthy, Arun srinivas Parthibarajan (auth.), Natarajan Meghanathan, Brajesh Kumar Kaushik, Dhinaharan Nagamalai (eds.)

ISBN-10: 3642178804

ISBN-13: 9783642178801

ISBN-10: 3642178812

ISBN-13: 9783642178818

This quantity constitutes the 3rd of 3 components of the refereed court cases of the 1st overseas convention on desktop technological know-how and knowledge expertise, CCSIT 2010, held in Bangalore, India, in January 2011. The forty six revised complete papers offered during this quantity have been conscientiously reviewed and chosen. The papers are equipped in topical sections on tender computing, similar to AI, Neural Networks, Fuzzy platforms, etc.; allotted and parallel structures and algorithms; defense and knowledge coverage; advert hoc and ubiquitous computing; instant advert hoc networks and sensor networks.

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Additional info for Advanced Computing: First International Conference on Computer Science and Information Technology, CCSIT 2011, Bangalore, India, January 2-4, 2011. Proceedings, Part III

Example text

There are generally 8 connectivity is taken into consideration as shown in the Fig. 1. e. 0, 1, 2 & 3. e. 4, 5, 6 & 7 are simply the negation of 0, 1, 2 & 3 directions. To obtain chain-code top left corner is considered as origin and scanning is done left to right, top to bottom (refer Fig. 2). Each pixel has observed separately and direction vector for that pixel is noted down. This process is carried out until the last pixel has scanned. Now, the frequency of occurrence in a particular direction is calculated for each segment and the histogram for each segment is used it to train the neural network.

Architecture of SFAM network Once SFAM has been trained, the equivalent of a “feed forward” pass for an unknown pattern classification consists of passing the input pattern through the complement coder and into the input layer. The output node activation function is evaluated and the winner is the one with the highest value. The category of the input pattern is the one with which the winning output node is associated [23]. 4 Face Recognition System Fig. 5 shows the structure of the recognition system.

2 Linear Discriminant Analysis (LDA) Linear Discriminant analysis or Fisherfaces method overcomes the limitations of eigenfaces method by applying the Fisher’s linear discriminant criterion. This criterion tries to maximize the ratio of the determinant of the between-class scatter matrix of the projected samples to the determinant of the within-class scatter matrix of the projected samples. Fisher discriminants group images of the same class and separates images of different classes. Fisher discriminants find the line that best separates the points.

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Advanced Computing: First International Conference on Computer Science and Information Technology, CCSIT 2011, Bangalore, India, January 2-4, 2011. Proceedings, Part III by Aravinthan Parthibarajan, Gopalakrishnan Narayanamurthy, Arun srinivas Parthibarajan (auth.), Natarajan Meghanathan, Brajesh Kumar Kaushik, Dhinaharan Nagamalai (eds.)


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