By Rituparna Chaki, Khalid Saeed, Sankhayan Choudhury, Nabendu Chaki
This booklet comprises the prolonged model of the works which have been awarded and mentioned within the First overseas Doctoral Symposium on utilized Computation and safety platforms (ACSS 2014) held in the course of April 18-20, 2014 in Kolkata, India. The symposium has been together prepared by way of the AGH college of technological know-how & know-how, Cracow, Poland and collage of Calcutta, India. the quantity I of this double-volume ebook comprises fourteen top of the range publication chapters in 3 diversified components. half 1 is on development popularity and it offers 4 chapters. half 2 is on Imaging and Healthcare functions includes 4 extra booklet chapters. The half three of this quantity is on instant Sensor Networking and it comprises as many as six chapters. quantity II of the publication has 3 elements offering a complete of 11 chapters in it. half four comprises 5 very good chapters on software program Engineering starting from cloud provider layout to transactional reminiscence. half five in quantity II is on Cryptography with ebook chapters in it. half 6 of this quantity is on laptop Aided layout with 4 chapters in it. We strongly think that the twenty 5 chapters in those volumes of utilized Computation and safety platforms could be preferred via all its readers.
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Additional info for Applied Computation and Security Systems: Volume One
The simplest solution could be proper binarization, but there are no good results then. One of the approach is using Gaussian matched filter  and then binarization (for example local entropy thresholding [3, 4]). Blood vessels usually have poor local contrast, and edge detection algorithms results are not sufficient. In Gaussian matched filter method, we receive greyscale image approximated by Gaussian-shaped curve. The aim is to detect piecewise linear segments of blood vessels so we create 12 different mask filters and search for vessels in every 15°.
It is not always true that the higher number of features lead to higher recognition rate. So optimum feature selection is a big issue in pattern recognition domain. Optimum feature selection has many advantages. It reduces the feature size and increases the recognition rate. Feature selection process first identifies the irrelevant features, discards them, and takes others which are treated as optimum features. Thus, feature selection is basically a search process. There are different search algorithms such as greedy , branch and bound , sequential search algorithms , mutual information , and tabu search  which have been used successfully.
Then, SIFT is used to extract some points. After extraction of some points, total image is divided into several blocks of size 16 9 16 and find the number of points in each block and store them in a row vector. Hence, one row corresponds to one image and column corresponds to feature. So total number of features of a particular face or a row is (256 9 256)/ (16 9 16) = 256. But the 256 extracted features contain some noisy and irrelevant features. As a result, the performance of the system degrades.
Applied Computation and Security Systems: Volume One by Rituparna Chaki, Khalid Saeed, Sankhayan Choudhury, Nabendu Chaki