Advances in Intelligent Informatics (Advances in Intelligent by El-Sayed M. El-Alfy, Sabu M. Thampi, Hideyuki Takagi, Selwyn

By El-Sayed M. El-Alfy, Sabu M. Thampi, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne

This publication encompasses a collection of refereed and revised papers of clever Informatics song initially awarded on the 3rd overseas Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this music conceal a number of clever informatics and similar themes together with sign processing, trend attractiveness, photograph processing facts mining and their applications.

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Algorithm 1. Grayscale Image to Color Image Transformation. Assumptions: Input: 1D grayscale image, 1. 2. 3. and 3D reference color image, of any size. Output: Pseudo colored image, Transform the referenced image to grayscale using any of the methods from equations (1-5) or simply single layer gray image _ Cluster the gray levels into 256 clusters for 0 − 255 levels for both and . _ K-Mean clustering algorithm is used here. we map with the same ith level in such that the For every ith gray level in _ th coordinate ( , ) of i of refer to the coordinate ( , ) of for the _ 4.

Various Image Enhancement Techniques-A Critical Review. : Image Enhancement Using Particle Swarm Optimization. : Artificial immune systems: a new computational intelligence approach. : Comparative analysis of evolutionary algorithms for image enhancement. : Digital image processing using MATLAB, vol. 2. , Ghosh, A. (2009). Gray-level Image Enhancement By Particle Swarm Optimization. : An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters 31(13), 1816–1824 (2010) 8 S.

Grayscale to Color Map Transformation for Efficient Image Analysis 15 Fig. 1 k-Means Clustering Algorithm The gray level of image is clustered using simple k-means clustering algorithm. kmeans is a vector quantization iterative technique to partition an image into k-selected clusters. The pixel intensity is used for computing mean cluster and distance is the squared difference between the intensity and the cluster center. The algorithm may not return optimal result but guaranties to converge and so is used in this paper.

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