By El-Sayed M. El-Alfy, Sabu M. Thampi, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne
This e-book features a collection of refereed and revised papers of clever Informatics music initially provided on the 3rd foreign Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this tune conceal a number of clever informatics and similar issues together with sign processing, development popularity, photograph processing facts mining and their applications.
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75, the no of feature vectors required to achieve 100% accuracy is only 25.
11 12 S. Prasad, P. P. 0722 , , (3) ))/2 (4) (5) Here, equation 1 is the average grayscale image formed from RGB. It is quick but results an inaccurate dirty image. Whereas by grayscale image formed by using equation 2-3, the result is good and it treats each color same as human eye perceive. Thus they are also called as Luma or luminance gray image. The 4th and 5th formula results in a de-saturation and decomposition gray image respectively. There are many other methods and approaches to get a grayscaled image but it’s very difficult to convert a gray image to a colored one.
The problem faced in segmentation may be due to its grayscale nature and so if it was in color space their might not be such case. Therefore, the aim of this paper is to transform grayscale image into colored (pseudo colored) image for better analysis and segment. This pseudo colored images may also be used in controlling various computer operations like operating power point presentation without using any statist input devices, as proposed and used by Prasad et al. [10-11]. Here, different Grayscale to Color Map Transformation for Efficient Image Analysis 11 color markers are used to identify the objects; as a pointer and tracking the movements of these pointers they are mapped with the static mouse operations.