Intelligence Semantics

Advances in Intelligent Informatics 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 tune initially offered on the 3rd overseas 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 themes together with sign processing, trend popularity, picture processing facts mining and their applications.

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2 Applications and Scopes The algorithm feasibility was tested under various different conditions and found to be positive. The robustness of proposed method is clearly visualized in this paper with the above results. The scope of this paper is mainly in segmentation and image analysis where the texture of an image is not so clear and unique. This method will also be used in biomedical and satellite image processing to classify various patterns and objects in the image. It also enhances the image.

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. These grayscale images from grayscale capturing devices are very challenging and involve many hurdles for image processing operations.

This paper presents a novel method to convert a grayscale image to a colored image for quality image analysis. The grayscale IP operations are very challenging and limited. The information extracted from such images is inaccurate. Therefore, the input image is transformed using a reference color image by reverse engineering. The gray levels of grayscale image are mapped with the color image in all the three layers (red, green, blue). These mapped pixels are used to reconstruct the grayscale image such that it is represented in a 3 dimensional color matrix.

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