Authors : N.S. Anu, Jeslin George and Ranjidha Rajan
Abstract: Grayscale conversion of images is an important step in all image processing tasks. It is done to minimize the complexity of processing a color image. Moreover, grayscale images preserves the brightness, contrast, edges, shape, texture and structure of color images. Traditional methods use standard NTSC coefficients for color to grayscale conversion. However, previous studies have revealed that the standard NTSC coefficients are not optimal for all types of image classification problems. This study presents a study on color to grayscale image conversion for classifying disease affected regions in a leaf from normal regions. We present an optimization technique using Genetic Algorithm (GA) for color to grayscale image conversion. By using GA, the coefficients for grayscale conversion are optimized to get minimum error in classification.
N.S. Anu, Jeslin George and Ranjidha Rajan, 2018. Optimized Grayscale Conversion in Images for Leaf Disease Detection. Journal of Engineering and Applied Sciences, 13: 4588-4591.