Feature Extraction for Image Processing and Computer Vision—A Comparative Approach

Abstract

Digital image processing is a technique to process image digitally. This means, image is represented using pixels and all the algorithms are implemented using that value. A digital image consists of a finite variety of parts remarkably named as image parts, pixels. Pixel value determines the type of image, i.e. whether it is grey scale or RGB. Computer vision is a part of deep learning in which processing is done on images. The application of image processing includes robotics, object detection, weather forecasting, etc. In this paper, the main goal is to focus on different feature extraction techniques applied by computer vision and digital image processing. Image features are important input for any image processing tasks. Features include blobs, corner, edges, etc.