A exhaustive summary on different approaches & applications related to the different fields of Digital Image Processing (DIP) with its implementation processes

Abstract

The goal of this research paper is to study different ways to work with images. Imagine if you wanted to find specific things in a picture, like telling apart different objects. To do this, we use special methods to first turn the picture into simple black and white parts and then separate these parts from each other. We tested different methods, like Otsu's approach and K-means Clustering, to see which worked best. Then, we used the texture, shape, and color of the objects in the picture to understand more about them. We also used a tool that helps find the edges of objects and cleans up any unwanted specks in the image, making it easier to see. This study also discusses picture categorization algorithms such as Artificial Neural Networks and Support Vector Mechanisms. An ANN categorises the image into the receptive class, and SVM is used to compile all of the categorised results. Overall, the study provides in-depth understanding of picture processing and identification procedures.