The book comprehensively covers a wide range of evolutionary Computer Vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection. The topic of “computer vision” has grown to encompass a wide range of activities, from gathering raw data to extracting patterns from images and interpreting data. The majority of computer vision jobs have to do with feature extraction from input scenes (digital images) in order to get information about events or descriptions. Computer vision combines pattern detection and image processing. Image understanding comes from the computer vision process. The field of computer vision, in contrast to computer graphics, focuses on extracting information from images. Computer technology is essential to the development of computer vision, whether it is for image quality improvement or image recognition. Since the design of the application system determines how well a computer vision system performs, numerous scholars have proposed extensive efforts to broaden and classify computer vision into a variety of fields and applications, including assembly line automation, robotics, remote sensing, computer and human communications, assistive technology for the blind, and other technologies. Deep Learning (DL) is a member of the AI method family. Artificial Neural Networks (ANNs) get their name from the fact that they receive an input, analyze it, and produce a result. Deep Learning is based on ANN. Because of the massive amount of data generated every minute by digital transformation, AI is becoming more and more popular.