The book Computer Vision covers fundamental and advanced concepts in the field, providing a structured exploration of principles, techniques, and real-world applications. It starts with an introduction to the fundamentals, detailing image processing techniques, representation formats, color spaces, and transformations. Key topics include filtering, edge detection, and real-time image acquisition, with an emphasis on image sensors and hardware acceleration using GPUs and FPGAs. Real-time object detection and recognition are extensively covered, featuring state-of-the-art models such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-Based Convolutional Neural Networks). Advanced techniques such as deep learning, convolutional neural networks (CNNs), generative adversarial networks (GANs), semantic segmentation, and transfer learning are discussed, providing insights into modern approaches in the field. A strong focus is placed on real-world applications, showcasing the role of computer vision in autonomous vehicles, security systems, industrial automation, and robotics. The book highlights applications across various domains, including healthcare, retail, smart cities, and e-commerce, with practical case studies demonstrating real-time implementations. The book also addresses key challenges in computer vision, such as data dependency, computational complexity, generalization issues, and ethical considerations. With a balance of theoretical concepts and hands-on insights, it provides a practical guide for those working in AI, ML, and computer vision technology.