Algorithms for Image Processing and Computer Vision

Image Processing and Computer Vision are closely related fields that focus on enabling computers to understand and interpret visual information. Image Processing primarily deals with modifying, enhancing, and analyzing images to improve their quality or extract useful details. It involves operations such as filtering, noise reduction, edge detection, color adjustment, segmentation, and restoration. These techniques help prepare images for further analysis or make them visually clearer for human viewing.

Computer Vision, on the other hand, aims to replicate human vision by enabling machines to recognize objects, understand scenes, and make decisions based on visual input. It uses advanced algorithms, machine learning, and deep learning models to detect objects, classify images, track motion, identify patterns, and even understand human gestures. Computer vision systems are used in facial recognition, autonomous vehicles, medical imaging, robotics, surveillance, and augmented reality.

While image processing focuses on transforming images, computer vision focuses on understanding them. Both fields often work together: image processing prepares the visual data, and computer vision interprets it. This combination plays a crucial role in modern technologies, enabling smarter systems that can perceive, analyze, and interact with the world in real time.