Computer Vision Techniques and Applications is a comprehensive and practical guide that delves into the science and engineering behind teaching machines to interpret and understand visual information. As visual data becomes increasingly central to modern technologies—spanning fields such as robotics, healthcare, autonomous systems, agriculture, and augmented reality—this book serves as an essential reference for researchers, developers, and students seeking to master the core concepts and cutting-edge developments in computer vision. The book begins by establishing a strong theoretical foundation in image formation, pixel-based analysis, and visual perception, before progressing into more advanced techniques involving object recognition, image segmentation, motion analysis, feature extraction, and 3D reconstruction. Each concept is explained with clarity, supported by mathematical formulations and real-world examples that demonstrate its application in contemporary visual systems.

Key applications highlighted include facial recognition, license plate reading, medical imaging diagnostics, automated inspection in manufacturing, gesture recognition, and autonomous navigation. Through case studies and hands-on coding examples in Python and popular libraries like OpenCV, TensorFlow, and PyTorch, readers gain practical skills for implementing vision systems that are robust, scalable, and adaptive to diverse use cases. In addition to the technical content, Computer Vision Techniques and Applications provides important context around data acquisition, annotation strategies, model evaluation metrics, and dataset challenges, helping readers build ethically sound and high-performing systems. Topics such as fairness, bias mitigation, data privacy, and explainability in computer vision are addressed with insight and responsibility, reinforcing the importance of transparency and accountability in intelligent vision applications.

ISBN:`2987-3653-2228 | No. of Pages: 492 | Book Version: 131.1.6