Image Processing and Machine Learning, Volume 1 1st Edition

Image Processing and Machine Learning, Volume 1 (1st Edition) by Erik Cuevas and Alma Nayeli Rodríguez is an essential, modern text that bridges theory and practice for anyone working at the intersection of computer vision and AI. Written with clarity and rigor, this volume delivers foundational methods in image processing alongside robust machine learning techniques, making complex concepts accessible without sacrificing depth.

Begin with a compelling overview of core topics — image enhancement, segmentation, feature extraction, and pattern recognition — then move into contemporary learning approaches including supervised and unsupervised methods, neural networks, and convolutional models tailored for visual data. Each chapter emphasizes algorithmic intuition, practical evaluation metrics, and real-world use cases from medical imaging and remote sensing to autonomous systems and industrial inspection.

Designed for graduate students, researchers, data scientists, and engineering professionals, this 1st Edition offers a structured pathway from fundamental principles to advanced applications. Its balanced presentation supports classroom instruction, independent study, and professional reference, and is relevant to practitioners across North America, Europe, Asia-Pacific and beyond.

If you seek a reliable, up-to-date resource to master image processing and machine learning techniques, this volume delivers the theory, examples, and context necessary to accelerate your projects and research. Secure a copy today to deepen your expertise and apply cutting-edge visual intelligence tools to real-world problems — order Image Processing and Machine Learning, Volume 1 now.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.