Machine vision. Theory. Algorithms. Practicalities

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In the past 40 years, machine vision has matured into a crucial field with diverse applications such as surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring, signature verification, biometric measurement, and analysis of remotely sensed images. As the body of research grows, it has become challenging for professionals and graduate students to grasp the essential theory and practicalities necessary for designing their own algorithms and systems. This book addresses this gap by presenting the fundamental concepts in an accessible manner, combining theory with algorithmic and practical design constraints. The updated edition organizes the content into horizontal levels of a complete machine vision system, featuring application case studies that highlight specific techniques and design constraints for real-world systems. It includes comprehensive coverage of 2-D and 3-D scene analysis, a thorough treatment of the Hough Transform—a vital technique for inspection and surveillance—and integrates essential topics and techniques into a cohesive system design approach. Additionally, it emphasizes the necessity for real-time processing in practical applications, ensuring that readers can effectively apply the concepts to their work.