Object Detection and Recognition in Digital Images: Theory and Practice

Object Detection and Recognition in Digital Images addresses key problems of CV focusing on the significant issues of object detection, tracking, and recognition in images, which are not easily found in other CV books. Chapters included are dedicated to tensor methods in computer vision, classification methods and algorithms, detection and tracking, and recognition. Throughout the book content is balanced between theory, implementation and case studies in order to provide a complete and accessible treatment of the topic. Explanations of the main theoretical ideas behind each method are provided for a thorough understanding which are augmented with a rigorous mathematical derivation of the formulas, and demonstrated working in real applications. Featured case studies focus on practical applications of the discussed methods including problems of skin and face detection, pedestrian detection, road signs recognition, hand gesture recognition, blind people navigation, and traffic surveillance.

• Discusses the most significant and up-to-date methods of object detection and recognition.
• Offers a balanced treatment between theory, implementation (mainly C++ of crucial algorithms, with some algorithms in MATLAB) and case studies detailing practical applications of the discussed methods.
• Companion website hosting software, example data files, test images, colour images, as well as additional material such as software manual, and solutions to exercises.

Keywords: Signal Processing, skin detection, face detection, pedestrian detection, road signs recognition, hand gesture recognition, blind people navigation, traffic surveillance, computer vision, object recognition, digital image recognition, machine vision