Practicing vision: Integration, evaluation and applications

Abstract Computer vision has emerged as a challenging and important area of research, both as an engineering and a scientific discipline. The growing importance of computer vision is evident from the fact that it was identified as one of the ″Grand Challenges″ and also from its prominent role in the National Information Infrastructure. While the design of a general purpose vision system continues to be elusive, machine vision systems are being used successfully in specific application domains. Building a practical vision system requires a careful selection of appropriate sensors, extraction and integration of information from available cues in the sensed data, and evaluation of system robustness and performance. We discuss and demonstrate advantages of (i) multi-sensor fusion, (ii) combination of features and classifiers, (iii) integration of visual modules, and (iv) admissibility and goal-directed evaluation of vision algorithms. The requirements of several prominent real world applications such as biometry, document image analysis, image and video database retrieval, and automatic object model construction offer exciting problems and new opportunities to design and evaluate vision algorithms.