A knowledge-based component library for high-level computer vision tasks

Computer vision is an interdisciplinary field that includes methods for acquiring, processing, analyzing, and understanding visual information. In computer vision, usually the approaches to solve problems are specific-application methods and, therefore, reusing captured knowledge in computer vision is hard. However, the aim of knowledge modeling (KM) is to capture and reuse knowledge to solve different problems. In this paper, we propose a knowledge-based component library for computer vision tasks such as video surveillance applications. The developed components are based on the region of interest (ROI) a well-known concept in the image processing and computer vision fields. We provide a set of reusable components that are specializations and/or compositions of ROIs. Finally, we propose several case studies that illustrate the feasibility of the proposal. Experimental results show that the proposed method deals effectively and efficiently with real-life computer vision problems.