Quadrilateral-based region segmentation for tracking

We propose a novel quadrilateral based region segmentation method that is favorable for object tracking. Instead of using groups of pixels or regular blocks, it uses groups of connected quadrilaterals to represent regions. The proposed method derives the vertices of each quadrilateral from the edge map using the concept of center of masses. By merging the quadrilaterals, regions can be represented. The proposed method offers better data reduction than pixelwise region representation and better boundary approximation than block-based segmentation methods. Experimental results show that it generates a more reasonable region map, which is more suitable for object tracking, and a smaller number of regions than the seeded region growing, K-means clustering, and constrained gravitational clustering methods. © 2002 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Video object segmentation for interactive multimedia

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 67-74.Recently, trends in video processing research have shifted from video compression to video analysis, due to the emerging standards MPEG-4 and MPEG-7. These standards will enable the users to interact with the objects in the audiovisual scene generated at the user’s end. However, neither of them prescribes how to obtain the objects. Many methods have been proposed for segmentation of video objects. One of the approaches is the “Analysis Model” (AM) of European COST-211 project. It is a modular approach to video object segmentation problem. Although AM performs acceptably in some cases, the results in many other cases are not good enough to be considered as semantic objects. In this thesis, a new tool is integrated and some modules are replaced by improved versions. One of the tools uses a block-based motion estimation technique to analyze the motion content within a scene, computes a motion activity parameter, and skips frames accordingly. Also introduced is a powerful motion estimation method which uses maximum a posteriori probability (MAP) criterion and Gibbs energies to obtain more reliable motion vectors and to calculate temporally unpredictable areas. To handle more complex motion in the scene, the 2-D affine motion model is added to the motion segmentation module, which employs only the translational model. The observed results indicate that the AM performance is improved substantially. The objects in the scene and their boundaries are detected more accurately, compared to the previous results.Ekmekçi, TolgaM.S