Artificial Intelligence
Automatic partitioning of full-motion video
Multimedia Systems
Sequential Updating of Projective and Affine Structure from Motion
International Journal of Computer Vision
A review of biologically motivated space-variant data reduction models for robotic vision
Computer Vision and Image Understanding
Threading Fundamental Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Active Camera Control and Camera Motion Recovery with Foveate Wavelet Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Perception
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
Automatic object extraction and reconstruction in active video
Automatic object extraction and reconstruction in active video
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient MPEG compressed video analysis using macroblock typeinformation
IEEE Transactions on Multimedia
A survey of motion-parallax-based 3-D reconstruction algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
Automatic Detection of Object of Interest and Tracking in Active Video
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Automatic Detection of Object of Interest and Tracking in Active Video
Journal of Signal Processing Systems
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A new method of video object extraction is proposed to automatically extract the object of interest from actively acquired videos. Traditional video object extraction techniques often operate under the assumption of homogeneous object motion and extract various parts of the video that are motion consistent as objects. In contrast, the proposed active video object extraction (AVOE) approach assumes that the object of interest is being actively tracked by a non-calibrated camera under general motion and classifies the possible movements of the camera that result in the 2D motion patterns as recovered from the image sequence. Consequently, the AVOE method is able to extract the single object of interest from the active video. We formalize the AVOE process using notions from Gestalt psychology. We define a new Gestalt factor called ''shift and hold'' and present 2D object extraction algorithms. Moreover, since an active video sequence naturally contains multiple views of the object of interest, we demonstrate that these views can be combined to form a single 3D object regardless of whether the object is static or moving in the video.