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Minimum condition number (CN) gradient encoding scheme was introduced to diffusion MRI community more than a decade ago. It's computation requires tedious numerical optimization which usually leads to sub-optimal solutions. The CN does not reflect any benefits in acquiring more measurements, i.e. it's optimal value is constant for any number of measurements. Further, it is variable under rotation...
This paper addresses issues of online learning and occlusion handling in video object tracking. Although manifold tracking is promising, large pose changes and long-term partial occlusions of video objects remain challenging. We propose a novel manifold tracking scheme that tackles such problems, with the following main novelties: (a) Online estimation of object appearances on Grassmann manifolds;...
We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color...
We propose a novel tracking scheme that jointly employs point feature correspondences and object appearance similarity. For selecting point correspondences, we use a subset of scale-invariant point features from SIFT that agree with a pre-defined affine transformation. The selected consensus points are then used for pre-selecting candidate regions. For appearance similarity based tracking, we employ...
Efficiency and robustness are the two most important issues for multiobject tracking algorithms in real-time intelligent video surveillance systems. We propose a novel 2.5-D approach to real-time multiobject tracking in crowds, which is formulated as a maximum aposteriori estimation problem and is approximated through an assignment step and a location step. Observing that the occluding object is...
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