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Titlebook: Computer Vision -- ECCV 2010; 11th European Confer Kostas Daniilidis,Petros Maragos,Nikos Paragios Conference proceedings 2010 Springer-Ver

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發(fā)表于 2025-3-23 10:09:37 | 只看該作者
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發(fā)表于 2025-3-23 15:44:34 | 只看該作者
3D Reconstruction of a Moving Point from a Series of 2D Projections images, given the 3D spatial pose and time of capture of the cameras that produced each image. Triangulation-based solutions do not apply, as multiple views of the point may not exist at each instant in time. A geometric analysis of the problem is presented and a criterion, called reconstructibilit
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發(fā)表于 2025-3-23 18:58:24 | 只看該作者
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發(fā)表于 2025-3-23 22:51:22 | 只看該作者
Detection and Tracking of Large Number of Targets in Wide Area Surveillanceveral challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data and low framerate of video. We propose a method that overcomes these challenges and evaluate it on CLIF dataset. We use median background modeling which requi
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發(fā)表于 2025-3-24 04:42:04 | 只看該作者
Discriminative Tracking by Metric Learningminative models for visual tracking are formulated as supervised learning of binary classifiers. The continuous output of the classification function is then utilized as the cost function for visual tracking. This may be less desirable since the function is optimized for making binary decision. Such
16#
發(fā)表于 2025-3-24 08:22:49 | 只看該作者
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發(fā)表于 2025-3-24 11:25:20 | 只看該作者
3D Deformable Face Tracking with a Commodity Depth Cameramages in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input de
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發(fā)表于 2025-3-24 16:34:38 | 只看該作者
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發(fā)表于 2025-3-24 20:30:14 | 只看該作者
20#
發(fā)表于 2025-3-25 01:41:59 | 只看該作者
The Diplomacies of Small Stateswe retain its best candidates according to appearance. We then prune the set of potential matches by iteratively shrinking the regions of the image that are consistent with the geometric prior. We can then successfully compute homographies between pairs of images containing highly repetitive patterns and even under oblique viewing conditions.
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