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Titlebook: Computer Vision - ACCV 2010; 10th Asian Conferenc Ron Kimmel,Reinhard Klette,Akihiro Sugimoto Conference proceedings 2011 Springer Berlin H

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11#
發(fā)表于 2025-3-23 11:25:54 | 只看該作者
12#
發(fā)表于 2025-3-23 17:11:31 | 只看該作者
13#
發(fā)表于 2025-3-23 19:33:09 | 只看該作者
14#
發(fā)表于 2025-3-24 00:07:47 | 只看該作者
Towards Full 3D Helmholtz Stereovision Algorithmsrocess. More precisely, we use a triangular mesh representation which allows to naturally specify relationships between the geometry of a point of the scene and its surface normal. We show how to implement the presented approach using a coherent gradient descent flow. Results and benefits are illustrated on various examples.
15#
發(fā)表于 2025-3-24 06:19:13 | 只看該作者
Network Connectivity via Inference over Curvature-Regularizing Line Graphsmical connectivity is then posed as an optimization problem over this curvature-regularizing graph – which gives subgraphs which comprise a representation of the tracts’ network topology. We present experimental results and an open-source implementation of the algorithm.
16#
發(fā)表于 2025-3-24 07:29:50 | 只看該作者
17#
發(fā)表于 2025-3-24 14:10:56 | 只看該作者
18#
發(fā)表于 2025-3-24 15:25:51 | 只看該作者
Context-Based Support Vector Machines for Interconnected Image Annotationrent links and (ii) the proof of convergence of our kernel to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.
19#
發(fā)表于 2025-3-24 20:10:27 | 只看該作者
Finding Human Poses in Videos Using Concurrent Matching and Segmentationces and a human body plan, and the body part configuration is consistent with the object foreground estimated by simultaneous superpixel labeling. Our experiments on a variety of videos show that the proposed method is efficient and more reliable than previous locally constrained approaches.
20#
發(fā)表于 2025-3-25 01:47:54 | 只看該作者
Teacher Training and the Learner significant speedup as our new solvers are about 40× (non-planar) and 160× (planar) faster than the general solver. Moreover, we show that our two solvers can be joined into a new general solver, which gives comparable or better results than the existing general solver for of most planar as well as non-planar scenes.
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