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

Coarctation

書目名稱Computer Vision -- ECCV 2010影響因子(影響力)




書目名稱Computer Vision -- ECCV 2010影響因子(影響力)學(xué)科排名




書目名稱Computer Vision -- ECCV 2010網(wǎng)絡(luò)公開度




書目名稱Computer Vision -- ECCV 2010網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision -- ECCV 2010被引頻次




書目名稱Computer Vision -- ECCV 2010被引頻次學(xué)科排名




書目名稱Computer Vision -- ECCV 2010年度引用




書目名稱Computer Vision -- ECCV 2010年度引用學(xué)科排名




書目名稱Computer Vision -- ECCV 2010讀者反饋




書目名稱Computer Vision -- ECCV 2010讀者反饋學(xué)科排名

glisten

Sequential Non-Rigid Structure-from-Motion with the 3D-Implicit Low-Rank Shape Modelsition takes place. In this paper we propose an incremental approach to the estimation of deformable models. Image frames are processed online in a sequential fashion. The shape is initialised to a rigid model from the first few frames. Subsequently, the problem is formulated as a model based camera

寬大

Spirometry

樹木心

stress-response

Euclidean Structure Recovery from Motion in Perspective Image Sequences via Hankel Rank Minimizationtive projection. Existing approaches rely either only on geometrical constraints reflecting the rigid nature of the object, or exploit temporal information by recasting the problem into a nonlinear filtering form. In contrast, here we introduce a new constraint that implicitly exploits the . of the

stress-response

Exploiting Loops in the Graph of Trifocal Tensors for Calibrating a Network of Cameras epipolar geometries, a parameterization of the graph of trifocal tensors is proposed in which each trifocal tensor is encoded by a 4-vector. The strength of this parameterization is that the homographies relating two adjacent trifocal tensors, as well as the projection matrices depend linearly on t

reaching

浪費(fèi)時(shí)間

Conjugate Gradient Bundle Adjustmentmputationally very expensive. An alternative to this approach is to apply the conjugate gradients algorithm in the inner loop. This is appealing since the main computational step of the CG algorithm involves only a simple matrix-vector multiplication with the Jacobian. In this work we improve on the

盤旋

NF-Features – No-Feature-Features for Representing Non-textured Regionsint detectors do not detect features. As these regions are usually non-textured, stable re-localization in different images with conventional methods is not possible. Therefore, a technique is presented which re-localizes once-detected NF-features using correspondences of regular features. Furthermo