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

CANE

engrave

激怒

Fully Isotropic Fast Marching Methods on Cartesian Gridstruly isotropic. We show the significance of removing the directional bias in the computation of the cost in certain applications of fast marching method. We also compare the accuracy and computation times of our proposed methods with the existing state of the art fast marching techniques to demonstrate the superiority of our method.

Banquet

Descattering Transmission via Angular Filteringure angularly varying scattered light and compute the unscattered direct component. We capture the scattering from a single light source via a lenslet array placed close to the image plane. As an application, we demonstrate enhanced tomographic reconstruction of scattering objects using estimated direct transmission images.

罗盘

Flexible Voxels for Motion-Aware Videographysible to capture fast moving objects without motion blur, while simultaneously preserving high-spatial resolution for static scene parts within the same video sequence. Our sampling scheme requires a fast per-pixel shutter on the sensor-array, which we have implemented using a co-located camera-projector system.

做事过头

Learning PDEs for Image Restoration via Optimal Controlhnique. The effectiveness of our L-PDE framework for image restoration is demonstrated with two exemplary applications: image denoising and inpainting, where the PDEs are obtained easily and the produced results are comparable to or better than those of traditional PDEs, which were elaborately designed.

uveitis

We Are Family: Joint Pose Estimation of Multiple Personsd occlude each other. In a comprehensive evaluation on a new, challenging group photo datasets we demonstrate the benefits of our multi-person model over a state-of-the-art single-person pose estimator which treats each person independently.

大量杀死

NATTY

irradicable

Stigma and Interpersonal Relationshipsknown tags in a semi-supervised manner. We conducted experiments on two well-known datasets and obtained significant performance improvements in both people and location recognition. We also demonstrated the ability to infer event labels with missing timestamps (i.e. with no event features).