Titlebook: Computer Vision – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V

VICT

堅(jiān)毅

Approximate Gaussian Mixtures for Large Scale Vocabulariess for image retrieval. It is a variant of expectation-maximization that can converge rapidly while dynamically estimating the number of components. We employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed an

不透明

Glossy

Joint Face Alignment with Non-parametric Shape Models as output. Our method is an extension of the recent localization method of Belhumeur . [1], which combines the output of local detectors with a non-parametric set of face shape models. We are inspired by the recent joint alignment method of Zhao . [20], which employs a modified Active Appearance Mo

東西

Discriminative Bayesian Active Shape Models) face model and a set of discriminant local detectors, one for each facial landmark. The patch responses can be embedded into a Bayesian inference problem, where the posterior distribution of the global warp is inferred in a ?maximum a posteriori (MAP) sense. However, previous formulations do not m

APRON

Patch Based Synthesis for Single Depth Image Super-Resolutione sensors measure depths with non-Gaussian noise and at lower starting resolutions than typical visible-light cameras. While patch based approaches for upsampling intensity images continue to improve, this is the first exploration of patching for depth images..We match against the height field of ea

obnoxious

Annotation Propagation in Large Image Databases via Dense Image Correspondenceaches rely on a corpus of training images where each pixel is labeled. However, for large image databases, pixel labels are expensive to obtain and are often unavailable. Furthermore, when classifying multiple images, each image is typically solved for independently, which often results in inconsist

把手

Numerically Stable Optimization of Polynomial Solvers for Minimal Problemsl problems, but also for finding stationary points for overdetermined problems. The state-of-the-art is based on the use of numerical linear algebra on the large but sparse coefficient matrix that represents the original equations multiplied with a set of monomials. The key observation in this paper

habile

獨(dú)裁政府

Group Tracking: Exploring Mutual Relations for Multiple Object Trackingbjects in mutual context with each other to benefit robust and accurate tracking. We introduce a unified framework to combine both Individual Object Models (IOMs) and Mutual Relation Models (MRMs). The MRMs consist of three components, the relational graph to indicate related objects, the mutual rel