Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

泥瓦匠

Schwarps: Locally Projective Image Warps Based on 2D Schwarzian Derivativesrp’s smoothness is to penalize its second order partial derivatives. Because this favors locally affine warps, this fails to capture the local projective component of the image deformation. This may have a negative impact on applications such as image registration and deformable 3D reconstruction. W

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Obituary

Generalized Connectivity Constraints for Spatio-temporal 3D Reconstructions by precomputing a geodesic shortest path tree on the occupancy likelihood. Connectivity of the final occupancy labeling is ensured with a set of linear constraints on the labeling function. In order to generalize the connectivity constraints from objects with genus 0 to an arbitrary genus, we dete

UTTER

Passive Tomography of Turbulence Strengthcal measure of local variations in the turbulent medium. It influences engineering decisions made in these domains. Turbulence strength (TS) also affects safety of aircraft and tethered balloons, and reliability of free-space electromagnetic relays. We show that it is possible to estimate TS, withou

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jagged

Improved Motion Invariant Deblurring through Motion Estimationnvariance is that, unlike other computational photographic techniques, it does not require pre-exposure velocity estimation in order to ensure numerically stable deblurring. Its disadvantage is that the invariance is only approximate - objects moving with non-zero velocity will exhibit artifacts in

RENIN

發(fā)誓放棄

STELL

Learning the Face Prior for Bayesian Face Recognitionons, expressions, aging, and occlusions in the wild. In this paper, we propose a new approach to learn the face prior for Bayesian face recognition. First, we extend Manifold Relevance Determination to learn the identity subspace for each individual automatically. Based on the structure of the learn

Efflorescent

Spatio-temporal Event Classification Using Time-Series Kernel Based Structured Sparsityon but for one problem. Features typically are confounded by alignment error in space and time. As a consequence, high-dimensional representations such as SIFT and Gabor features have been favored despite their much greater computational cost and potential loss of information. We propose a Kernel St