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

CORD

3D Interest Point Detection via Discriminative Learningan preference, can be ill-equipped for handling the variety and subjectivity in human responses. Different tasks have different requirements for interest point detection; some tasks may necessitate high precision while other tasks may require high recall. Sometimes points with high curvature may be

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Pose Locality Constrained Representation for 3D Human Pose Reconstructioncal constraints is known to be non-convex and usually leads to difficulty in finding an optimal solution. An attractive alternative is to learn a prior model of the human body from a set of human pose data. In this paper, we develop a new approach, namely pose locality constrained representation (PL

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Multi Focus Structured Light for Recovering Scene Shape and Global Illuminationts the working volume of projector-camera systems and global illumination can induce large errors in shape estimates. In this paper, we develop an algorithm for scene recovery in the presence of both defocus and global light transport effects such as interreflections and sub-surface scattering. Our

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Debrief

SRA: Fast Removal of General Multipath for ToF Sensorsmoving this interference which has two main advantages. First, it allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques bas

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Sub-pixel Layout for Super-Resolution with Images in the Octic Groupg multiple images, transformed in the octic group, with a sensor of asymmetric sub-pixel layout increases the spatial sampling compared to a conventional sensor with a rectilinear grid of pixels and hence increases the image resolution. We further prove a theoretical bound for achieving well-posed s

Hormones

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognitionesting example contains a face image set captured from different poses, illuminations, expressions and resolutions. While several feature learning and dictionary learning methods have been proposed for image set based face recognition in recent years, most of them learn the features and dictionaries

Mnemonics

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