Titlebook: Computer Vision -- ECCV 2012. Workshops and Demonstrations; Florence, Italy, Oct Andrea Fusiello,Vittorio Murino,Rita Cucchiara Conference

直覺沒有

A Unified Energy Minimization Framework for Model Fitting in Depth functions are used to represent object models implicitly and a novel 3D chamfer matching based energy function is minimized by adjusting the generic projection matrix, which could be parameterized differently according to specific applications. Our proposed energy function takes the advantage of th

MURKY

Object Recognition Robust to Imperfect Depth Dataith images can greatly enhance the performance of many computer vision algorithms, yet degraded depth measurements (e.g., missing data) can also cause dramatic performance losses to levels below image-only algorithms. We propose a generic fusion model based on maximum likelihood estimates of fused i

冷漠

3D Object Detection with Multiple Kinects RGB cameras as well as depth information have been widely explored in computer vision there is surprisingly little recent work combining multiple cameras and depth information. Given the recent emergence of consumer depth cameras such as Kinect we explore how multiple cameras and active depth senso

PANEL

預(yù)防注射

Quality Assessment of Non-dense Image Correspondencescorrespondences point-wise and consider only correspondences that are actually estimated. They cannot evaluate the fact that some algorithms might leave important scene correspondences undetected - correspondences which might be vital for succeeding applications. Additionally, often the reference co

過于平凡

adipose-tissue

Sinus-Rhythm

On the Evaluation of Scene Flow Estimations with ground truth which have not allowed the development of general, reliable solutions. Hopefully, the renewed interest in dynamic 3D content, which has led to increased research in this area, will also lead to more rigorous evaluation and more effective algorithms. We begin by classifying method

侵略主義

Analysis of KITTI Data for Stereo Analysis with Stereo Confidence Measuresim is to test the value of stereo confidence measures (e.g. a left-right consistency check of disparity maps, or an analysis of the slope of a local interpolation of the cost function at the taken minimum) when applied to recorded datasets, such as published with KITTI. We choose popular measures as

Genistein

Lessons and Insights from Creating a Synthetic Optical Flow Benchmarkntrast to the well-known Middlebury dataset, the MPI-Sintel Flow dataset contains longer and more varied sequences with image degradations such as motion blur, defocus blur, and atmospheric effects. Animators use a variety of techniques that produce pleasing images but make the raw animation data in