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BMVC
2010
13 years 5 months ago
Back to the Future: Learning Shape Models from 3D CAD Data
Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of o...
Michael Stark, Michael Goesele, Bernt Schiele
ECCV
2002
Springer
14 years 9 months ago
Learning Shape from Defocus
We present a novel method for inferring three-dimensional shape from a collection of defocused images. It is based on the observation that defocused images are the null-space of ce...
Paolo Favaro, Stefano Soatto
MVA
1994
224views Computer Vision» more  MVA 1994»
13 years 9 months ago
3D Object Model Fitting to Still Images Using Linear Combination Method of 2D Aspect Images
This paper describes a method for fitting 3D object model to still (single) 2D observed image by searching for the model's optimum posture parameters in the parameter space t...
Hiroyasu Sakamoto, Masahide Kawakami
ICCV
2005
IEEE
14 years 9 months ago
Eliminating Structure and Intensity Misalignment in Image Stitching
The aim of this paper is to achieve seamless image stitching for eliminating obvious visual artifact caused by severe intensity discrepancy, image distortion and structure misalig...
Jiaya Jia, Chi-Keung Tang
IJPRAI
2002
97views more  IJPRAI 2002»
13 years 7 months ago
Shape Description and Invariant Recognition Employing Connectionist Approach
This paper presents a new approach for shape description and invariant recognition by geometric-normalization implemented by neural networks. The neural system consists of a shape...
Jezekiel Ben-Arie, Zhiqian Wang