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CVPR
2012
IEEE
13 years 6 months ago
A learning-based framework for depth ordering
Depth ordering is instrumental for understanding the 3D geometry of an image. We as humans are surprisingly good ordering even with abstract 2D line drawings. In this paper we pro...
Zhaoyin Jia, Andrew C. Gallagher, Yao-Jen Chang, T...
SCALESPACE
2007
Springer
15 years 10 months ago
Segmentation Under Occlusions Using Selective Shape Prior
In this work, we address the problem of segmenting multiple objects, under possible occlusions, in a level set framework. A variational energy that incorporates a piecewise constan...
Sheshadri Thiruvenkadam, Tony F. Chan, Byung-Woo H...
ICCV
2001
IEEE
16 years 5 months ago
Beyond Lambert: Reconstructing Surfaces with Arbitrary BRDFs
W e address an open and hitherto neglected problem in computer vision, how to reconstruct the geometry of objects with arbitrary and possibly anisotropic bidirectional reflectance...
Sebastian Magda, David J. Kriegman, Todd Zickler, ...
TVCG
2002
109views more  TVCG 2002»
15 years 3 months ago
Robust Creation of Implicit Surfaces from Polygonal Meshes
Implicit surfaces are used for a number of tasks in computer graphics, including modeling soft or organic objects, morphing, collision detection, and constructive solid geometry. A...
Gary D. Yngve, Greg Turk
135
Voted
CVPR
2008
IEEE
16 years 5 months ago
The Logistic Random Field - A convenient graphical model for learning parameters for MRF-based labeling
Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valu...
Marshall F. Tappen, Kegan G. G. Samuel, Craig V. D...