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BMCBI
2004
208views more  BMCBI 2004»
15 years 1 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
CVPR
2003
IEEE
16 years 3 months ago
3D Object Modeling and Recognition Using Affine-Invariant Patches and Multi-View Spatial Constraints
: This paper presents a novel representation for three-dimensional objects in terms of affine-invariant image patches and their spatial relationships. Multi-view constraints associ...
Fred Rothganger, Svetlana Lazebnik, Cordelia Schmi...
CVPR
2010
IEEE
14 years 10 months ago
Global and local isometry-invariant descriptor for 3D shape comparison and partial matching
In this paper, based on manifold harmonics, we propose a novel framework for 3D shape similarity comparison and partial matching. First, we propose a novel symmetric meanvalue rep...
Huai-Yu Wu, Hongbin Zha, Tao Luo, Xulei Wang, Song...
BMVC
2010
14 years 11 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
ICCV
2007
IEEE
15 years 7 months ago
Scale-Dependent 3D Geometric Features
Three-dimensional geometric data play fundamental roles in many computer vision applications. However, their scale-dependent nature, i.e. the relative variation in the spatial ext...
John Novatnack, Ko Nishino