Alex Levinshtein, Cristian Sminchisescu, Sven Dickinson  Home/Search
Context
Related
| toronto.edu/~sven/...r2005learning.pdf Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: toronto.edu/~sven/publications (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: We present an algorithm for automatically constructing a decompositional shape model from examples. Unlike current approaches to structural model acquisition, in which one-to-one correspondences among appearance-based features are used to construct an exemplar-based model, we search for many-tomany correspondences among qualitative shape features (multi-scale ridges and blobs) to construct a generic shape model. Since such features are highly ambiguous, their structural context must be... (Update)
Active bibliography (related documents): More All
0.5: Unsupervised Learning of Dense Hierarchical Appearance.. - Scalzo, Piater (Correct)
0.5: Investigating the effects of scale in MRF texture.. - Scott Blunsden Louis (Correct)
0.5: Pedestrian Detection Using Derived Third-Order Symmetry.. - Havasi, Szlavik.. (2004) (Correct)
Similar documents based on text: More All
0.3: Incremental Model-Based Estimation Using Geometric.. - Sminchisescu.. (2001) (Correct)
0.2: Incremental Model-Based Estimation Using Geometric.. - Sminchisescu, Metaxas.. (2005) (Correct)
0.2: Estimating Articulated Human Motion With Covariance Scaled.. - Sminchisescu, Triggs (2003) (Correct)
BibTeX entry: (Update)
@misc{ levinshtein-learning,
author = "Alex Levinshtein and Cristian Sminchisescu and Sven Dickinson",
title = "Learning Hierarchical Shape Models from Examples",
url = "citeseer.ist.psu.edu/levinshtein05learning.html" }
Citations (may not include all citations):208 Approximating discrete probability distributions with depend.. (context) - Chow, Liu - 1968
184 A Global Geometric Framework for Nonlinear Dimensionality Re.. (context) - Tenenbaum, de Silva et al. - 2000
164 Representation and recognition of the spatial organization o.. (context) - Nishihara - 1978
75 A metric for distributions with applications to image databa.. (context) - Rubner, Tomasi et al. - 1998
52 Model-Based Three Dimensional Interpretations of Two Dimensi.. (context) - Brooks - 1983
50 Object Class Recognition by Unsupervised ScaleInvariant Lear.. - Fergus, Perona et al. - 2003
20 Efficient Matching of Pictorial Structures - Felzenszwalb, Huttenlocher - 2000
20 Detecting Pedestrians Using Patterns of Motion and Appearanc.. - Viola, Jones et al. - 2003
19 A Bayesian Approach to Unsupervised One-Shot Learning of Obj.. (context) - Fei-Fei, Fergus et al. - 2003
18 On Embedding Trees into Uniformly Convex Banach Spaces - Matousek - 1999
16 Human tracking with mixtures of trees - Ioffe, Forsyth - 2001
12 The Earth Mover's Distance under Transformation Sets - Cohen, Guibas - 1999
11 Finding and Tracking People From the Bottom Up - Ramanan, Forsyth - 2003
10 Unsupervised Learning of Human Motion - Song, Goncalves et al. - 2003
6 Many-to-Many Feature Matching Using Spherical Coding of Dire.. - Demirci, Shokoufandeh et al. - 2004
4 A layered motion representation with occlusion and compact s.. - Jepson, Fleet et al. - 2002
3 Using Temporal Coherence to Build Models of Animals - Ramanan, Forsyth - 2003
2 Semi-Local Affine Parts for Object Recognition - Lazebnik, Schmid et al. - 2004
2 Real-time scale selection in hybrid multi-scale representati.. - Lindeberg, Bretzner - 2003
Documents on the same site (http://www.cs.toronto.edu/~sven/publications.html): More
Recognition by Functional Parts - Rivlin, Dickinson, al. (1995) (Correct)
Improving the Scope of Deformable Model Shape and.. - Sminchisescu.. (2001) (Correct)
Spectral Methods for View-Based 3-D Object.. - Macrini.. (2002) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC