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CVPR
2009
IEEE
1863views Computer Vision» more  CVPR 2009»
15 years 5 months ago
Abnormal Crowd Behavior Detection using Social Force Model
In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed ove...
Alexis Oyama, Mubarak Shah, Ramin Mehran
CVPR
2009
IEEE
15 years 5 months ago
Global Optimization for Alignment of Generalized Shapes
In this paper, we introduce a novel algorithm to solve global shape registration problems. We use gray-scale “images” to represent source shapes, and propose a novel twocompo...
Hongsheng Li (Lehigh University), Tian Shen (Lehig...
CVPR
2009
IEEE
15 years 5 months ago
Unsupervised Learning for Graph Matching
Graph matching is an important problem in computer vision. It is used in 2D and 3D object matching and recognition. Despite its importance, there is little literature on learnin...
Marius Leordeanu, Martial Hebert
CVPR
2009
IEEE
15 years 5 months ago
Max-Margin Hidden Conditional Random Fields for Human Action Recognition
We present a new method for classification with structured latent variables. Our model is formulated using the max-margin formalism in the discriminative learning literature. We...
Yang Wang 0003, Greg Mori
CVPR
2009
IEEE
15 years 5 months ago
Rank Priors for Continuous Non-Linear Dimensionality Reduction
Non-linear dimensionality reductionmethods are powerful techniques to deal with high-dimensional datasets. However, they often are susceptible to local minima and perform poorly ...
Andreas Geiger (Karlsruhe Institute of Technology)...
CVPR
2009
IEEE
15 years 5 months ago
Unsupervised Maximum Margin Feature Selection with Manifold Regularization
Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the supervised scenario, while unsupervised feature s...
Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui ...
CVPR
2009
IEEE
15 years 5 months ago
Learning a Distance Metric from Multi-instance Multi-label Data
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. ...
Rong Jin (Michigan State University), Shijun Wang...
CVPR
2009
IEEE
15 years 5 months ago
Global Connectivity Potentials for Random Field Models
Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions a...
Sebastian Nowozin, Christoph H. Lampert
CVPR
2009
IEEE
15 years 5 months ago
On the Set of Images Modulo Viewpoint and Contrast Changes
We consider regions of images that exhibit smooth statistics, and pose the question of characterizing the “essence” of these regions that matters for visual recognition. Ideal...
Ganesh Sundaramoorthi (UCLA), Peter Petersen (UCLA...
CVPR
2009
IEEE
15 years 5 months ago
Fast Multiple Shape Correspondence by Pre-Organizing Shape Instances
Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shapecorrespondenc...
Andrew Temlyakov, Brent C. Munsell, Song Wang