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» Unsupervised Learning of Object Deformation Models
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CVPR
2008
IEEE
14 years 9 months ago
Structure-perceptron learning of a hierarchical log-linear model
In this paper, we address the problems of deformable object matching (alignment) and segmentation with cluttered background. We propose a novel hierarchical log-linear model (HLLM...
Long Zhu, Yuanhao Chen, Xingyao Ye, Alan L. Yuille
CVPR
2008
IEEE
14 years 9 months ago
Unsupervised modeling of object categories using link analysis techniques
We propose an approach for learning visual models of object categories in an unsupervised manner in which we first build a large-scale complex network which captures the interacti...
Gunhee Kim, Christos Faloutsos, Martial Hebert
ACMSE
2006
ACM
14 years 1 months ago
HELLAS: a specialized architecture for interactive deformable object modeling
Applications involving interactive modeling of deformable objects require highly iterative, floating-point intensive numerical simulations. As the complexity of these models incr...
Shrirang M. Yardi, Benjamin Bishop, Thomas P. Kell...
ICCV
2011
IEEE
12 years 7 months ago
Scene Recognition and Weakly Supervised Object Localization with Deformable Part-Based Models
Weakly supervised discovery of common visual structure in highly variable, cluttered images is a key problem in recognition. We address this problem using deformable part-based mo...
Megha Pandey, Svetlana Lazebnik
CVPR
2010
IEEE
14 years 3 months ago
Cascade Object Detection with Deformable Part Models
We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured mod...
Pedro Felzenszwalb, Ross Girshick, David McAlleste...