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CVPR
2003
IEEE
16 years 12 hour ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
Robert Fergus, Pietro Perona, Andrew Zisserman
CVPR
2010
IEEE
15 years 6 months ago
The chains model for detecting parts by their context
Detecting an object part relies on two sources of information - the appearance of the part itself, and the context supplied by surrounding parts. In this paper we consider problem...
Leonid Karlinsky, Michael Dinerstein, Daniel Harar...
ICIP
2010
IEEE
14 years 8 months ago
Exemplar-Based EM-like image denoising via manifold reconstruction
Discovering local geometry of low-dimensional manifold embedded into a high-dimensional space has been widely studied in the literature of machine learning. Counter-intuitively, w...
Xin Li
ICIP
1997
IEEE
15 years 11 months ago
SVD and log-log frequency sampling with Gabor kernels for invariant pictorial recognition
This paper presents an e cient scheme for a neinvariant object recognition. A ne invariance is obtained by a representation which is based on a new sampling con guration in the fr...
Zhiqian Wang, Jezekiel Ben-Arie
ICMLA
2009
14 years 7 months ago
Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
Johannes Hartz