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» Unsupervised Learning of Invariant Features Using Video
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PAMI
2008
302views more  PAMI 2008»
13 years 8 months ago
Learning to Detect Moving Shadows in Dynamic Environments
We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a stat...
Ajay J. Joshi, Nikolaos Papanikolopoulos
PAMI
2007
101views more  PAMI 2007»
13 years 8 months ago
A Thousand Words in a Scene
— This paper presents a novel approach for visual scene modeling and classification, investigating the combined use of text modeling methods and local invariant features. Our wo...
Pedro Quelhas, Florent Monay, Jean-Marc Odobez, Da...
ECCV
2008
Springer
14 years 10 months ago
Learning Visual Shape Lexicon for Document Image Content Recognition
Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content catego...
Guangyu Zhu, Xiaodong Yu, Yi Li, David S. Doermann
ECCV
2002
Springer
14 years 10 months ago
Robust Parameterized Component Analysis
Principal ComponentAnalysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the var...
Fernando De la Torre, Michael J. Black
ICIP
2005
IEEE
14 years 10 months ago
Eigenwalks: walk detection and biometrics from symmetry patterns
In this paper we present a symmetry-based approach which can be used to detect humans and to extract biometric characteristics from video image-sequences. The method employs a simp...
Laszlo Havasi, Tamás Szirányi, Zolt&...