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» Probabilistic Scene Models for Image Interpretation
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CVPR
2011
IEEE
14 years 1 months ago
Effective 3D Object Detection and Regression Using Probabilistic Segmentation Features in CT Images
3D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical images of computer aided diagnosis (CAD). In this paper, we propose ...
Le Lu, Jinbo Bi, Matthias Wolf, Marcos Salganicoff
TIFS
2010
149views more  TIFS 2010»
14 years 4 months ago
Detecting Forgery From Static-Scene Video Based on Inconsistency in Noise Level Functions
Recently developed video editing techniques have enabled us to create realistic synthesized videos. Therefore, using video data as evidence in places such as courts of law requires...
Michihiro Kobayashi, Takahiro Okabe, Yoichi Sato
PAMI
2012
13 years 5 days ago
Holistic Context Models for Visual Recognition
— A novel framework to context modeling, based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availabili...
Nikhil Rasiwasia, Nuno Vasconcelos
PAMI
2007
101views more  PAMI 2007»
14 years 9 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
2002
Springer
15 years 11 months ago
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image...
Feng Han, Zhuowen Tu, Song Chun Zhu