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    CVPR
    2005
    IEEE
    217views Computer Vision» more  CVPR 2005»
    Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
    15 years 11 months ago
    Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
    Download  www.cs.sunysb.edu
    We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The fir...
    Wei Zhang 0002, Bing Yu, Gregory J. Zelinsky, Dimi...
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    NIPS
    2000
    152views Information Technology» more  NIPS 2000»
    The Manhattan World Assumption: Regularities in Scene Statistics which Enable Bayesian Inference
    14 years 10 months ago
    The Manhattan World Assumption: Regularities in Scene Statistics which Enable Bayesian Inference
    Download  www.stat.ucla.edu
    Preliminary work by the authors made use of the so-called "Manhattan world" assumption about the scene statistics of city and indoor scenes. This assumption stated that ...
    James M. Coughlan, Alan L. Yuille
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    ICCV
    2007
    IEEE
    173views Computer Vision» more  ICCV 2007»
    Latent Model Clustering and Applications to Visual Recognition
    15 years 3 months ago
    Latent Model Clustering and Applications to Visual Recognition
    Download  www.cs.huji.ac.il
    We consider clustering situations in which the pairwise affinity between data points depends on a latent ”context” variable. For example, when clustering features arising fro...
    Simon Polak, Amnon Shashua
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    FIMH
    2009
    Springer
    223views Medical Imaging» more  FIMH 2009»
    Discriminative Joint Context for Automatic Landmark Set Detection from a Single Cardiac MR Long Axis Slice
    14 years 7 months ago
    Discriminative Joint Context for Automatic Landmark Set Detection from a Single Cardiac MR Long Axis Slice
    Download  www.msu.edu
    Cardiac magnetic resonance (MR) imaging has advanced to become a powerful diagnostic tool in clinical practice. Automatic detection of anatomic landmarks from MR images is importan...
    Xiaoguang Lu, Bogdan Georgescu, Arne Littmann, Edg...
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    ICMLA
    2009
    169views Machine Learning» more  ICMLA 2009»
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    14 years 7 months ago
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    Download  www.ipb.uni-bonn.de
    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
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