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CVPR
2005
IEEE
14 years 8 months ago
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
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...
ICAS
2009
IEEE
141views Robotics» more  ICAS 2009»
14 years 1 months ago
An Enhanced IMS Architecture Featuring Cross-Layer Monitoring and Adaptation Mechanisms
IP Multimedia Subsystem (IMS) entails novel business opportunities for pioneering and emerging multimedia services, such as IPTV and VoIP video call applications. However, this st...
Lemonia Boula, Harilaos Koumaras, Anastasios Kourt...
NECO
2008
101views more  NECO 2008»
13 years 6 months ago
Rapid Convergence to Feature Layer Correspondences
ll Text][Abstract] , May 1, 2008; 99 (5): 2496-2509.J Neurophysiol T. O. Sharpee, K. D. Miller and M. P. Stryker Natural Stimuli On the Importance of Static Nonlinearity in Estimat...
Jörg Lücke, Christian Keck, Christoph vo...
ICCV
2011
IEEE
12 years 6 months ago
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers ...
Matthew D. Zeiler, Graham W. Taylor, Rob Fergus
NOLISP
2005
Springer
13 years 11 months ago
MLP Internal Representation as Discriminative Features for Improved Speaker Recognition
Feature projection by non-linear discriminant analysis (NLDA) can substantially increase classification performance. In automatic speech recognition (ASR) the projection provided b...
Dalei Wu, Andrew C. Morris, Jacques C. Koreman