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» Learning Boosted Asymmetric Classifiers for Object Detection
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ICVGIP
2008
13 years 9 months ago
Implementation of the "Local Rank Differences" Image Feature Using SIMD Instructions of CPU
Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend. The computatiponal perfor...
Adam Herout, Pavel Zemcík, Roman Jurá...
ACIVS
2008
Springer
14 years 1 months ago
"Local Rank Differences" Image Feature Implemented on GPU
A currently popular trend in object detection and pattern recognition is usage of statistical classifiers, namely AdaBoost and its modifications. The speed performance of these cla...
Lukás Polok, Adam Herout, Pavel Zemcí...
ICIP
2008
IEEE
14 years 9 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
IVC
2006
187views more  IVC 2006»
13 years 7 months ago
Dynamics of facial expression extracted automatically from video
We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions, including AdaBoost, support vector mach...
Gwen Littlewort, Marian Stewart Bartlett, Ian R. F...
ICCV
2007
IEEE
14 years 1 months ago
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...