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SCIA
2007
Springer
114views Image Analysis» more  SCIA 2007»
14 years 2 months ago
Object Recognition Using Frequency Domain Blur Invariant Features
In this paper, we propose novel blur invariant features for the recognition of objects in images. The features are computed either using the phase-only spectrum or bispectrum of th...
Ville Ojansivu, Janne Heikkilä
GECCO
2006
Springer
171views Optimization» more  GECCO 2006»
14 years 13 days ago
Evolving ensemble of classifiers in random subspace
Various methods for ensemble selection and classifier combination have been designed to optimize the results of ensembles of classifiers. Genetic algorithm (GA) which uses the div...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...
PAMI
2008
250views more  PAMI 2008»
13 years 8 months ago
Combined Top-Down/Bottom-Up Segmentation
We construct an image segmentation scheme that combines top-down (TD) with bottom-up (BU) processing. In the proposed scheme, segmentation and recognition are intertwined rather th...
Eran Borenstein, Shimon Ullman
HIS
2008
13 years 10 months ago
Learning Spatial Grammars for Drawn Documents Using Genetic Algorithms
The problem of object recognition may be cast into a spatial grammar framework. This system comprises three novel elements: a spatial organisation of line features, an efficient t...
Simon J. Hickinbotham, Anthony G. Cohn
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 2 months ago
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure o...
Yung-Keun Kwon, Byung Ro Moon