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Your search keyword '"González-Velasco, Horacio M."' showing total 12 results
Start Over Author "González-Velasco, Horacio M." Publication Type Books
12 results on '"González-Velasco, Horacio M."'

11. Comparing Feature Extraction Techniques and Classifiers in the Handwritten Letters Classification Problem.

Author

García-Manso, Antonio, García-Orellana, Carlos J., González-Velasco, Horacio M., Macías-Macías, Miguel, and Gallardo-Caballero, Ramón

Abstract

The aim of this study is to compare the performance of two feature extraction techniques, Independent Component Analysis (ICA) and Principal Component Analysis (PCA) and also to compare two different kinds of classifiers, Neural Networks and Support Vector Machine (SVM). To this aim, a system for handwritten letters recognition was developed, which consist of two stages: a feature extraction stage using either ICA or PCA, and a classifier based on neural networks or SVM. To test the performance of the system, the subset of uppercase letters of the NIST#19 database was used. From the results of our tests, it can be concluded that when a neural network is used as classifier, the results are very similar with the two feature extraction techniques (ICA and PCA). But when the SVM classifier is used, the results are quite different, performing better the feature extractor based on ICA. [ABSTRACT FROM AUTHOR]

Published
2010
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12. A Method for Interactive Shape Detection in Cattle Images Using Genetic Algorithms.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Kropatsch, Walter G., Kampel, Martin, Hanbury, Allan, González-Velasco, Horacio M., and García-Orellana, Carlos J.

Abstract

Segmentation methods based on deformable models have proved to be successful with difficult images, particularly those using genetic algorithms to minimize the energy function. Nevertheless, they are normally conceived as fully automatic, and not always generate satisfactory results. In this work, a method to include the information of fixed points whithin a contour detection system using point distribution models and genetic algorithms is presented. Also, an interactive scheme is proposed to take advantage of this technique. The method has been tested against a database of 93 cattle images, with a significant improvement in the success rate of the detections, from 61% up to 95%. [ABSTRACT FROM AUTHOR]

Published
2007
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