COBRA: an evolved online tool for mammography interpretation

Authors:
Carlos-Andrés Peña-Reyes;Rosa Villa;Luis Prieto;Eduardo Sanchez
Affiliations:
Swiss Federal Institute of Technology in Lausanne, EPFL, Switzerland;Centro Nacional de Microelectrónica, Barcelona, Spain;Duran i Reynals Hospital, Barcelona, Spain;Swiss Federal Institute of Technology in Lausanne, EPFL, Switzerland
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 6
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Abstract

The main objective in motion detection algorithms for video surveillance applications is to minimize the false alarm probability while maintaining the probability of detection as high as possible. Many motion detection systems fail when the noise in a specific zone is high, increasing the false detection probability, and so the system can not detect motion in these zones. In this paper we present an alternative scheme that tries to solve the mentioned problem using the classification capacity of a neural network.