Abstract
Recently, log-polar images have been successfully used in active-vision tasks such as vergence control or target tracking. However, while the role of foveal data has been exploited and is well known, that of periphery seems underestimated and not well understood. Nevertheless, peripheral information becomes crucial in detecting non-foveated objects or events. In this paper, a multiple-model approach (MMA) for top-down, model-based attention processes is proposed. The advantages offered by this proposal for space-variant image representations are discussed. A simple but representative frontal-face detection task is given as an example of application of the MMA. The combination of appearance-based features and a linear regression-based classifier proved very effective. Results show the ability of the system to detect faces at very low resolutions, which has implications in fields such as visual surveillance.
Research partly funded by grants E-2003-03 from Fundació Caixa-Castelló Bancaixa, European IST 2001 37540 (CAVIAR project), and CTIDIB/2002/333, from Conselleria de Educació, Cultura i Ciè ncia, Generalitat Valenciana.
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Traver, V.J., Bernardino, A., Moreno, P., Santos-Victor, J. (2004). Appearance-Based Object Detection in Space-Variant Images: A Multi-model Approach. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_67
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DOI: https://doi.org/10.1007/978-3-540-30125-7_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
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