Abstract
In this article we present a technique that allows high-speed movement analysis using the accurate displacement measurement given by the feature extraction and correlation method. Specially, we demonstrate that it is possible to use the time to impact computation for object segmentation. This segmentation allows the detection of objects at different distances.
There are several methods to measure movement in front of a mobile vehicle (robot) equipped with a camera. Some methods detect movement from the analysis of the optical flow, while other methods detect movement from the displacement of objects or part of the objects (corners, edges, etc). Those methods based on the optical flow are suitable for high speed analysis (say 25 images per second) but they are not very accurate and treat the image as a whole, being it difficult to separate different objects in the scene. Those methods based on image feature extraction are good for object recognition and clustering, that can be more precise than other methods, but they usually require many calculations to yield a result, making it difficult to implement these methods in a navigation system of a robot or mobile vehicle.
This research has been funded by the Spanish Ministerio de Ciencia y Tecnología project TIC2001-3546 and EU FEDER
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© 2004 Springer-Verlag Berlin Heidelberg
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Pardo, F., Boluda, J.A., De Ves, E. (2004). Feature Extraction and Correlation for Time-to-Impact Segmentation Using Log-Polar Images. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_95
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DOI: https://doi.org/10.1007/978-3-540-24768-5_95
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