Abstract
This Paper presents a new approach based on RBF NN (Radial Based Function Neural Network) in order to produce high quality optical-flow confidence estimation. The new approach is compared with a widely used confidence estimator obtaining a significant improvement. In order to evaluate the presented approach performance we have used a multi-scale version of the well known Lukas and Kanade optical flow model and widely used benchmarking optical flow sequences. The new approach aims refining optical flow representation maps but is easily applicable to other vision primitives (stereo vision, object segmentation, object recognition, object tracking, etc). Therefore, this approach represents an automatic reliability estimation model based on artificial neural networks of interest for multiple vision primitives.
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Rodrigo, A., Javier, D., Pilar, O., Pablo, G., Eduardo, R. (2011). Optical Flow Reliability Model Approximated with RBF. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_12
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DOI: https://doi.org/10.1007/978-3-642-21498-1_12
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