Abstract
Since years variational methods belong to the most accurate techniques for computing the optical flow in image sequences. However, if based on the grey value constancy assumption only, such techniques are not robust enough to cope with typical illumination changes in real-world data. In our paper we tackle this problem in two ways: First we discuss different photometric invariants for the design of illumination-robust variational optical flow methods. These invariants are based on colour information and include such concepts as spherical/conical transforms, normalisation strategies and the differentiation of logarithms. Secondly, we embed them into a suitable multichannel generalisation of the highly accurate variational optical flow technique of Brox et al. This in turn allows us to access the true potential of such invariants for estimating the optical flow. Experiments with synthetic and real-world data demonstrate the success of combining accuracy and robustness: Even under strongly varying illumination, reliable and precise results are obtained.
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Mileva, Y., Bruhn, A., Weickert, J. (2007). Illumination-Robust Variational Optical Flow with Photometric Invariants. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_16
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DOI: https://doi.org/10.1007/978-3-540-74936-3_16
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