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Definition
The goal of robust methods in computer vision is to extract all the information necessary to solve a given task while discarding everything that is not needed. The tasks can be very simple or very complex, but in real-life applications, a robust procedure will always be required. In the end, the performance of a machine for solving a vision problem will be judged against that of human observers performing the equivalent task. Since the human visual system works in a much more sophisticated manner than the present day computer vision systems, this ultimate goal is still far from reality.
Background
The task of a robust algorithm is to derive a model that estimates one or more structures present in the data, each structure depending only on a part of the data. A point obeying the model is called an inlier, while a point not obeying it is called an outlier. Since there are several inlier structures, therefore relative to one inlier...
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References
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Meer, P., Mittal, S. (2014). Robust Methods. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_650
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DOI: https://doi.org/10.1007/978-0-387-31439-6_650
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