Abstract
Much of computer vision and image analysis involves the extraction of “meaningful” information from images using concepts akin to regression and model fitting. Applications include: robot vision, automated surveillance (civil and military) and inspection, biomedical image analysis, video coding, human-machine interface, visualization, historical film restoration etc. However, problems in computer vision often have characteristics that are distinct from those usually addressed by the statistical community. These include pseudo-outliers: in a given image, there are usually several populations of data. Some parts may correspond to one object in a scene and other parts will correspond to other, rather unrelated, objects. When attempting to fit a model to this data, one must consider all populations as outliers to other populations — the term pseudo-outlier has been coined for this situation. Thus it will rarely happen that a given population achieves the critical size of 50% of the total population and, therefore, techniques that have been touted for their high breakdown point (e.g., Least Median of Squares) are no longer reliable candidates, being limited to a 50% breakdown point.
Computer vision researchers have developed their own techniques that perform in a robust fashion. These include RANSAC, ALKS, RESC and MUSE. In this paper new robust procedures are introduced and applied to two problems in computer vision: range image fitting and segmentation, and image motion estimation. The performance is shown, empirically, to be superior to existing techniques and effective even when as little as 5-10% of the data actually belongs to any one structure.
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Suter, D., Wang, H. (2004). Robust Fitting Using Mean Shift: Applications in Computer Vision. In: Hubert, M., Pison, G., Struyf, A., Van Aelst, S. (eds) Theory and Applications of Recent Robust Methods. Statistics for Industry and Technology. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-7958-3_27
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DOI: https://doi.org/10.1007/978-3-0348-7958-3_27
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