Abstract
The essential parameters approach is a well-known technique in computer vision for recovering the motion and scene parameters from a sequence of images. This approach has long been considered as suboptimal because of the underestimation of the effect of noise and outliers. This paper re-evaluates this method because it is correctly classified as a structured Total Least Squares problem and proposes very robust linear neurons for its solution. Then, a novel neural network, CASEDEL EXIN, which exploits these neurons together with the case deletion diagnostics, is introduced. It is not only very robust (outlier rejection), but is also able to identify the outliers. This fact can be exploited, for example, to refine the image segmentation.
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