This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points....
Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a net...
This paper addresses the problem of superresolution from low resolution spherical images that are not perfectly registered. Such a problem is typically encountered in omnidirectio...
Abstract. Matrix factorization is a fundamental building block in many computer vision and machine learning algorithms. In this work we focus on the problem of ”structure from mo...
Recent research provided several new and fast approaches for the class of parameter estimation problems that are common in computer vision. Incorporation of complex noise model (mo...