Background Image Estimation with MRF and DBSCAN Algorithms

Density - based clustering algorithms – DBSCAN and SNN

This document describes the implementation of two density-based clustering algorithms: DBSCAN [Ester1996] and SNN [Ertoz2003]. These algorithms were implemented within the context of the LOCAL project [Local2005] as part of a task that aims to create models of the geographic space (Space Models) to be used in context-aware mobile systems. Here, the role of the clustering algorithms is to identi...

Density Level Set Estimation on Manifolds with DBSCAN

We show that DBSCAN can estimate the connected components of the λ-density level set {x : f(x) ≥ λ} given n i.i.d. samples from an unknown density f . We characterize the regularity of the level set boundaries using parameter β > 0 and analyze the estimation error under the Hausdorff metric. When the data lies in R we obtain a rate of Õ(n−1/(2β+D)), which matches known lower bounds up to logari...

Background estimation in nonlinear image restoration.

One of the essential ways in which nonlinear image restoration algorithms differ from linear, convolution-type image restoration filters is their capability to restrict the restoration result to nonnegative intensities. The iterative constrained Tikhonov-Miller (ICTM) algorithm, for example, incorporates the nonnegativity constraint by clipping all negative values to zero after each iteration. ...

Improvements in Mrf Parameter Estimation

MRF-Based Blind Image Deconvolution

This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method by applying it on a wide variety of...