27,401 research outputs found
DISCRIMINANT STEPWISE PROCEDURE
Stepwise procedure is now probably the most popular tool for automatic feature selection. In the most cases it represents model selection approach which evaluates various feature subsets (so called wrapper). In fact it is heuristic search technique which examines the space of all possible feature subsets. This method is known in the literature under different names and variants. We organize the concepts and terminology, and show several variants of stepwise feature selection from a search strategy point of view. Short review of implementations in R will be given
Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal
Most existing image denoising algorithms can only deal with a single type of
noise, which violates the fact that the noisy observed images in practice are
often suffered from more than one type of noise during the process of
acquisition and transmission. In this paper, we propose a new variational
algorithm for mixed Gaussian-impulse noise removal by exploiting image local
consistency and nonlocal consistency simultaneously. Specifically, the local
consistency is measured by a hyper-Laplace prior, enforcing the local
smoothness of images, while the nonlocal consistency is measured by
three-dimensional sparsity of similar blocks, enforcing the nonlocal
self-similarity of natural images. Moreover, a Split-Bregman based technique is
developed to solve the above optimization problem efficiently. Extensive
experiments for mixed Gaussian plus impulse noise show that significant
performance improvements over the current state-of-the-art schemes have been
achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on
Multimedia & Expo (ICME) 201
Outlier robust corner-preserving methods for reconstructing noisy images
The ability to remove a large amount of noise and the ability to preserve
most structure are desirable properties of an image smoother. Unfortunately,
they usually seem to be at odds with each other; one can only improve one
property at the cost of the other. By combining M-smoothing and
least-squares-trimming, the TM-smoother is introduced as a means to unify
corner-preserving properties and outlier robustness. To identify edge- and
corner-preserving properties, a new theory based on differential geometry is
developed. Further, robustness concepts are transferred to image processing. In
two examples, the TM-smoother outperforms other corner-preserving smoothers. A
software package containing both the TM- and the M-smoother can be downloaded
from the Internet.Comment: Published at http://dx.doi.org/10.1214/009053606000001109 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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