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Author

Pertti T. Koivisto

Other affiliations: University of Tampere
Bio: Pertti T. Koivisto is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Filter (signal processing) & Simulated annealing. The author has an hindex of 7, co-authored 28 publications receiving 168 citations. Previous affiliations of Pertti T. Koivisto include University of Tampere.

Papers
More filters
Journal ArticleDOI
TL;DR: New criteria for shape preservation are presented and these criteria are applied in optimizing soft morphological filters, which are optimized by simulated annealing and genetic algorithms.
Abstract: New criteria for shape preservation are presented. These criteria are applied in optimizing soft morphological filters. The filters are optimized by simulated annealing and genetic algorithms which are briefly reviewed. Situations, where this kind of criteria give better results compared to the traditional MAE and MSE criteria, are illustrated.

21 citations

Journal ArticleDOI
TL;DR: The characteristics of impulse bursts in remote sensing images are analyzed and a model for this noise is proposed, which takes into consideration other noise types, for example, the multiplicative noise present in radar images.
Abstract: The characteristics of impulse bursts in remote sensing images are analyzed and a model for this noise is proposed. The model also takes into consideration other noise types, for example, the multiplicative noise present in radar images. As a case study, soft morphological filters utilizing a training-based optimization scheme are used for the noise removal. Different approaches for the training are discussed. It is shown that these techniques can provide an effiective removal of impulse bursts. At the same time, other noise types in images, for example, the multiplicative noise, can be suppressed without compromising good edge and detail preservation. Numerical simulation results, as well as examples of real remote sensing images, are presented.

19 citations

Proceedings ArticleDOI
18 May 2005
TL;DR: This paper compares in this paper two ways how to apply some robust order statistic filter and one is to use a two-stage approach where impulses are first detected and removed, and after that additive noise is suppressed.
Abstract: Summary form only given. Images are quite often corrupted by mixed additive and impulsive noise. Then, the task in the filtering is to remove both components of the noise. We compare in this paper two ways how to do this. The first one is to apply some robust order statistic filter and the other one is to use a two-stage approach where impulses are first detected and removed, and after that additive noise is suppressed. For the latter approach, two methods are proposed. We demonstrate through experiments that the latter approach performs better than several standard order statistic filtering techniques. In addition, a modified version of a method to estimate the variance of additive noise is introduced. The given method performs well enough even with mixed noise.

17 citations

Journal ArticleDOI
TL;DR: This paper demonstrates how optimization schemes, simulated annealing and genetic algorithms, can be employed in the search for soft morphological filters having optimal performance in a given signal processing task.
Abstract: Soft morphological filters form a large class of nonlinear filters with many desirable properties. However, few design methods exist for these filters. This paper demonstrates how optimization schemes, simulated annealing and genetic algorithms, can be employed in the search for soft morphological filters having optimal performance in a given signal processing task. Furthermore, the properties of the achieved optimal soft morphological filters in different situations are analyzed.

13 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Two novel methods which can develop optimizing Top-Hat morphological filtering parameters are presented for spot target detection, one based on neural network and the other based on genetic algorithm.

258 citations

Journal ArticleDOI
TL;DR: This paper discusses use of the more general coefficient of determination in nonlinear filtering, and addresses the VC dimension of increasing operators in terms of their morphological kernel/basis representations.

190 citations

Journal ArticleDOI
TL;DR: The application of Stein's principle is applied to build a new estimator for arbitrary multichannel images embedded in additive Gaussian noise in order to exploit the correlations existing between the different spectral components.
Abstract: Multichannel imaging systems provide several observations of the same scene which are often corrupted by noise. In this paper, we are interested in multispectral image denoising in the wavelet domain. We adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components. Our main contribution is the application of Stein's principle to build a new estimator for arbitrary multichannel images embedded in additive Gaussian noise. Simulation tests carried out on optical satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques.

105 citations

Journal ArticleDOI
TL;DR: A bibliography of nearly 1700 references related to computer vision and image analysis, arranged by subject matter is presented, including computational techniques; feature detection and segmentation; image and scene analysis; two-dimensional shape; pattern; color and texture; matching and stereo.

94 citations