Robust image modeling on image processing

Allende, Héctor; Galbiati, Jorge; Vallejos, Ronny

doi:10.1016/s0167-8655(01)00054-x

Cited by 20 publications

(18 citation statements)

References 10 publications

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“…Unfortunately, these outlying pixels are usually scattered throughout the scene and are small in number and, therefore, identifying these pixels could be a tedious task. A common approach to eliminate the effect of those pixels is to use robust techniques [1,8].…”

Section: Robust Estimatorsmentioning

confidence: 99%

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M-estimators with asymmetric influence functions: the distribution case

Allende

Frery

Galbiati

et al. 2006

Journal of Statistical Computation and Simulation

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Many applications consecrate the use of asymmetric distributions, and practical situations often require robust parametric inference. This paper presents the derivation of M-estimators with asymmetric influence functions, motivated by the G 0 A distribution. This law, regarded as the universal model for speckled imagery, can be highly skewed and maximum likelihood estimation can be severely hampered by small percentages of outliers. These outliers appear mainly because the hypothesis of independence and equal distribution of observations are seldom satisfied in practice; for instance, in the process of filtering, some pixels within a window frequently come from regions with different underlying distributions. Traditional robust estimation methods, on the basis of symmetric robustifying functions, assume that the distribution is symmetric, but when the data distribution is asymmetric, these methods yield biased estimators. Empirical influence functions for maximum likelihood estimators are computed, and based on this information we propose the asymmetric M-estimator (AM-estimator), an M-estimator with asymmetric redescending functions. The performance of AM estimators is assessed, and it is shown that they either compete with or outperform both maximum likelihood and Huber-type M-estimators.

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Section: Robust Estimatorsmentioning

confidence: 99%

“…In remote sensing, for instance, these properties can be used to discriminate types of land use and to develop specialised filters for speckle noise reduction, among other applications. Statistical image filtering, segmentation and classification are procedures that heavily rely on dependable inference procedures [1].…”

Section: Introductionmentioning

confidence: 99%

M-estimators with asymmetric influence functions: the distribution case

Allende

Frery

Galbiati

et al. 2006

Journal of Statistical Computation and Simulation

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the best known classes of robust estimators are M-estimators, which are a generalization of the ML-estimators [AGV01]. In this work, we use them to estimate the parameters of the G 0 A distribution.…”

Section: Robust Estimatorsmentioning

confidence: 99%

“…The importance of the ψ functions is that they truncate the score of the influential observations in the likelihood equation. Many theoretical results concerning the asymptotic and the robustness properties of M-estimators are available in the literature [AGV01], [BLF02], [RV02]. On the other hand, it is possible consider M-estimators with asymmetrical influence functions [AFGP03], which depend on underlying distributions.…”

Section: Robust Estimatorsmentioning

confidence: 99%

Robust Estimation of Roughness Parameter in SAR Amplitude Images

Allende

Pizarro

2003

Lecture Notes in Computer Science

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Abstract. The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution G 0 A as the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of 'outliers', i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment.

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“…Also the two-dimensional autoregressive model has been used to perform unsupervised texture segmentation (Cariou & Chehdi, 2008). Generalizations of the previous algorithms using the generalized M estimators to deal with the effect caused by additive contamination was also addressed (Allende et al, 2001). Later on, robust autocovariance (RA) estimators for two dimensional autoregresive (AR-2D) processes were introduced (Ojeda, 2002).…”

Section: Introductionmentioning

confidence: 99%