Results 121 to 130 of about 467,408 (158)

Adaptive stack filtering under the mean absolute error criterion

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
An adaptive filter algorithm is developed for the class of stack filters, which is a class of nonlinear filters obeying a weak superposition property. The adaptation algorithm can be interpreted as a learning algorithm for a group of decision-making units, the decisions of which are subject to a set of constraints called the stacking constraints. Under
J. H. Lin, T. M. Sellke, E. J. Coyle
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Optimal Mean-Absolute-Error Nonincreasing Binary Filters

1997
The basic task of optimal filtering is to operate on an observed image in a manner that produces a filtered image that is a good estimate of a desired image. In the language of image restoration, we wish to filter a degraded document image to produce a restored image close to an ideal document image.
Edward R. Dougherty, Robert P. Loce
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Generalized stack filters and minimum mean absolute error estimation

1988., IEEE International Symposium on Circuits and Systems, 2003
A class of sliding window operators called generalized stack filters is developed. This class of filters, which includes all rank order filters, stack filters, and digital morphological filters, is the set of all filters possessing the threshold decomposition architecture and a consistency property called the stacking property.
J.H. Lin, E.J. Coyle
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A Weighted Mean Absolute Error Metric for Image Quality Assessment

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020
Pixel-wise image quality assessment (IQA) algorithms, such as mean square error (MSE), mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) correlate well with perceptual quality when dealing with images sharing the same distortion type but not well when processing images in different distortion types, which is inconsistent with human visual
Sihan Hao, Sumei Li
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Lossless image coding based on minimum mean absolute error predictors

SICE Annual Conference 2007, 2007
For prediction-based lossless image coding, the coding performance depends largely on the efficiency of predictors. In general, mmse predictors are well used, but these predictors suffer from large errors at edges. In response, the authors have proposed minimum mean absolute error (mmae) predictors which are less sensitive to edges.
null Yoshihiko Hashidume, null Yoshitaka Morikawa   +1 more
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Optimal morphological restoration: The morphological filter mean-absolute-error theorem

Journal of Visual Communication and Image Representation, 1992
Morphological restoration is grounded on the Matheron representation for morphological filters, in the present context these being monotonically increasing, translation-invariant image-to-image operators. As conceived in its most general form, optimal-morphological-filter design involves a search over potential bases of structuring elements that can be
Robert P. Loge, Edward R. Dougherty
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Adaptive generalized stack filtering under the mean-absolute-error criterion

SPIE Proceedings, 1992
A new adaptive algorithm is developed in this paper for determining optimal generalized stack (GS) filters under the mean absolute error criterion. This algorithm, based on the neural network representation of Boolean functions, is much more efficient than the traditional truth table based algorithms.
Lin Yin, Jaakko T. Astola, Yrjo A. Neuvo
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Mean-Absolute-Error Representation and Optimization of Computational-Morphological Filters

Graphical Models and Image Processing, 1995
Abstract Computational mathematical morphology provides a framework for analysis and representation of range-preserving, finite-range operators in the context of mathematical morphology. As such, it provides a framework for statistically optimal design in the framework of a Matheron-type representation; that is, each increasing, translation-invariant
R.P. Loce, E.R. Dougherty
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Rank order operators and the mean absolute error criterion

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
The analysis of the mean absolute error for rank order filters is discussed. These filters are a subset of a larger class of nonlinear filters, called stack filters, which obey two basic properties: a) a weak superposition property, the so-called threshold decomposition and b) an ordering property called the stacking property.
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Mean absolute percentage error and bias in economic forecasting

Economics Letters, 2011
Abstract This article develops a simple theoretical framework to show how forecasters may bias downward point predictions under the assumption that the asymmetric loss function is directly related to the (Mean) Absolute Percentage Error (M)APE.
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