Stack filters

Wendt, Peter; Coyle, Eugene; Gallagher, Neal C.

doi:10.1109/tassp.1986.1164871

Cited by 587 publications

(176 citation statements)

References 19 publications

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“…Unfortunately, textures of interested image regions would be removed simultaneously while median filter is operated so to cause blurred textures [7]. To tackle the above problem, weighted median filter was presented [8], [9]. The principle of weighted median filter is similar to that of median filter but the pixel value at the central position of weighted median filter is duplicated k times.…”

Section: A Weighted Median Filtermentioning

confidence: 99%

Application of Computer Vision to Crack Detection of Concrete Structure

Su¹

2013

IJET

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Abstract-Most important civil infrastructures are made of concrete, so accurate information by routine inspection is necessary for structure maintenance. Sometimes temporarily erected scaffoldings are needed for infrastructure inspections. Bridge inspection for example, the inspectors must stand on the platform to examine the underside of a bridge, but such a procedure is risky. At present, several inspection systems coupled with Charge Coupled Devices (CCD) cameras have been developed and applied to infrastructure inspections in order to reduce the danger of accidents to the human inspectors. This paper proposes a computer vision technique based on CCD images to attempt to automatically detect cracks in concrete structure. The experimental result indicates that the optimal accuracies of 90% and 84% could be achieved for the training and testing samples, respectively.

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Section: A Weighted Median Filtermentioning

confidence: 99%

Application of Computer Vision to Crack Detection of Concrete Structure

Su¹

2013

IJET

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“…Using threshold decomposition, they derived the root structure of the recursive separable median filter. In recognition of the importance of the threshold decomposition, it was made one of the defining properties of stack filters [137], which will be discussed in much greater detail later. Several other filters closely related to median filters have been introduced.…”

Section: Generalization Of Median-type Filters : Theoretical Analysismentioning

confidence: 99%

“…The final class of filters discussed here is the class called stack filters [137], which contain the class of rank-order smoothers introduced in [25] since stack filters are also maxima of minima, but the operators whose output is either always 0 or always M --1 are allowed when the input takes values in More fundamental than this slight difference in the class of operations that are considered is the differing motivations behind the definitions of the two filter classes. Specifically, stack filters were defined as having the threshold decomposition property and the ordering property [89] called the stacking property in [137].…”

Section: Generalization Of Median-type Filters : Theoretical Analysismentioning

confidence: 99%

“…Specifically, stack filters were defined as having the threshold decomposition property and the ordering property [89] called the stacking property in [137]. The stacking property then led to a connection to positive Boolean functions [56].…”

Section: Generalization Of Median-type Filters : Theoretical Analysismentioning

confidence: 99%

“…The goal in the definition of stack filters [137] was to develop a class of strictly digital nonlinear filters which had the two properties considered at that time to be the essential features of the median filter: the threshold decomposition and the stacking properties. The hope was that the class of filters would have many of the desirable characteristics of the median filter, be large enough to include essentially every operation "of interest," small enough to allow development of an e~cient optimization procedure, and simple enough to allow easy implementation.…”

Section: Stack Filtersmentioning

confidence: 99%

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An overview of median and stack filtering

Coyle

Gallagher

Gabbouj

1992

Circuits Systems and Signal Process

108

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Abstract. Within the last two decades a small group of researchers has built a useful, nontrivial theory of nonlinear signal processing around the median-related filters known as rank-order filters, order-statistic filters, weighted median filters, and stack filters. This required significant effort to overcome the bias, both in education and research, toward linear theory, which has been dominant since the days of Fourier, Laplace, and "Convolute."We trace the development of this theory of nonlinear filtering from its beginnings in the study of noise-removal properties and structural behavior of the median filter to the recently developed theory of optimal stack filtering.The theory of stack filtering provides a point of view which unifies many different filter classes, including morphological filters, so it is discussed in detail. Of particular importance is the way this theory has brought together, in a single analytical framework, both the estimation-based and the structural-based approaches to the design of these filters.Some recent applications of median and stack filters are provided to demonstrate the effectiveness of this approach to nonlinear filtering. They include: the design of an optimal stack filter for image restoration; the use of vector median filters to attenuate impulsive noise in color images and to eliminate cross luminance and cross color in TV images; and the use of median-based filters for image sequence coding, reconstruction, and scan rate conversion in normal TV and HDTV systems.

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