VLSI Implementation of a fast rank order filtering algorithm

Harber, R.G.; Bass, S.; Neudeck, G.W.

doi:10.1109/icassp.1985.1168229

Cited by 33 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early examples include cellular automata, parallel architectures, VLSI, optical/electronic, and analog optical implementations of morphological and rank filters whose special cases are the simple erosions and dilations (Preston et al, 1979;Sternberg, 1980;Harber et al, 1985;Ochoa et al, 1987;O'Neil and Rhodes, 1986;Hereford and Rhodes, 1988;Barrera et al, 1994). Nowadays, the vast majority of many computer (hardware or software) systems for digital image processing include among their main operations the basic morphological operators such as erosions, dilations, openings and closings.…”

Section: Why a Representation Theory?mentioning

confidence: 99%

Representations for Morphological Image Operators and Analogies with Linear Operators

Maragos¹

2013

Advances in Imaging and Electron Physics

View full text Add to dashboard Cite

Section: Why a Representation Theory?mentioning

confidence: 99%

Representations for Morphological Image Operators and Analogies with Linear Operators

Maragos¹

2013

Advances in Imaging and Electron Physics

View full text Add to dashboard Cite

“…It makes possible the direct VLSI implementation of the threshold decomposition architecture for all rank-order operators [59]. It allows the summing of the output of the Boolean functions to be performed by a space-efficient binary search for the threshold level of the highest 1 in the stack of l's at the outputs of the Boolean functions.…”

Section: The Stackin9 Property For Signals and Boolean Functionsmentioning

confidence: 99%

“…The median filter, for one and two dimensions, was shown to be relatively easy to implement in software and hardware, see, for instance, [1], [2], [12], [17], [30], [35], [42], [59], [63], [76], [100], [118], [125], [133], and [142]. Huang et al [63] presented a fast algorithm for two-dimensional median filtering based on sorting and updating the gray level histogram of the picture elements in the window.…”

Section: Implementation Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

An overview of median and stack filtering

Coyle

Gallagher

Gabbouj

1992

Circuits Systems and Signal Process

108

View full text Add to dashboard Cite

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.

show abstract

“…Early examples include [21]- [23]; for some recent examples see the session on morphological systems of the workshop [24]. In addition, there are VLSI, optical/electronic, and analog optical implementations of rank order filters (whose special cases are the simple erosions and dilations) or morphological filters [25]- [28]. Therefore, from an upplications viewpoint, our representation theory establishes the capabilities and limitations of all these computer architectures and implementations by finding the general class of signal operations that they can perform.…”

Section: Introductionmentioning

confidence: 99%

A representation theory for morphological image and signal processing

Maragos

1989

IEEE Trans. Pattern Anal. Machine Intell.

204

View full text Add to dashboard Cite

Abstract-This paper presents a unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis. This unification requires a set-theoretic melhodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translation-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-invariant, increasing, upper semicontinuous system can be represented exactly as a minimal nonlinear superposition of morphological erosions or dilations.In this representation, many similarities and a few differences are observed between systems processing binary or multilevel signals, and continuous-domain or discrete-domain signals.The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic filters, linear filters, and shape recognition transforms.Although the developed theory is algebraic, its prototype operations are well suitable for shape analysis; hence, the results of this study also apply to systems that extract information about the geometrical structure of signals.Zndex Terms--Image/signal processing, mathematical morphology, nonlinear/linear filtering, shape analysis, systems representation.

show abstract

VLSI Implementation of a fast rank order filtering algorithm

Cited by 33 publications

References 2 publications

Representations for Morphological Image Operators and Analogies with Linear Operators

Representations for Morphological Image Operators and Analogies with Linear Operators

An overview of median and stack filtering

A representation theory for morphological image and signal processing

Contact Info

Product

Resources

About