Pipeline architectures for morphologie image analysis

Abbott, Lynn; Haralick, Robert M.; Zhuang, Xinhua

doi:10.1007/bf01212310

Cited by 48 publications

(12 citation statements)

References 9 publications

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“…Proof To validate (21) It is clear that recursive formulas (18) and (21) for sequentially calculating each dilation f @ ki are pipelineable and that so is the dilation f@k, which is equal to the maximum of the n dilations in the sequence due to (15).…”

Section: Proofmentioning

confidence: 99%

“…The combination of recursive formulas (22) and (23) for sequentially calculating each f O ki are pipelineable, and so is the erosion f (9 k, which is equal to the minimum of the erosions in the sequence due to (15). It is easily verified that when f is a binary image and k is a binary structuring element, performing f (9 k or f ® k in the binary domain by using the two-pixel decomposition has essentially the same efficiency as performing the same operations in the grayscale domain by using the recursive formulas.…”

Section: Proofmentioning

confidence: 99%

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Decomposition of morphological structuring elements

Zhuang

1994

J Math Imaging Vis

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Abstract. Mathematical morphology has become an important tool for machine vision since the influential work by Serra (1982). It is a branch of image analysis based on set-theoretic descriptions of images and their transformations. As is well known, the chain rule for basic morphological operations, i.e., dilation and erosion, lends itself well to pipelining. Specialized pipeline architecture hardware built in the past decade is capable of efficiently performing morphological operations. The nature of specialized hardware depends on the strategy for morphologically decomposing a structuring element. The two-pixel decomposition technique and the cellular decomposition technique are the two main techniques for morphological structuring-element decomposition. The former was used by the image flow computer and the latter by the cytocomputer.This paper represents a continuation of the work reported by Zhuang and Haralick (1986). We first give the optimal two-pixel decomposition for the binary structuring element and subsequently attempt to solve the grayscale-structuring-element two-pixel decomposition problem. To our knowledge, no efficient algorithm for this problem has been found to date. The difficulty can be overcome by using an adequate representation for the grayscale structuring element. Representing a grayscale image as a specific 3D set, i.e., an umbra (Sternberg, 1986), makes it easier to shift all basic morphological theorems from the binary domain to the grayscale domain; however, a direct umbra representation is not appropriate for the grayscale-structuring-element decomposition. In this paper a morphologically realizable representation and a two-pixel decomposition for the grayscale structuring element are presented; moreover, the recursive algorithms, which are pipelineable for efficiently performing grayscale morphological operations, are developed on the basis of the proposed representation and decomposition. This research will certainly be beneficial to real-time image analysis in terms of computer architecture and software development.

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Section: Proofmentioning

confidence: 99%

Section: Proofmentioning

confidence: 99%

Decomposition of morphological structuring elements

Zhuang

1994

J Math Imaging Vis

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“…This is useful for detecting boundaries between different transparent materials. The morphological techniques that are used here are based on the basic properties of erosion and dilation [2] Gray-scale erosion is defined as ðXYBÞðx; yÞ ¼ min ðj ;iÞ2B fXðy þ j; x þ iÞ À Bðj; iÞg ð35:1Þ…”

Section: Introductionmentioning

confidence: 99%

Glass Thickness Measurement Using Morphology

Miller

Shabestari²

2012

Machine Vision Handbook

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“…In this paper we present the geometric-step morphological skeleton and we show that the algorithms based on this skeleton require logarithmic computational complexity, when implemented on a pipelined morphological processor (PMP) [5] with a sufficiently large neighborhood operator. In order to obtain high data compression rate for the image representation, we develop the reduced-cardinality geometric-step morphological skeleton and we show that the cardinality of the new representation is comparable to that obtained in [4].…”

Section: Introductionmentioning

confidence: 99%

A Fast Algorithm For The Morphological Coding Of Binary Images

Schonfeld

Goutsias

1988

SPIE Proceedings

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In this paper we investigate the computational performance of a number of morphological skeletons. We also develop the reduced -cardinality geometric -step morphological skeleton which is shown to achieve logarithmic computational complexity, compared to the linear computational complexity of the uniform -step morphological skeleton, when implemented on a large neighborhood pipelined morphological processor. Furthermore, an upper bound on the skeleton cardinality is derived.

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Pipeline architectures for morphologie image analysis

Cited by 48 publications

References 9 publications

Decomposition of morphological structuring elements

Decomposition of morphological structuring elements

Glass Thickness Measurement Using Morphology

A Fast Algorithm For The Morphological Coding Of Binary Images

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