Attribute Openings, Thinnings, and Granulometries

Breen, Edmond J.; Jones, Ronald

doi:10.1006/cviu.1996.0066

Cited by 369 publications

(361 citation statements)

References 15 publications

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“…The latter is not a thinning algorithm, but is strictly equivalent to a thinning with a Max rule for non-increasing attributes. We voluntarily omit priority queue-based algorithms [24,3] which exhibit a high dependance w.r.t the threshold value λ [14]. Experiments show that our algorithm is faster for natural images and requires less memory than other other available algorithms.…”

Section: Analysis and Experimentsmentioning

confidence: 99%

“…Efficient algorithms have been described in [6,14,18,24] for connected attribute openings and closings. To our knowledge, few algorithms have been proposed for attribute thinnings and thickenings [3,18]. In this paper, we present a new algorithm for such filters.…”

Section: Introductionmentioning

confidence: 99%

“…A popular choice for the pruning, known as the Max rule [18], consists in removing a node if its associated criteria do not satisfy a prescribed threshold and if all its descendants were removed. In [3], Breen et al propose a queuebased algorithm to perform direct filtering using the Max rule. In [25], Wilkinson et al present an union-find based algorithm for performing connected attribute openings and closings.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

Lesage

Darbon

Akgül

2006

Advances in Visual Computing

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Abstract. Connected attribute filters are morphological operators widely used for their ability of simplifying the image without moving its contours. In this paper, we present a fast, versatile and easy-to-implement algorithm for grayscale connected attribute thinnings and thickennings, a subclass of connected filters for the wide range of non-increasing attributes. We show that our algorithm consumes less memory and is computationally more efficient than other available methods on natural images, for strictly identical results.

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Section: Analysis and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

Lesage

Darbon

Akgül

2006

Advances in Visual Computing

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“…Differential attribute profiles [15,17,21] are generalizations of DMPs based on connected attribute filters [22]. Instead of a series of openings-by-reconstruction, a series of attribute filters are used which form either a size or shape granulometry [23,24].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Parallel Algorithm for Multi-Scale Analysis of Connected Components in Gigapixel Images

Wilkinson

Pesaresi

Ouzounis³

2016

IJGI

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Differential Morphological Profiles (DMPs) and their generalized Differential Attribute Profiles (DAPs) are spatial signatures used in the classification of earth observation data. The Characteristic-Salience-Leveling (CSL) is a model allowing the compression and storage of the multi-scale information contained in the DMPs and DAPs into raster data layers, used for further analytic purposes. Computing DMPs or DAPs is often constrained by the size of the input data and scene complexity. Addressing very high resolution remote sensing gigascale images, this paper presents a new concurrent algorithm based on the Max-Tree structure that allows the efficient computation of CSL. The algorithm extends the "one-pass" method for computation of DAPs, and delivers an attribute zone segmentation of the underlying trees. The DAP vector field and the set of multi-scale characteristics are computed separately and in a similar fashion to concurrent attribute filters. Experiments on test images of 3.48 to 3.96 Gpixel showed an average computational speed of 59.85 Mpixel per second, or 3.59 Gpixel per minute on a single 2U rack server with 64 opteron cores. The new algorithms could be extended to morphological keypoint detectors capable of handling gigascale images.

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“…Thanks to efforts devoted to its efficient computation [2,14,7,11,8] or its use in complex knowledge handling procedures [15], component-trees have been considered for the design of various kinds of grey-level image processing methods, including image filtering and segmentation [5,6,17,12,16,10], video segmentation [14], image registration [7], image compression [14], or image retrieval [9,1].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Complex Images Based on Component-Trees: Methodological Tools

Caldairou

Naegel

Passat

2009

Lecture Notes in Computer Science

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Abstract. Component-trees can be used for the design of image processing methods, and in particular segmentation ones. However, despite their ability to consider various kinds of knowledge and their tractable computation, methodological deadlocks still forbid to efficiently involve them in real applications. In this article, we explore new solutions to some of these deadlocks, and more especially those related to (i) complexity of the structures of interest and (ii) multiple knowledge handling. The usefulness of the proposed strategies is illustrated by preliminary results related to vessel segmentation from 3-D angiographic data.

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Attribute Openings, Thinnings, and Granulometries

Cited by 369 publications

References 15 publications

An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

An Efficient Parallel Algorithm for Multi-Scale Analysis of Connected Components in Gigapixel Images

Segmentation of Complex Images Based on Component-Trees: Methodological Tools

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