Building the Component Tree in Quasi-Linear Time

Najman, Laurent; Couprie, Michel

doi:10.1109/tip.2006.877518

Cited by 201 publications

(204 citation statements)

References 19 publications

Supporting

Mentioning

200

Contrasting

Unclassified

Order By: Relevance

“…Alternative methods were presented in [49][50][51][52]. The recursive method discussed in [19] separates the construction of the tree from the computation of attributes and the actual filtering.…”

Section: The Max-tree Algorithm and The One-pass Methodsmentioning

confidence: 99%

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

Wilkinson

Pesaresi

Ouzounis³

2016

IJGI

View full text Add to dashboard Cite

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.

show abstract

Section: The Max-tree Algorithm and The One-pass Methodsmentioning

confidence: 99%

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

Wilkinson

Pesaresi

Ouzounis³

2016

IJGI

View full text Add to dashboard Cite

show abstract

“…The set C ⋆ (F ), called the component tree of F [25,26], is a finite subset of C(F ) that is widely used in practice for image filtering.…”

Section: Edge-weighted Graphsmentioning

confidence: 99%

Ultrametric Watersheds

Najman¹

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…They are pushed in the queue (lines 10-11) and registered as representative if we reached a new level component (line 14). If q's gray-level i is higher than h, the flooding procedure is relaunched recursively to extract the subtree (lines [17][18][19]. Back from the neighbors, the attribute of the current h-level component is updated (line 21).…”

Section: Floodingmentioning

confidence: 99%

“…It may also be penalized by its dynamic allocation/deallocation scheme. The medical volume example suggests that the performance gap narrows when the number of gray levels increases, supposedly because of the dependance on L affecting both other algorithms [14,17]. To draw a definitive conclusion, one should adapt Wilkinson et al's union-find attribute opening to thinning operations.…”

Section: Computational Loadmentioning

confidence: 99%

An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

Lesage

Darbon

Akgül

2006

Advances in Visual Computing

View full text Add to dashboard Cite

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.

show abstract

Building the Component Tree in Quasi-Linear Time

Cited by 201 publications

References 19 publications

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

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

Ultrametric Watersheds

An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

Contact Info

Product

Resources

About