273 research outputs found

    On morphological hierarchical representations for image processing and spatial data clustering

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    Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

    P algorithm, a dramatic enhancement of the waterfall transformation

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    This document has been extended by "Towards a unification of waterfalls, standard and P algorithms", see http://hal-ensmp.archives-ouvertes.fr/hal-00835016.This document describes an efficient enhancement of the waterfall algorithm, a hierarchical segmentation algorithm defined from the watershed transformation. The first part of the document recalls the definition of the waterfall algorithm, its various avatars as well as its links with the geodesic reconstruction. The second part starts by analyzing the different shortcomings of the algorithm and introduces several strategies to palliate them. Two enhancements are presented, the first one named standard algorithm and the second one, P algorithm. The different properties of P algorithm are analyzed. This analysis is detailed in the last part of the document. The performances of the two algorithms, in particular, are addressed and their analogies with perception mechanisms linked to the brightness constancy phenomenon are discussed

    Morphological Segmentation on Learned Boundaries

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    International audienceColour information is usually not enough to segment natural complex scenes. Texture contains relevant information that segmentation approaches should consider. Martin et al. [Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5) (2004) 530-549] proposed a particularly interesting colour-texture gradient. This gradient is not suitable for Watershed-based approaches because it contains gaps. In this paper, we propose a method based on the distance function to fill these gaps. Then, two hierarchical Watershed-based approaches, the Watershed using volume extinction values and the Waterfall, are used to segment natural complex scenes. Resulting segmentations are thoroughly evaluated and compared to segmentations produced by the Normalised Cuts algorithm using the Berkeley segmentation dataset and benchmark. Evaluations based on both the area overlap and boundary agreement with manual segmentations are performed

    RESIDUAL APPROACH ON A HIERARCHICAL SEGMENTATION

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    International audienceResidual operators analyze the evolution of an image subject to the application of a series of transformations, for example a series of openings of increasing size. When a significant object is filtered out by a transformation corresponding to its size, an important residue is observed. Maximal residues are kept for each pixel, indicating the most significant objects present in the image. Different families of operators have been used in the literature: morphological openings or clos-ings, attribute openings or openings by reconstruction. In this paper we propose to compute residues on a hierarchy of parti-tions, computing the differences between regions at different hierarchical levels based on the classical earth mover's dis-tance. The advantage of our approach is that it is autodual and generic as it can be applied with any hierarchical approach

    Towards a unification of waterfalls, standard and P algorithms

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    This document is an extension of the paper: "P algorithm, a dramatic enhancement of the waterfall transformation". It has mainly two purposes. Firstly, it comes back to the waterfalls, standard and P algorithms to propose a general segmentation scheme which covers and unifies these different processes. Secondly, it contains the source code for the implementation of these waterfalls, standard and P operators with the MAMBA Image software library

    Interactive Segmentation and Visualization of DTI Data Using a Hierarchical Watershed Representation

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    Magnetic resonance diffusion tensor imaging (DTI) measures diffusion of water molecules and is used to characterize orientation of white matter fibers and connectivity of neurological structures. Segmentation and visualization of DT images is challenging, because of low data quality and complexity of anatomical structures. In this paper, we propose an interactive segmentation approach, based on a hierarchical representation of the input DT image through a tree structure. The tree is obtained by successively merging watershed regions, based on the morphological waterfall approach, hence the name watershed tree. Region merging is done according to a combined similarity and homogeneity criterion. We introduce filters that work on the proposed tree representation, and that enable region-based attribute filtering of DTI data. Linked views between the visualizations of the simplified DT image and the tree enable a user to visually explore both data and tree at interactive rates. The coupling of filtering, semiautomatic segmentation by labeling nodes in the tree, and various interaction mechanisms support the segmentation task. Our method is robust against noise, which we demonstrate on synthetic and real DTI data

    On Segmentation Evaluation Metrics and Region Counts

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    Abstract Five image segmentation algorithms are evaluated: mean shift, normalised cuts, efficient graph-based segmentation, hierarchical watershed, and waterfall. The evaluation is done using three evaluation metrics: probabilistic Rand index, global consistency error, and boundary precision-recall. We examine region-based metrics as a function of the number of regions produced by an algorithm. This allows new insights into algorithms and evaluation metrics to be gained

    Stochastic Multiscale Segmentation Constrained by Image Content

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    International audienceWe introduce a noise-tolerant segmentation algorithm efficient on 3D multiscale granular materials. The approach uses a graph-based version of the stochastic watershed and relies on the morphological granulometry of the image to achieve a content-driven unsupervised segmentation. We present results on both a virtual material and a real X-ray microtomographic image of solid propellant
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