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28
Random walks for image segmentation
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2006
"... A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with userdefined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the ..."
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Cited by 387 (21 self)
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A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with userdefined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a highquality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs.
ClearView: An Interactive Context Preserving Hotspot Visualization Technique
"... Abstract—Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless h ..."
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Cited by 37 (1 self)
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Abstract—Volume rendered imagery often includes a barrage of 3D information like shape, appearance and topology of complex structures, and it thus quickly overwhelms the user. In particular, when focusing on a specific region a user cannot observe the relationship between various structures unless he has a mental picture of the entire data. In this paper we present ClearView, a GPUbased, interactive framework for texturebased volume raycasting that allows users which do not have the visualization skills for this mental exercise to quickly obtain a picture of the data in a very intuitive and userfriendly way. ClearView is designed to enable the user to focus on particular areas in the data while preserving context information without visual clutter. ClearView does not require additional feature volumes as it derives any features in the data from image information only. A simple pointandclick interface enables the user to interactively highlight structures in the data. ClearView provides an easy to use interface to complex volumetric data as it only uses transparency in combination with a few specific shaders to convey focus and context information. Index Terms—Focus & Context, GPU rendering, volume raycasting.
A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting
"... Abstract. We present a method for automatically segmenting skin lesions by initializing the random walker algorithm with seed points whose properties, such as colour and texture, have been learnt via a training set. We leverage the speed and robustness of the random walker algorithm and augment it i ..."
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Cited by 18 (11 self)
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Abstract. We present a method for automatically segmenting skin lesions by initializing the random walker algorithm with seed points whose properties, such as colour and texture, have been learnt via a training set. We leverage the speed and robustness of the random walker algorithm and augment it into a fully automatic method by using supervised statistical pattern recognition techniques. We validate our results by comparing the resulting segmentations to the manual segmentations of an expert over 120 cases, including 100 cases which are categorized as difficult (i.e.: low contrast, heavily occluded, etc.). We achieve an Fmeasure of 0.95 when segmenting easy cases, and an Fmeasure of 0.85 when segmenting difficult cases. 1
Active Learning for Interactive 3D Image Segmentation
"... Abstract. We propose a novel method for applying active learning strategies to interactive 3D image segmentation. Active learning has been recently introduced to the field of image segmentation. However, so far discussions have focused on 2D images only. Here, we frame interactive 3D image segmentat ..."
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Cited by 15 (3 self)
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Abstract. We propose a novel method for applying active learning strategies to interactive 3D image segmentation. Active learning has been recently introduced to the field of image segmentation. However, so far discussions have focused on 2D images only. Here, we frame interactive 3D image segmentation as a classification problem and incorporate active learning in order to alleviate the user from choosing where to provide interactive input. Specifically, we evaluate a given segmentation by constructing an “uncertainty field ” over the image domain based on boundary, regional, smoothness and entropy terms. We then calculate and highlight the plane of maximal uncertainty in a batch query step. The user can proceed to guide the labeling of the data on the query plane, hence actively providing additional training data where the classifier has the least confidence. We validate our method against random plane selection showing an average DSC improvement of 10 % in the first five plane suggestions (batch queries). Furthermore, our user study shows that our method saves the user 64 % of their time, on average. 1
Direct Volume Editing
"... Abstract—In this work we present basic methodology for interactive volume editing on GPUs, and we demonstrate the use of these methods to achieve a number of different effects. We present fast techniques to modify the appearance and structure of volumetric scalar fields given on Cartesian grids. Sim ..."
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Cited by 11 (0 self)
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Abstract—In this work we present basic methodology for interactive volume editing on GPUs, and we demonstrate the use of these methods to achieve a number of different effects. We present fast techniques to modify the appearance and structure of volumetric scalar fields given on Cartesian grids. Similar to 2D circular brushes as used in surface painting we present 3D spherical brushes for intuitive coloring of particular structures in such fields. This paint metaphor is extended to allow the user to change the data itself, and the use of this functionality for interactive structure isolation, hole filling, and artefact removal is demonstrated. Building on previous work in the field we introduce highresolution selection volumes, which can be seen as a resolutionbased focus+context metaphor. By utilizing such volumes we present a novel approach to interactive volume editing at subvoxel accuracy. Finally, we introduce a fast technique to paste textures onto isosurfaces in a 3D scalar field. Since the texture resolution is independent of the volume resolution, this technique allows structurealigned textures containing appearance properties or textual information to be used for volume augmentation and annotation. Index Terms—Volume editing, GPU, painting, carving, annotations. 1
Stereo matching using random walks
 In ICPR
, 2008
"... This paper presents a novel twophase stereo matching algorithm using the random walks framework. At first, a set of reliable matching pixels is extracted with prior matrices defined on the penalties of different disparity configurations and Laplacian matrices defined on the neighbourhood informatio ..."
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Cited by 7 (0 self)
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This paper presents a novel twophase stereo matching algorithm using the random walks framework. At first, a set of reliable matching pixels is extracted with prior matrices defined on the penalties of different disparity configurations and Laplacian matrices defined on the neighbourhood information of pixels. Following this, using the reliable set as seeds, the disparities of unreliable regions are determined by solving a Dirichlet problem. The variance of illumination across different images is taken into account when building the prior matrices and the Laplacian matrices, which improves the accuracy of the resulting disparity maps. Even though random walks have been used in other applications, our work is the first application of random walks in stereo matching. The proposed algorithm demonstrates good performance using the Middlebury stereo datasets. 1.
COMBINATORIAL CONTINUOUS MAXIMUM FLOW
, 2011
"... Maximum flow (and minimumcut) algorithms have had a strong impact on computer vision. In particular, graph cuts algorithms provide a mechanism for the discrete optimization of an energy functional which has been used in a variety of applications such as image segmentation, stereo, image stitching an ..."
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Cited by 6 (2 self)
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Maximum flow (and minimumcut) algorithms have had a strong impact on computer vision. In particular, graph cuts algorithms provide a mechanism for the discrete optimization of an energy functional which has been used in a variety of applications such as image segmentation, stereo, image stitching and texture synthesis. Algorithms based on the classical formulation of maxflow defined on a graph are known to exhibit metrication artefacts in the solution. Therefore, a recent trend has been to instead employ a spatially continuous maximum flow (or the dual mincut problem) in these same applications to produce solutions with no metrication errors. However, known fast continuous maxflow algorithms have no stopping criteria or have not been proved to converge. In this work, we revisit the continuous maxflow problem and show that the analogous discrete formulation is different from the classical maxflow problem. We then apply an appropriate combinatorial optimization technique to this combinatorial continuous maxflow (CCMF) problem to find a nulldivergence solution that exhibits no metrication artefacts and may be solved exactly by a fast, efficient algorithm with provable convergence. Finally, by exhibiting the dual problem of our CCMF formulation, we clarify the fact, already proved by Nozawa in the continuous setting, that the maxflow and the total variation problems are not always equivalent.
Hierarchical Exploration of Volumes Using Multilevel Segmentation of the IntensityGradient Histograms
"... histogram from a volume dataset. 2. We mimic the user visual search of shapes in the histogram by recursively segmenting the histogram image using normalized cuts. 3. We construct a multiresolution hierarchy for interactive exploration. 4. Users traverse this hierarchy to discover features in the v ..."
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Cited by 5 (1 self)
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histogram from a volume dataset. 2. We mimic the user visual search of shapes in the histogram by recursively segmenting the histogram image using normalized cuts. 3. We construct a multiresolution hierarchy for interactive exploration. 4. Users traverse this hierarchy to discover features in the volume data and compose meaningful visualizations. Abstract—Visual exploration of volumetric datasets to discover the embedded features and spatial structures is a challenging and tedious task. In this paper we present a semiautomatic approach to this problem that works by visually segmenting the intensitygradient 2D histogram of a volumetric dataset into an exploration hierarchy. Our approach mimics user exploration behavior by analyzing the histogram with the normalizedcut multilevel segmentation technique. Unlike previous work in this area, our technique segments the histogram into a reasonable set of intuitive components that are mutually exclusive and collectively exhaustive. We use informationtheoretic measures of the volumetric data segments to guide the exploration. This provides a datadriven coarsetofine hierarchy for a user to interactively navigate the volume in a meaningful manner. Index Terms—Volume exploration, volume classification, normalized cut, Informationguided exploration. 1
Quadratic Markovian Probability Fields for Image Binary Segmentation
"... Figure 1. Image model generation. I1 and I2 are the original data, α is matting factor and g is the observed image. We present a Markov Random Field model for image binary segmentation that computes the probability that each pixel belongs to a given class. We show that the computation of a real valu ..."
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Cited by 3 (3 self)
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Figure 1. Image model generation. I1 and I2 are the original data, α is matting factor and g is the observed image. We present a Markov Random Field model for image binary segmentation that computes the probability that each pixel belongs to a given class. We show that the computation of a real valued field has noticeable computational and performance advantages with respect to the computation of binary valued field; the proposed energy function is efficiently minimized with standard fast linear order algorithms as Conjugate Gradient or multigrid GaussSeidel schemes. By providing a good initial guesses as starting point we avoid to construct from scratch a new solution, accelerating the computational process, and allow us to naturally implement efficient multigrid algorithms. For applications with limited computational time, a good partial solution can be obtained by stopping the iterations even if the global optimum is not yet reached. We present a meticulous comparison with state of the art methods: Graph Cut, Random Walker and GMMF. The algorithms ’ performance are compared using a cross–validation procedure and an automatics algorithm for learning the parameter set. 1.