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Seeded image segmentation is a popular type of supervised image segmentation in computer vision and image processing. Previous methods of seeded image segmentation treat the image as a weighted graph and minimize an energy function on the graph to produce a segmentation. In this paper, we propose to conduct the seeded image segmentation according to the result of a heat diffusion process in which...
Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity...
In this paper, we deal with a generative model for multilabel, interactive segmentation. To estimate the pixel likelihoods for each label, we propose a new higher-order formulation additionally imposing the soft label consistency constraint whereby the pixels in the regions, generated by unsupervised image segmentation algorithms, tend to have the same label. In contrast with previous works which...
Graph cuts have proven useful for image segmentation and for volumetric reconstruction in multiple view stereo. However, solutions are biased: the cost function tends to favour either a short boundary (in 2D) or a boundary with a small area (in 3D). This bias can be avoided by instead minimising the cut ratio, which normalises the cost by a measure of the boundary size. This paper uses ideas from...
Graph-cuts optimization is prevalent in vision and graphics problems. It is thus of great practical importance to parallelize the graph-cuts optimization using today's ubiquitous multi-core machines. However, the current best serial algorithm by Boykov and Kolmogorov (called the BK algorithm) still has the superior empirical performance. It is non-trivial to parallelize as expensive synchronization...
This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we reformulate the problem as an optimization of...
LIDAR-based object detection usually relies on geometric feature extraction, followed by a generative or discriminative classification approach. Instead, we propose to change the way of detecting objects using LIDAR by means of not only a featureless approach, but also inferring context-aware relations of object parts. For the first feature, a coarse-to-fine segmentation based on β-skeleton random...
Acquiring accurate silhouettes has many applications in computer vision. This is usually done through motion detection, or a simple background subtraction under highly controlled environments (i.e. chroma-key backgrounds). Lighting and contrast issues in typical outdoor or office environments make accurate segmentation very difficult in these scenes. In this paper, gradients are used in conjunction...
The swarm intelligence technique is applied in image processing for feature extraction. The perceptual graph is proposed to represent the relationship between adjacent image points. The ant colony system is applied to build the perceptual graph. In the experiments, edge extraction and image segmentation are implemented with the proposed method, which show that the artificial ant swarm can effectively...
In this paper, we address the problem of automatic pre-segmentation for object detection and recognition in remote sensing image processing. It plays an important role in reducing computational burden and increasing efficiency for further image processing and analysis. A visual-attention based saliency computation approach is introduced to select the perceptually salient and highly informative regions...
In this work we explore the application of graph cuts and seed based region growing (SBRG) techniques to segment and detect the boundary of different breast tissue regions in mammograms. The graph cut (GC) is applied with multi-selection of seed labels to provide the hard constraint, whereas the seeds labels are selected based on user defined. The region growing is applied with multi-selection of...
Daily increase in the use of multimedia data brings along the problem of accessing the desired data within the enormous amounts of visual contents. To overcome this problem, the users have preferred internet based search engines. Unfortunately, none of these widely used popular search engines offer a content based solution. In this work, the grouping of the results obtained from the internet image...
Branching morphogenesis is a developmental process shared by many organs, including the submandibular salivary gland. During morphogenesis, cells within the gland undergo rearrangements to cause changes in the overall tissue morphology. This work presents a methodology based on cell-graphs to quantify these changes in cellular arrangements. Multiple confocal images of developing salivary gland organ...
The diaphragm is a thin double-domed muscle that separates the thoracic and abdominal cavities. An accurate delineation of the diaphragm surface will be useful in providing a good region of interest for segmentation problems pertaining to the thoracic and abdominal cavities. In this paper, we present a fully automatic 3D graph-based method for the segmentation of the diaphragm in non-contrast CT data...
We present an iterative model-constrained graph-cut algorithm for the segmentation of Abdominal Aortic Aneurysm (AAA) thrombus. Given an initial segmentation of the aortic lumen, our method automatically segments the thrombus by iteratively coupling intensity-based graph min-cut segmentation and geometric parametric model fitting. The geometric model effectively constrains the graph min-cut segmentation...
This paper presents a new method for the site modeling from aerial image data. Initially 3D lines are extracted by using elevation data obtained by area-based stereo. The grouping process is implemented by using centroid neural network algorithm to classify 3D lines into groups of lines. Using grouped 3D lines, hypothesis selection is carried out based on undirected graph, in which close cycles represent...
In recent years, statistical shape models, of which Active Appearance Models (AAMs) are a subset have been increasingly applied to the automatic segmentation of medical images. AAMs are a local search technique requiring good initialisation. In 3D automatic initialisation can be achieved by multiple initialisations, registration, template matching or by application dependent heuristics. The first...
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution level analysis. At each resolution level, a...
We propose a novel method for detecting characteristic informative phenotype patterns from biomedical images. By building a metric space quantifying the difference between images, we learn the distributions of different classes, and then detect the characteristic regions using graph partition. We show that the detected regions are statistically significant. Our approach can also be used for designing...
We propose a method for simultaneous segmentation of serially acquired magnetic resonance (MR) images. An existing graph-cuts based algorithm is extended and applied to 4-D images. A probabilistic atlas is generated for each baseline scan by intersubject registration of multiple labeled images. The atlases are used for baseline and aligned follow-up images and are combined with an intensity model...
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