Visual-hint Boundary to Segment Algorithm for Image Segmentation

Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.Comment: 45 page

An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

A comparative study of image segmentation by application of normalized cut on graphs

Orientador: Marco Antonio Garcia de CarvalhoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: O particionamento de grafos tem sido amplamente utilizado como meio de segmentação de imagens. Uma das formas de particionar grafos é por meio de uma técnica conhecida como Corte Normalizado, que analisa os autovetores da matriz laplaciana de um grafo e utiliza alguns deles para o corte. Essa dissertação propõe o uso de Corte Normalizado em grafos originados das modelagens por Quadtree e Árvore dos Componentes a fim de realizar segmentação de imagens. Experimentos de segmentação de imagens por Corte Normalizado nestas modelagens são realizados e um benchmark específico compara e classifica os resultados obtidos por outras técnicas propostas na literatura específica. Os resultados obtidos são promissores e nos permitem concluir que o uso de outras modelagens de imagens por grafos no Corte Normalizado pode gerar melhores segmentações. Uma das modelagens pode inclusive trazer outro benefício que é gerar um grafo representativo da imagem com um número menor de nós do que representações mais tradicionaisAbstract: The graph partitioning has been widely used as a mean of image segmentation. One way to partition graphs is through a technique known as Normalized Cut, which analyzes the graph's Laplacian matrix eigenvectors and uses some of them for the cut. This work proposes the use of Normalized Cut in graphs generated by structures based on Quadtree and Component Tree to perform image segmentation. Experiments of image segmentation by Normalized Cut in these models are made and a specific benchmark compares and ranks the results obtained by other techniques proposed in the literature. The results are promising and allow us to conclude that the use of other image graph models in the Normalized Cut can generate better segmentations. One of the structures can also bring another benefit that is generating an image representative graph with fewer graph nodes than the traditional representationsMestradoTecnologia e InovaçãoMestre em Tecnologi

計算幾何学を用いた高品質画像検索システム

Tohoku University徳山豪課

計算幾何学を用いた高品質画像検索システム

Tohoku University徳山豪課

Methods for Real-time Visualization and Interaction with Landforms

This thesis presents methods to enrich data modeling and analysis in the geoscience domain with a particular focus on geomorphological applications. First, a short overview of the relevant characteristics of the used remote sensing data and basics of its processing and visualization are provided. Then, two new methods for the visualization of vector-based maps on digital elevation models (DEMs) are presented. The first method uses a texture-based approach that generates a texture from the input maps at runtime taking into account the current viewpoint. In contrast to that, the second method utilizes the stencil buffer to create a mask in image space that is then used to render the map on top of the DEM. A particular challenge in this context is posed by the view-dependent level-of-detail representation of the terrain geometry. After suitable visualization methods for vector-based maps have been investigated, two landform mapping tools for the interactive generation of such maps are presented. The user can carry out the mapping directly on the textured digital elevation model and thus benefit from the 3D visualization of the relief. Additionally, semi-automatic image segmentation techniques are applied in order to reduce the amount of user interaction required and thus make the mapping process more efficient and convenient. The challenge in the adaption of the methods lies in the transfer of the algorithms to the quadtree representation of the data and in the application of out-of-core and hierarchical methods to ensure interactive performance. Although high-resolution remote sensing data are often available today, their effective resolution at steep slopes is rather low due to the oblique acquisition angle. For this reason, remote sensing data are suitable to only a limited extent for visualization as well as landform mapping purposes. To provide an easy way to supply additional imagery, an algorithm for registering uncalibrated photos to a textured digital elevation model is presented. A particular challenge in registering the images is posed by large variations in the photos concerning resolution, lighting conditions, seasonal changes, etc. The registered photos can be used to increase the visual quality of the textured DEM, in particular at steep slopes. To this end, a method is presented that combines several georegistered photos to textures for the DEM. The difficulty in this compositing process is to create a consistent appearance and avoid visible seams between the photos. In addition to that, the photos also provide valuable means to improve landform mapping. To this end, an extension of the landform mapping methods is presented that allows the utilization of the registered photos during mapping. This way, a detailed and exact mapping becomes feasible even at steep slopes

An Approach for Segmentation of Colored Images with Seeded Spatial Enhancement

In the image analysis, image segmentation is the operation that divides image into set of different segments. The work deals about common color image segmentation techniques and methods. Image enhancement is done using four connected approach for seed selection of the image. An algorithm is implemented on the basis of manual seed selection. It select a seed point in an image an then check for its four neighbor pixels connected to that particular seed point. And segment that image in foreground and background framing. At the end, the evaluation criterion will be introduced and applied on the algorithms results. Five most used image segmentation algorithms, namely, efficient graph based, K means, Mean shift, Expectation maximization and hybrid method are compared with implemented algorithm

Color and depth based image segmentation using a game-theoretic approach

In this thesis a new game theoretic approach to image segmentation is proposed. It is an attempt to give a contribution to a new interesting research area in image processing, which tries to boost image segmentation combining information about appareance (e.g. color) and information about spatial arrangement. The proposed algorithm firstly partition the image into small subsets of pixels, in order to reduce computational complexity of the subsequent phases. Two different distance measures between each pair of pixels subsets are then computed, one regarding color information and one based on spatial-geometric information. A similarity measure between each pair of pixel subset is then computed, exploiting both color and spatial data. Finally, pixels subsets are modeled into an evolutionary game in order to group similar pixels into meaningful segments. After a brief review of image segmentation approaches, the proposed algorithm is described and different experimental tests are carried up to evaluate its segmentation performanc