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IEEE International Conference on Image Processing (ICIP)

作者： Gueziri, Houssem-Eddine McGuffin, Michael J. Laporte, Catherine Ecole Techonol Super Montreal PQ Canada

ISBN: (纸本)9781479983391

graph-based segmentation methods such as the random walker (RW) are known to be computationally expensive. For high resolution images, user interaction with the algorithm is significantly affected. This paper introduces a novel seeding approach for graph-based segmentation that reduces computation time. Instead of marking foreground and background pixels, the user roughly marks the object boundary forming separate regions. The image pixels are then grouped into a hierarchy of increasingly large layers based on their distance from these markings. Next, foreground and background seeds are automatically generated according to the hierarchical layers of each region. The highest layers of the hierarchy are ignored leading to a significant graph reduction. Finally, validation experiments based on multiple automatically generated input seeds were carried out on a variety of medical images. Results show a significant gain in time for high resolution images using the new approach.

关键词： graph-based segmentation Random Walker graph reduction optimization simulation

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Automatic Object segmentation Using Perceptual Grouping of Regions with Contextual Constraints 5

Automatic Object Segmentation Using Perceptual Grouping of R...

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International Conference on Image Processing Theory Tools and Applications

作者： Zand, Mohsen Doraisamy, Shyamala Halin, Alfian Abdul Mustaffa, Mas Rina Univ Putra Malaysia Fac Comp Sci & Informat Technol Dept Multimedia Serdang 43400 Malaysia

ISBN: (纸本)9781479986378

Image segmentation is still considered a very challenging subject despite years of research effort poured into the field. The problem is exacerbated when there is need for specific object detection. Since objects can be visually non-homogeneous, techniques that attempt to segment images into visually uniform regions using only the bottom-up cues, tend to fail. We propose a novel two-step model that incorporates both bottom-up information and top-down object constraints. Firstly, a set of uniform regions are generated using an extension of contour detection, seeded region growing, and graph-based methods. The second step applies co-occurrence constraints on the image regions in order to perceptually group regions into objects. This unsupervised segmentation process models each object using higher-level knowledge in the form of visual co-occurrences of its constituent parts. Experiments on the horse and ImageCLEF databases show that the proposed technique performs comparably well with existing state-of-the-art techniques.

关键词： Automatic image segmentation graph-based segmentation Object segmentation Contextual relationships

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Automatic Object segmentation Using Perceptual Grouping of Regions with Contextual Constraints

Automatic Object Segmentation Using Perceptual Grouping of R...

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International Conference on Image Processing Theory, Tools and Applications

作者： Mohsen Zand Shyamala Doraisamy Alfian Abdul Halin Mas Rina Mustaffa Department of Multimedia Faculty of Computer Science and Information Technology Universiti Putra Malaysia

ISBN: (纸本)9781479986385

关键词： Automatic image segmentation graph-based segmentation Object segmentation Contextual relationships Object segmentation image segmentation region of an image edge-based segmentation region segmentation texture-based segmentation contour detection Object detection bottom-up

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Learning of perceptual grouping for object segmentation on RGB-D data

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2014年第1期25卷 64-73页

作者： Richtsfeld, Andreas Moerwald, Thomas Prankl, Johann Zillich, Michael Vincze, Markus Vienna Univ Technol Automat & Control Inst ACIN A-1040 Vienna Austria

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation. (C) 2013 The Authors. Published by Elsevier Inc. All rights reserved.

关键词： Computer vision Object segmentation Perceptual organization RGB-D images B-spline fitting Object reconstruction SVM learning graph-based segmentation

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Computational Complexity Analysis of the graph Extraction Algorithm for Volumetric segmentation Method 11

Computational Complexity Analysis of the Graph Extraction Al...

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11th International Conference on Signal Processing and Multimedia Applications (SIGMAP) is part of 11th International Joint Conference on E-Business and Telecommunications (ICETE)

作者： Burdescu, Dumitru Dan Stanescu, Liana Brezovan, Marius Spahiu, Cosmin Stoica Univ Craiova Fac Automat Comp & Elect Comp & Informat Technol Dept Blvd Decebal 107 Dolj 200440 Romania

ISBN: (纸本)9789898565969

The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. Unfortunately there are huge of papers for planar images and segmentation methods and most graph-based for planar images and very few papers for volumetric segmentation methods. The major concept used in graph-based volumetric segmentation method is the concept of homogeneity of regions and thus the edge weights are based on color distance. A huge number of approaches to segmentation are based on finding compact regions in some feature space. A recent technique using feature space regions first transforms the data by smoothing it in a way that preserves boundaries between regions. In this paper we extend our previous papers for planar images by adding a new step in the volumetric segmentation algorithm that allows us to determine regions closer to it. The key to the whole algorithm of volumetric segmentation is the honeycomb. Then the volumetric segmentation module creates virtual cells of prisms with tree-hexagonal structure defined on the set of the image voxels of the input spatial image and a spatial triangular grid graph having tree-hexagons as cells of vertices. The computational complexity analysis shows that our volumetric

关键词： Spatial segmentation graph-based segmentation Color segmentation Syntactic segmentation

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Syntactic-based Region Algorithm for Volumetric segmentation 3

Syntactic-Based Region Algorithm for Volumetric Segmentation

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Burdescu, Dumitru Dan Stanescu, Liana Brezovan, Marius Spahiu, Cosmin Stoica Univ Craiova Comp & Informat Technol Dept Fac Automat Comp & Elect Craiova 200440 Dolj Romania

ISBN: (纸本)9781479930807

Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. The major concept used in graph-based volumetric segmentation method is the concept of homogeneity of regions and thus the edge weights are based on color distance. Recent techniques for planar images using feature space regions transform the data by smoothing it in a way that preserves boundaries between regions. In this paper we extend our previous work for planar images by adding a new step in the volumetric segmentation algorithm that allows us to determine regions closer to it. The key to the whole algorithm of volumetric segmentation is the honeycomb cells. The volumetric segmentation module creates virtual cells of prisms with tree-hexagonal structure defined on the set of the image voxels of the input spatial image and a spatial triangular grid graph having tree-hexagons as cells of vertices.

关键词： Spatial segmentation graph-based segmentation Color segmentation Syntactic segmentation

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Efficient Volumetric segmentation Method

Efficient Volumetric Segmentation Method

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Burdescu, Dumitru Dan Stanescu, Liana Brezovan, Marius Spahiu, Cosmin Stoica Univ Craiova Comp & Informat Technol Dept Fac Automat Comp & Elect Craiova 200440 Dolj Romania

ISBN: (纸本)9788360810583

In this paper we extend our previous work for planar images by adding a new step in the volumetric segmentation algorithm that allows us to determine regions closer to it. There are huge of papers for planar images and segmentation methods and most of them are graph-based for planar images and very few papers for volumetric segmentation methods. However, even if image segmentation is heavily researched field, extending the algorithms to spatial has been proven not to be an easy task. A true volumetric segmentation remains a difficult problem to tackle due to the complex nature of the topology, of spatial objects, the huge amount of data to be processed and the complexity of the algorithms that scale with the new added dimension. The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. A number of approaches to segmentation are based on finding compact regions in some feature space. A recent technique using feature space regions first transforms the data by smoothing it in a way that preserves boundaries between regions. The key to the whole own algorithm of volumetric segmentation is the honeycomb cells. The pre-processing module is used mainly to blur the initial RUB spatial image in order to reduce the image store and to make algorithms to be efficient. Then the volumetric segmentation module creates virtual cells of prisms with tree-hexagonal structure defined on the set of the image voxels of the input spatial image and a volumetric grid graph having tree hexagons as cells of vertices. Early graph-based methods use fixed thresholds and local measures in finding a volumetric segmentation.

关键词： Volumetric segmentation graph-based segmentation Color segmentation Syntactic segmentation

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SPATIOTEMPORAL segmentation FOR STEREOSCOPIC VIDEO

SPATIOTEMPORAL SEGMENTATION FOR STEREOSCOPIC VIDEO

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Zhao, Yu Liu, Yebin Dai, Qionghai Tsinghua Univ Dept Automat Broadband Networks & Digital Media Lab Beijing 100084 Peoples R China

ISBN: (纸本)9781479947171

We propose an efficient and practical spatiotemporal segmentation method for stereoscopic videos. In our framework, an improved graph-based video segmentation method is firstly applied to group pixels with similar color, depth and motion into spatiotemporal superpixels. We then construct a graph over these superpixels and use spectral clustering method to obtain further segmentation results. This approach divides the segmentation into two processes, which are able to generate spatially and temporally coherent segmentations for videos at a fast speed. Additionally the information of depth and motion, which not only guides the connections of pixels in different frames and views but also measures the similarity, improves the quality of segmentation. Experimental results have demonstrated our method is competitive with the state-of-art methods.

关键词： spatiotemporal segmentation stereoscopic video graph-based segmentation spectral clustering

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Application of graph segmentation Method in Thermal Camera Object Detection

Application of Graph Segmentation Method in Thermal Camera O...

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International Conference on Methods and Models in Automation and Robotics

作者： Hung V. NGUYEN Linh Hoai TRAN Department of Automation Control Institute of Science and Technology Hanoi Vietnam School of Electrical Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (纸本)9781479987023

The paper presents the application of graph-based segmentation algorithm in image object detection. The input images are taken from thermal camera for night surveillance application. The graph-based algorithm was selected due to its low complexity, which allows us to process each image with the complexity of O(***(n)) where n is the number of pixels, and due to the fact that thermal images contains smaller number of regions of colors. With the detected regions, some additional measures are used to filter out the artifacts to correctly detect the object in the images. The numerical results have proved the high quality of the proposed solutions.

关键词： graph-based segmentation image segmentation object detection thermal camera

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An extension of the Felzenszwalb-Huttenlocher segmentation to 3D point clouds

An extension of the Felzenszwalb-Huttenlocher segmentation t...

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5th International Conference on Machine Vision (ICMV) - Computer Vision, Image Analysis and Processing

作者： Sima, Mihai-Cotizo Nuechter, Andreas Jacobs Univ Bremen GmbH Automat Grp Sch Engn & Sci D-28759 Bremen Germany

ISBN: (纸本)9780819495877

This paper investigates the segmentation algorithm proposed by Felzenszwalb and Huttenlocher(1) and its compatibility to 3D point clouds acquired with state-of-the-art 3D laser scanners. To use the algorithm, we adapt the range and intensity data to the smoothed graph structure used by the algorithm. We investigate the influence of the algorithm's parameters to its performance and result that are meaningful to both the machines and the humans.

关键词： graph-based segmentation 3D point clouds 3D laser scanner

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