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

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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A comparative study of some methods for color medical images segmentation

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2011年第1期2011卷 1-12页

作者： Stanescu, Liana Burdescu, Dumitru Dan Brezovan, Marius Univ Craiova Fac Automat Comp & Elect Craiova 200440 Romania

The aim of this article is to study the problem of color medical images segmentation. The images represent pathologies of the digestive tract such as ulcer, polyps, esophagites, colitis, or ulcerous tumors, gathered with the help of an endoscope. This article presents the results of an objective and quantitative study of three segmentation algorithms. Two of them are well known: the color set back-projection algorithm and the local variation algorithm. The third method chosen is our original visual feature-based algorithm. It uses a graph constructed on a hexagonal structure containing half of the image pixels in order to determine a forest of maximum spanning trees for connected component representing visual objects. This third method is a superior one taking into consideration the obtained results and temporal complexity. These three methods were successfully used in generic color images segmentation. In order to evaluate these segmentation algorithms, we used error measuring methods that quantify the consistency between them. These measures allow a principled comparison between segmentation results on different images, with differing numbers of regions generated by different algorithms with different parameters.

关键词： graph-based segmentation color segmentation segmentation evaluation error measures

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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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Efficient Threshold for Volumetric segmentation 7

Efficient Threshold for Volumetric Segmentation

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7th International Conference on Digital Image Processing (ICDIP)

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

ISBN: (纸本)9781628418293

Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

关键词： Volumetric segmentation graph-based segmentation graph algorithms Threshold Dissimilarity

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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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An Efficient and Adaptive Threshold of Volumetric segmentation

An Efficient and Adaptive Threshold of Volumetric Segmentati...

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Burdescu, Dumitru Dan Brezovan, Marius Stanescu, Liana Spahiu, Cosmin Stoica Slabu, Florin Univ Craiova Fac Automat Comp & Elect Bvd Decebal 107 Craiova 200440 Dolj Romania

ISBN: (纸本)9783319164861;9783319164854

Image segmentation plays a crucial role in effective understanding of digital images. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim of this paper is to present a new and efficient method to detect visual objects from color spatial images. The presented method uses a general-purpose volumetric segmentation algorithm, based on a unified framework that uses a virtual tree-hexagonal structure defined on the set of image voxels.

关键词： Volumetric segmentation graph-based segmentation Adaptive algorithms Threshold

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segmentation and Classification of Range Image from an Intelligent Vehicle in Urban Environment

Segmentation and Classification of Range Image from an Intel...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Zhu, Xiaolong Zhao, Huijing Liu, Yiming Zhao, Yipu Zha, Hongbin Peking Univ State Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9781424466757

As the rapid development of sensing and mapping techniques, it becomes a well-known technology that a map of complex environment can be generated using a robot carrying sensors. However, most of the existing researches represent environments directly using the integration of point clouds or other low-level geometric primitives. It remains an open problem to automatically convert these low-level map representations to semantic descriptions in order to effectively support high-level decision of a robot. based on another representation of 3D point clouds, i.e. range image, this paper proposes a framework of segmentation and classification of range image, the objective of which is to annotate class labels to the data clusters that are obtained through a graph-based segmentation. Experimental results are presented and evaluated demonstrating that the proposed algorithm has efficiency in understanding the semantic knowledge of a large dynamic urban outdoor environment.

关键词： 3D point cloud integration dynamic urban outdoor environment graph theory graph-based segmentation image classification image segmentation intelligent robots intelligent vehicle low-level geometric primitives mobile robots range image classification range image segmentation robot carrying sensors robot vision semantic knowledge

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segmentation of Subcutaneous Fat within Mouse Skin in 3D OCT Image Data using Random Forests

Segmentation of Subcutaneous Fat within Mouse Skin in 3D OCT...

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Conference on Medical Imaging - Image Processing

作者： Kepp, Timo Droigk, Christine Casper, Malte Evers, Michael Salma, Nunciada Manstein, Dieter Handels, Heinz Univ Lubeck Inst Med Informat Lubeck Germany Univ Lubeck Inst Biomed Opt Lubeck Germany Massachusetts Gen Hosp Cutaneous Biol Res Ctr Boston MA 02114 USA

ISBN: (数字)9781510616387

ISBN: (纸本)9781510616387

Cryolipolysis is a well-established cosmetic procedure for non-invasive local fat reduction. This technique selectively destroys subcutaneous fat cells using controlled cooling. Thickness measurements of subcutaneous fat were conducted using a mouse model. For detailed examination of mouse skin optical coherence tomography (OCT) was performed, which is a non-invasive imaging modality. Due to a high number of image slices manual delineation is not feasible. Therefore, automatic segmentation algorithms are required. In this work an algorithm for the automatic 3D segmentation of the subcutaneous fat layer is presented, which is based on a random forest classification followed by a graph-based refinement step. Our approach is able to accurately segment the subcutaneous fat layer with an overall average symmetric surface distance of 11.80 +/- 6.05 mu m and Dice coefficient of 0.921 +/- 0.045. Furthermore, it was shown that the graph-based refining step leads to increased accuracy and robustness of the segmentation results of the random forest classifier.

关键词： optical coherence tomography random forest classification graph-based segmentation

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USER-GUIDED graph REDUCTION FOR FAST IMAGE segmentation

USER-GUIDED GRAPH REDUCTION FOR FAST IMAGE SEGMENTATION

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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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A visual object segmentation algorithm with spatial and temporal coherence inspired by the architecture of the visual cortex

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COGNITIVE PROCESSING 2022年第1期23卷 27-40页

作者： Ramirez-Quintana, Juan A. Rangel-Gonzalez, Raul Chacon-Murguia, Mario, I Ramirez-Alonso, Graciela Tecnol Nacl Mexico IT Chihuahua Grad & Res Dept Av Tecnol 2909 Chihuahua 31310 Mexico Intel Tecnol Mexico Guadalajara Jalisco Mexico Univ Autonoma Chihuahua Fac Ingn Chihuahua Chihuahua Mexico

Scene analysis in video sequences is a complex task for a computer vision system. Several schemes have been addressed in this analysis, such as deep learning networks or traditional image processing methods. However, these methods require thorough training or manual adjustment of parameters to achieve accurate results. Therefore, it is necessary to develop novel methods to analyze the scenario information in video sequences. For this reason, this paper proposes a method for object segmentation in video sequences inspired by the structural layers of the visual cortex. The method is called Neuro-Inspired Object segmentation, SegNI. SegNI has a hierarchical architecture that analyzes object features such as edges, color, and motion to generate regions that represent the objects in the scenario. The results obtained with the Video segmentation Benchmark VSB100 dataset demonstrate that SegNI can adapt automatically to videos with scenarios that have different nature, composition, and different types of objects. Also, SegNI adapts its processing to new scenario conditions without training, which is a significant advantage over deep learning networks.

关键词： Video object segmentation Neuro-inspired algorithms Visual cortex graph-based segmentation

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