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International Conference on Intelligent Earth Observing and Applications (IEOAs)

作者： Wang, Geng Wang, Guanghui Yu, Mei Cui, Chengling China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China NASG Satellite Surveying & Mapping Applicat Ctr Beijing 100830 Peoples R China

ISBN: (纸本)9781510600492

Seamless contiguity is the key technology for parallel segmentation of remote sensing data with large quantities. It can be effectively integrate fragments of the parallel processing into reasonable results for subsequent processes. There are numerous methods reported in the literature for seamless contiguity, such as establishing buffer, area boundary merging and data sewing. et. We proposed a new method which was also based on building buffers. The seamless contiguity processes we adopt are based on the principle: ensuring the accuracy of the boundary, ensuring the correctness of topology. Firstly, block number is computed based on data processing ability, unlike establishing buffer on both sides of block line, buffer is established just on the right side and underside of the line. Each block of data is segmented respectively and then gets the segmentation objects and their label value. Secondly, choose one block(called master block) and do stitching on the adjacent blocks(called slave block), process the rest of the block in sequence. Through the above processing, topological relationship and boundaries of master block are guaranteed. Thirdly, if the master block polygons boundaries intersect with buffer boundary and the slave blocks polygons boundaries intersect with block line, we adopt certain rules to merge and trade-offs them. Fourthly, check the topology and boundary in the buffer area. Finally, a set of experiments were conducted and prove the feasibility of this method. This novel seamless contiguity algorithm provides an applicable and practical solution for efficient segmentation of massive remote sensing image.

关键词： remote sensing image parallel segmentation seamless contiguity buffer topology

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The parallel segmentation Algorithm Based on Pyramid Image for High Spatial Resolution Remote Sensing Image

The Parallel Segmentation Algorithm Based on Pyramid Image f...

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18th China National Symposium on Remote Sensing - Remote Sensing of the Environment

作者： Huang Lingcao Zhang Guo Zhou Chunxia Wang Yanan Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Wuhan Univ Chinese Antarct Ctr Surveying & Mapping Wuhan 430079 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

ISBN: (纸本)9781628411768

Image segmentation is the foundation of the object-based and automatic interpretation of remote sensing images, but the high-resolution remote sensing image data is generally large, for this problem, the traditional approach is generally processing in sub-block, and then merge the results, but because of the complexity of the nature object, the merging result is not satisfied, and the segmentation algorithm is often more complex to calculate time-consuming, and it affect the image automatic interpretation of real-time. In this paper, we propose a parallel segmentation algorithm based on pyramid image, first of all, we create the pyramid image and segment it with the initial homogeneous regions were got, it divide the data according to the initial homogeneous regions and segment them from the top of pyramid image to the bottom with data parallelism, and it improve segmented efficiency, at the same time, it can avoid the problem of "merging line" when merging of the segmenting results in different image block. Experimental results show that the result of this algorithm is almost the same as the result of Mean Shift algorithm segmentation case;it says that this algorithm is correct and reliability, it also shows that this algorithm is efficiency by comparing the use of time between serial segmentation and parallel segmentation.

关键词： High spatial resolution Remote Sensing Image Mean Shift parallel segmentation Pyramid Image

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High-speed parallel segmentation algorithms of MeanShift for litchi canopies based on Spark and Hadoop

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2024年第3期15卷

作者： Xiong, Hongyi Wang, Jianhua Xiao, Yiming Xiao, Fangjun Huang, Renhuang Hong, Licong Wu, Bofei Zhou, Jinfeng Long, Yongbin Lan, Yubin South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou 510642 Guangdong Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Guangdong Peoples R China Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti Guangzhou 510642 Guangdong Peoples R China

The MeanShift algorithm is a nonparametric method based on gradient ascent and it can effectively handle complex variations in lychee orchard scenes as well as changes in lychee tree canopies due to its adaptability, multi-scale analysis capabilities, and robustness, making it widely used in the segmentation processing of drone-based remote sensing images of lychee orchards. However, due to the high computational complexity of the MeanShift algorithm, its performance in processing large-scale drone remote sensing images of lychee tree canopies is not highly efficient, leading to low segmentation efficiency, which hampers its broader application. To address these issues, this study proposes high-speed MeanShift parallel segmentation algorithms for drone remote sensing images of lychee tree canopies based on MapReduce and Spark distributed computing frameworks. In this study, a cluster consisting of four nodes with Hadoop and Spark was set up, and 4000 drone remote sensing images were used as test data to evaluate the algorithm. Experimental results show that, the MeanShift algorithm based on MapReduce reduced the task execution time by 86.1% compared to the traditional MeanShift algorithm, while the MeanShift algorithm based on Spark reduced the task execution time by 88.0%, without compromising segmentation accuracy. The MeanShift parallel segmentation algorithm based on Hadoop and Spark platform can overcome the bottleneck of task execution efficiency and significantly enhance computational speed on a single machine.

关键词： Spark MapReduce MeanShift algorithm parallel segmentation drone remote sensing of lychee tree canopy images

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REM-Seg: A Robust EM Algorithm for parallel segmentation and Registration of Point Clouds

REM-Seg: A Robust EM Algorithm for Parallel Segmentation and...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Benjamin Eckart Alonzo Kelly Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781467363563

For purposes of real-time 3D sensing, it is important to be able to quickly register together incoming point cloud data. In this paper, we devise a method to quickly and robustly decompose large point clouds into a relatively small number of meaningful surface patches from which we register new data points. The surface patch representation sidesteps the costly problem of matching points to points since incoming data only need to be compared with the patches. The chosen parametrization of the patches (as Gaussians) leads to a smooth data likelihood function with a well-defined gradient. This representation thus forms the basis for a robust and efficient registration algorithm using a parallelized gradient descent implemented on a GPU using CUDA. We use a modified Gaussian Mixture Model (GMM) formulation solved by Expectation Maximization (EM) to segment the point cloud and an annealing gradient descent method to find the 6-DOF rigid transformation between the incoming point cloud and the segmented set of surface patches. We test our algorithm, Robust EM segmentation (REM-Seg), against other GPU-accelerated registration algorithms on simulated and real data and show that our method scales well to large numbers of points, has a wide range of convergence, and is suitably accurate for 3D registration.

关键词： parallel segmentation Registration EM Registration expectation-maximisation algorithm Clouds Large Numbers Patches wide-range Robustness Gauss

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SYSTEM segmentation FOR parallel DIAGNOSIS OF COMPUTERS

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IEEE TRANSACTIONS ON COMPUTERS 1971年第3期C 20卷 261-+页

作者： RAMAMOORTHY, CV CHANG, LC IEEE

In this paper we develop methods of segmenting a large system into smaller subsystems so that the letter can be diagnosed in parallel. Two basic techniques called serial segmentation and parallel segmentation are introduced. Furthermore, an algorithm is developed for the purpose of reducing the complexity and the size of the large subsystem.

关键词： Computer diagnosis computer system segmentation graph theory parallel diagnosis parallel segmentation serial segmentation.

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parallel 3D Split and Merge segmentation with Oriented Boundary Graph

Parallel 3D Split and Merge Segmentation with Oriented Bound...

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16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision

作者： Baldacci, F. Desbarats, P. Univ Bordeaux 1 LaBRI F-33405 Talence France

ISBN: (纸本)9788086943169

In this paper, we present how 3D split and merge segmentation using topological and geometrical structuring with an Oriented Boundary Graph may be optimized by parallel algorithms. This structuring allows to implement efficiently split and merge operations, but since these treatments have often to be applied with large images, we have studied how to improve performances by parallelizing this process. After a short description of the structuring 0 model and its construction, we describe algorithms for parallelizing the construction of the structuring and describe how this model can be maintained while using parallel processes. We explain the way of partitioning data for use with multiprocessor systems, and extension for use with NUMA architectures and graphics processing units (GPU) is described. Exemples on two medical images of different sizes is presented and execution time will be given.

关键词： parallel segmentation Split and Merge 3D Topological structuring Topological graph NUMA architecture Medical image segmentation

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Remote Sensing Image segmentation Based on and Cloud Model and parallel Mechanism

Remote Sensing Image Segmentation Based on and Cloud Model a...

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2nd International Conference on Material and Manufacturing Technology (ICMMT 2011)Progress Meeting of the Royal-Society-of-Tropical-Medicine-and-Hygiene's Annual Research

作者： Liu Yanli Jiang Hua Xu Gang Guilin Univ Elect Technol Sch Comp & Control Guilin Peoples R China Guilin Univ Elect Technol Sch Elect Engn &Automat Guilin Peoples R China

ISBN: (纸本)9783037852521

This paper discusses the uncertainty in remote sensing image segmentation by studying the uncertainty in cloud model, qualitative to quantitative conversion, proposed one way to study the image segmentation based on the combination of parallel mechanism and cloud mode. Proved by experiments, this method can better describe the uncertainty of image target. It can quickly and accurately segment the target, the efficiency is far superior then the traditional image segmentation algorithms.

关键词： cloud model uncertainty parallel segmentation image segmentation

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"RAPID" REGIONS-OF-INTEREST DETECTION IN BIG HISTOPATHOLOGICAL IMAGES

"RAPID" REGIONS-OF-INTEREST DETECTION IN BIG HISTOPATHOLOGIC...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Sulimowicz, Li Ahmad, Ishfaq Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781509060672

The sheer volume and size of histopathological images (e.g., 10(6) MPixel) underscores the need for faster and more accurate Regions-of-interest (ROI) detection algorithms. In this paper, we propose such an algorithm, which has four main components that help achieve greater accuracy and faster speed: First, while using coarse-to-fine topology preserving segmentation as the baseline, the proposed algorithm uses a superpixel regularity optimization scheme for avoiding irregular and extremely small superpixels. Second, the proposed technique employs a prediction strategy to focus only on important superpixels at finer image levels. Third, the algorithm reuses the information gained from the coarsest image level at other finer image levels. Both the second and the third components drastically lower the complexity. Fourth, the algorithm employs a highly effective parallelization scheme using adaptive data partitioning, which gains high speedup. Experimental results, conducted on the BSD500 [1] and 500 whole-slide histological images from the National Lung Screening Trial (NLST)(1) dataset, confirm that the proposed algorithm gained 13 times speedup compared with the baseline, and around 160 times compared with SLIC [11], without losing accuracy.

关键词： Superpixels regions-of-interest detection big histopathological image parallel segmentation

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A Novel Symbolic Representation Based on Fast segmentation

A Novel Symbolic Representation Based on Fast Segmentation

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Yin, Hong Yang, Shuqiang Yin, Ping Jin, Songchang Zhao, Hui Natl Univ Def Technol Sch Comp Sci Changsha Hunan Peoples R China Infrastruct Barrack Dept Guangzhou Mil Reg Guangzhou Guangdong Peoples R China XiangYang Sch NCOs of Mil Sch Econ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783038351153

Symbolic representation of time series has recently attracted a lot of research interest. This is a difficult problem because of the high dimensionality of the data, particularly when the length of the time series becomes longer. In this paper, we introduce a new symbolic representation based on fast segmentation, called the trend feature symbols approximation (TFSA). The experimental results show that compared to some method, the segmentation efficiency of TFSA is improved.

关键词： time series symbolic data mining parallel segmentation trend feature

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Open-source software platform for medical image segmentation applications 13

Open-source software platform for medical image segmentation...

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13th International Conference on Medical Information Processing and Analysis

作者： Namias, R. D'Amato, J. P. del Fresno, M. UNR CONICET UAM France CIFASIS Rosario Santa Fe Argentina Univ Nacl Ctr Inst PLADEMA Tandil Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina CIC PBA Buenos Aires DF Argentina

ISBN: (数字)9781510616349

ISBN: (纸本)9781510616349

Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We include the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

关键词： Medical Imaging Analysis Multiple segmentation Framework parallel segmentation

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