A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇...A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇ s code. Symbolic string matching technique is applied to establish a correspondence between the two consecutive contours. The surface is composed of the pieces reconstructed from the correspondence points. Experimental results show that the proposed method exhibits a good behavior for the quality of surface reconstruction and its time complexity is proportional to mn where m and n are the numbers of vertices of the two consecutive slices, respectively.展开更多
In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth...In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth regenerating(MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating(LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group(4, 2) or(5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.展开更多
As a typical erasure coding choice, Reed-Solomon （RS） codes have such high repair cost that there is a penaltyfor high reliability and storage efficiency, thereby they are not suitable in geo-distributed storage sys...As a typical erasure coding choice, Reed-Solomon （RS） codes have such high repair cost that there is a penaltyfor high reliability and storage efficiency, thereby they are not suitable in geo-distributed storage systems. We present anovel family of concurrent regeneration codes with local reconstruction （CRL） in this paper. The CRL codes enjoy threebenefits. Firstly, they are able to minimize the network bandwidth for node repair. Secondly, they can reduce the numberof accessed nodes by calculating parities from a subset of data chunks and using an implied parity chunk. Thirdly, they arefaster than existing erasure codes for reconstruction in geo-distributed storage systems. In addition, we demonstrate howthe CRL codes overcome the limitations of the Reed-Solomon codes. We also illustrate analytically that they are excellent inthe trade-off between chunk locality and minimum distance. Furthermore, we present theoretical analysis including latencyanalysis and reliability analysis for the CRL codes. By using quantity comparisons, we prove that CRL（6, 2, 2） is only0.657x of Azure LRC（6, 2, 2）, where there are six data chunks, two global parities, and two local parities, and CRL（10,4, 2） is only 0.656x of HDFS-Xorbas（10, 4, 2）, where there are 10 data chunks, four local parities, and two global paritiesrespectively, in terms of data reconstruction times. Our experimental results show the performance of CRL by conductingperformance evaluations in both two kinds of environments： 1） it is at least 57.25% and 66.85% more than its competitorsin terms of encoding and decoding throughputs in memory, and 2） it has at least 1.46x and 1.21x higher encoding anddecoding throughputs than its competitors in JBOD （Just a Bunch Of Disks）. We also illustrate that CRL is 28.79% and30.19% more than LRC on encoding and decoding throughputs in a geo-distributed environment.展开更多
分布式视频压缩感知(Distributed Compressed Video Sensing,DCVS)多假设重构算法将传统视频编码中的多假设预测运动估计思想引入到分布式压缩感知视频编码系统中,改善了对视频序列的重构质量。在该算法中,大变化块采用本帧邻域块信息...分布式视频压缩感知(Distributed Compressed Video Sensing,DCVS)多假设重构算法将传统视频编码中的多假设预测运动估计思想引入到分布式压缩感知视频编码系统中,改善了对视频序列的重构质量。在该算法中,大变化块采用本帧邻域块信息作为参考,而当本帧邻域块含有较多纹理和细节时,算法性能有待提高。为此,对非局部相似性的思想进行改进,提出基于加权非局部相似性的分布式视频压缩感知多假设重构算法。在该算法中,对大变化块中的纹理块采用加权非局部相似性在相邻已重构帧中寻找自相似块,最终生成辅助重构信息块;对于非纹理块,则简单利用加权非局部相似性生成相似块。对不同特点的视频序列的仿真实验结果表明,改进后的算法有效改善了视频序列的重构质量,具有较优的重构SSIM,PSNR指标,其中PSNR约提高1dB。展开更多
文摘A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇ s code. Symbolic string matching technique is applied to establish a correspondence between the two consecutive contours. The surface is composed of the pieces reconstructed from the correspondence points. Experimental results show that the proposed method exhibits a good behavior for the quality of surface reconstruction and its time complexity is proportional to mn where m and n are the numbers of vertices of the two consecutive slices, respectively.
基金supported in part by the National Natural Science Foundation of China (61640006, 61572188)the Natural Science Foundation of Shaanxi Province, China (2015JM6307, 2016JQ6011)the project of science and technology of Xi’an City (2017088CG/RC051(CADX002))
文摘In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth regenerating(MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating(LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group(4, 2) or(5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.
文摘As a typical erasure coding choice, Reed-Solomon （RS） codes have such high repair cost that there is a penaltyfor high reliability and storage efficiency, thereby they are not suitable in geo-distributed storage systems. We present anovel family of concurrent regeneration codes with local reconstruction （CRL） in this paper. The CRL codes enjoy threebenefits. Firstly, they are able to minimize the network bandwidth for node repair. Secondly, they can reduce the numberof accessed nodes by calculating parities from a subset of data chunks and using an implied parity chunk. Thirdly, they arefaster than existing erasure codes for reconstruction in geo-distributed storage systems. In addition, we demonstrate howthe CRL codes overcome the limitations of the Reed-Solomon codes. We also illustrate analytically that they are excellent inthe trade-off between chunk locality and minimum distance. Furthermore, we present theoretical analysis including latencyanalysis and reliability analysis for the CRL codes. By using quantity comparisons, we prove that CRL（6, 2, 2） is only0.657x of Azure LRC（6, 2, 2）, where there are six data chunks, two global parities, and two local parities, and CRL（10,4, 2） is only 0.656x of HDFS-Xorbas（10, 4, 2）, where there are 10 data chunks, four local parities, and two global paritiesrespectively, in terms of data reconstruction times. Our experimental results show the performance of CRL by conductingperformance evaluations in both two kinds of environments： 1） it is at least 57.25% and 66.85% more than its competitorsin terms of encoding and decoding throughputs in memory, and 2） it has at least 1.46x and 1.21x higher encoding anddecoding throughputs than its competitors in JBOD （Just a Bunch Of Disks）. We also illustrate that CRL is 28.79% and30.19% more than LRC on encoding and decoding throughputs in a geo-distributed environment.
文摘分布式视频压缩感知(Distributed Compressed Video Sensing,DCVS)多假设重构算法将传统视频编码中的多假设预测运动估计思想引入到分布式压缩感知视频编码系统中,改善了对视频序列的重构质量。在该算法中,大变化块采用本帧邻域块信息作为参考,而当本帧邻域块含有较多纹理和细节时,算法性能有待提高。为此,对非局部相似性的思想进行改进,提出基于加权非局部相似性的分布式视频压缩感知多假设重构算法。在该算法中,对大变化块中的纹理块采用加权非局部相似性在相邻已重构帧中寻找自相似块,最终生成辅助重构信息块;对于非纹理块,则简单利用加权非局部相似性生成相似块。对不同特点的视频序列的仿真实验结果表明,改进后的算法有效改善了视频序列的重构质量,具有较优的重构SSIM,PSNR指标,其中PSNR约提高1dB。
