Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational eff...
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Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency;moreover, an application on image segmentation verifies its facilitation for traffic scene analysis.
In this paper,a data-driven linear clustering(DLC)method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities.A large substation load dataset with annu...
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In this paper,a data-driven linear clustering(DLC)method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities.A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for *** optimal autoregressive integrated moving average(ARIMA)models are constructed for the sum series of each obtained cluster to forecast their respective future ***,the system load forecasting result is obtained by summing up all the ARIMA *** error analysis and application results,it is both theoretically and practically proved that the proposed DLC method can reduce random forecasti ng errors while guaranteeing modelling accuracy,so that a more stable and precise system load forecasting result can be obtained.
We propose a linear clustering approach for large datasets of molecular geometries produced by high-throughput molecular dynamics simulations (e.g., protein folding and protein-ligand docking simulations). To this sco...
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ISBN:
(纸本)9780769547497
We propose a linear clustering approach for large datasets of molecular geometries produced by high-throughput molecular dynamics simulations (e.g., protein folding and protein-ligand docking simulations). To this scope, we transform each three-dimensional (3D) molecular conformation into a single point in the 3D space reducing the space complexity while still encoding the molecular similarities and geometries. We assign an identifier to each single 3D point mapping a docked ligand, generate a tree from the whole space, and apply a tree-based clustering on the reduced conformation space that identifies most dense hyperspaces. We adapt our method for MapReduce and implement it in Hadoop. The load-balancing, fault-tolerance, and scalability in MapReduce allows screening of very large conformation datasets not approachable with traditional clustering methods. We analyze results for datasets with different concentrations of optimal solutions, and draw conclusions about the limitations and usability of our method. The advantages of this approach make it attractive for complex applications in real-world high-throughput molecular simulations.
Task graph scheduling has been found effective in performance prediction and optimization of parallel applications. A number of static scheduling algorithms have been proposed for task graph execution on parallel mach...
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Task graph scheduling has been found effective in performance prediction and optimization of parallel applications. A number of static scheduling algorithms have been proposed for task graph execution on parallel machines. Such an approach cannot be adapted to changes in values of program parameters and the number of processors and it also cannot handle large task graphs. In this paper, we model parallel computation using parameterized task graphs which represent coarse-grain parallelism independent of the problem size. We present a symbolic scheduling algorithm for a parameterized task graph which first derives linear clusters and then assigns task clusters to processors. The runtime system executes clusters on each processor in a multi-threaded fashion. The experiments using various scientific computing kernel benchmarks show that our method delivers compact and symbolic schedules with performance highly competitive to static approaches. (C) 2004 Elsevier Inc. All rights reserved.
Privacy preserving data clustering is a useful method for extracting intrinsic cluster structures from distributed databases keeping personal privacy. In a previous research, a model of performing Fuzzy c-Lines cluste...
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ISBN:
(纸本)9781665499248
Privacy preserving data clustering is a useful method for extracting intrinsic cluster structures from distributed databases keeping personal privacy. In a previous research, a model of performing Fuzzy c-Lines clustering was proposed, where a privacy preserving scheme of k-means-type model was adopted with cryptographic calculation. This paper further improves the model for handling incomplete data ignoring the influences of missing values. The element-wise clustering criterion enables to derive local principal component vectors in each data sources by considering minimization of low-rank approximation of observed elements only. Then, fuzzy memberships of each object are calculated in a collaborative manner among organizations, where partial distances between objects and prototypes are derived with cryptographic framework so that intra-organization information is kept secret. The characteristic features of the proposed method are demonstrated through numerical experiments.
Ellipse fitting is a fundamental yet critical task in computer vision, and the development of robust and accurate algorithms is crucial for various applications. In this study, we propose a triple-stage robust ellipse...
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Ellipse fitting is a fundamental yet critical task in computer vision, and the development of robust and accurate algorithms is crucial for various applications. In this study, we propose a triple-stage robust ellipse fitting algorithm to address the challenges posed by noise and outliers in the input data. Specifically, to overcome the sensitivity of existing methods to outliers, we introduce an adaptive outlier removal (AOR) algorithm. This algorithm dynamically removes outliers based on the probability density of all data points, eliminating the need for manual parameter adjustment and enhancing robustness to outliers. Furthermore, we tackle the issue of multiple ellipses in the input data by projecting the filtered points into the polar coordinate system. The points are then divided into equal intervals based on the polar angle, facilitating linear clustering to identify the point sets belonging to candidate ellipses, which helps to avoid erroneous fits and improve accuracy. Finally, to avoid solving the geometric distance between the point and the quadratic curve, a simplified ellipse fitting objective function and its corresponding optimization scheme are developed, in which the ellipse parameters are iteratively solved. To verify the universality and accuracy of the algorithm, we tested it on both synthetic data and real-world images from various scenarios with state-of-the-art approaches. Additionally, experiments have been carried out on a physical spacecraft pose measurement platform. The experimental results demonstrate that the algorithm exhibits excellent performance in terms of fitting accuracy and robustness, with a position estimation error of less than 2 mm and an attitude estimation error of less than 0.1(degrees).
作者：
Yang, BishengZhang, YunfeiLu, FengWuhan Univ
State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Chinese Acad Sci
Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China
Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygo...
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Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.
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