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Blending Surface Segmentation and Editing for 3D Models

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第8期28卷 2879-2894页

作者： Zhang, Long Guo, Jianwei Xiao, Jun Zhang, Xiaopeng Yan, Dong-Ming Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China

Recognizing and fitting shape primitives from underlying 3D models are key components of many computer graphics and computer vision applications. Although a vast number of structural recovery methods are available, they usually fail to identify blending surfaces, which corresponds to small transitional regions among relatively large primary patches. To address this issue, we present a novel approach for automatic segmentation and surface fitting with accurate geometric parameters from 3D models, especially mechanical parts. Overall, we formulate the structural segmentation as a Markov random field (MRF) labeling problem. In contrast to existing techniques, we first propose a new clustering algorithm to build superfacets by incorporating 3D local geometric information. This algorithm extracts the general quadric and rolling-ball blending regions, and improves the robustness of further segmentation. Next, we apply a specially designed MRF framework to efficiently partition the original model into different meaningful patches of known surface types by defining the multilabel energy function on the superfacets. Furthermore, we present an iterative optimization algorithm based on skeleton extraction to fit rolling-ball blending patches by recovering the parameters of the rolling center trajectories and ball radius. Experiments on different complex models demonstrate the effectiveness and robustness of the proposed method, and the superiority of our method is also verified through comparisons with state-of-the-art approaches. We further apply our algorithm in applications such as mesh editing by changing the radius of the rolling balls.

关键词： Three-dimensional displays Solid modeling Shape Surface morphology Clustering algorithms Trajectory partitioning algorithms Mesh segmentation structure recovery superfacets rolling-ball blending surface Markov random field

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A LOGIC-TO-LOGIC COMPARATOR FOR VLSI LAYOUT VERIFICATION

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1988年第8期7卷 897-907页

作者： MAURER, PM SCHAPIRA, AD AT&T INFORMAT SYST HOLMDELNJ 07733 AT&T INFORMAT SYST MURRAY HILLNJ 07974

The logic-to-logic comparator (LLC) is described as a tool that verifies VLSI layouts by comparing the logic diagrams for a circuit with structures extracted from the circuit's layout. LLC can operate at either the gate or the transistor level, although the gate level is preferred. It is similar to other graph-isomorphism-based tools, but incorporates some important improvements. The most important of these is a path-comparison algorithm that uses dynamically calculated global information. In addition, LLC contains a gate-matching algorithm that can use certain types of symmetry to distinguish between gates that appear to be identical to less sophisticated algorithms. LLC has been used to verify several commercially available VLSI chips.< >

关键词： Very large scale integration Logic circuits Logic design Information systems partitioning algorithms Data mining Heuristic algorithms Microprocessors Timing Computer science

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TRAM - A DESIGN METHODOLOGY FOR HIGH-PERFORMANCE, EASILY TESTABLE, MULTIMEGABIT RAMS

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IEEE TRANSACTIONS ON COMPUTERS 1988年第10期37卷 1235-1250页

作者： JARWALA, NT PRADHAN, DK AT&T BELL LABS PRINCETONNJ 08540

An architecture is proposed for multimegabit dynamic RAMs (random-access memories) that achieves higher testability and performance than the conventional four-quadrant RAMs. Applying the principle of divide and conquer, the RAM is partitioned into modules, each appearing as the leaf node of a binary interconnect network. Such a network carries the address/data/control bus, permitting the nodes to communicate among themselves as well as with the outside world. This architecture is shown to be easily testable. Parallelism in testing and partial self-test result in a large savings of testing time; the savings is independent of the test algorithm used. Unlike other testability schemes, this approach promises improved performance with only a small increase in chip area. It is also shown that the architecture is easily partionable and restructurable, with potential for yield and reliability improvement

关键词： Integrated Memory Circuits Random Access Storage TRAM Design Methodology High Performance Multimegabit Dynamic RA Ms Testability Performance Yield Reliability Design Methodology Read Write Memory Automatic Testing Random Access Memory Degradation Failure Analysis partitioning algorithms Design For Testability Very Large Scale Integration Charge Transfer

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Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5517-5530页

作者： Bagirov, Adil M. Taheri, Sona Bai, Fusheng Zheng, Fangying Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3353 Australia RMIT Univ Sch Sci Melbourne Vic 3000 Australia Chongqing Normal Univ Natl Ctr Appl Math Chongqing Chongqing 401331 Peoples R China Zhejiang Sci Tech Univ Dept Math Sci Hangzhou 310018 Peoples R China

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints.

关键词： Clustering algorithms partitioning algorithms Optimization Measurement Costs Semisupervised learning Linear programming Cluster analysis discrete gradient method (DGM) nonsmooth optimization pairwise constraints semisupervised clustering (SSC)

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Multisubspace Elastic Network for Multimode Quality-Related Process Monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第9期16卷 5874-5883页

作者： Song, Bing Yan, Huaicheng Shi, Hongbo Tan, Shuai East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China

In this article, a novel multimode quality-related process monitoring method called multisubspace elastic network (MSEN) is proposed. To make mode partition more precisely, this article develops a novel clustering algorithm based on the neighborhood information and subtractive clustering algorithm. In each single mode, unlike conventional process monitoring models that only focus on whether the fault occurs, a novel elastic network based quality-related process monitoring model is established to judge whether the fault is quality related or not. In addition, to select the most suitable monitoring model for online data, the k-nearest neighbor rule and the voting strategy are applied. Once the fault is detected, the contribution plot method is used in both quality-related and quality-unrelated subspace for fault diagnosis. Finally, the proposed MSEN method is tested under the continuous stirred tank reactor to verify its superiority and advantage.

关键词： Clustering algorithms partitioning algorithms Hidden Markov models Informatics Feature extraction Fault detection fault diagnosis multimode process process monitoring quality related

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Adaptive Density Peaks Clustering Based on K-Nearest Neighbor and Gini Coefficient

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IEEE ACCESS 2020年 8卷 113900-113917页

作者： Jiang, Dong Zang, Wenke Sun, Rui Wang, Zehua Liu, Xiyu Shandong Normal Univ Sch Business Jinan 250014 Peoples R China

Density Peaks Clustering (DPC) is a density-based clustering algorithm that has the advantage of not requiring clustering parameters and detecting non-spherical clusters. The density peaks algorithm obtains the actual cluster center by inputting the cutoff distance and manually selecting the cluster center. Thus, the clustering center point is not selected on the basis of considering the whole data set. This paper proposes a method called G-KNN-DPC to calculate the cutoff distance based on the Gini coefficient and K-nearest neighbor. G-KNN-DPC first finds the optimal cutoff distance with Gini coefficient, and then the center point with the K-nearest neighbor. The automatic clustering center method can not only avoid the error that a cluster detects two center points but also effectively solve the traditional DPC algorithm defect that cannot handle complex data sets. Compared with DPC, Fuzzy C-Means, K-means, KDPC and DBSCAN, the proposed algorithm creates better clusters on different data sets.

关键词： Clustering algorithms Classification algorithms partitioning algorithms Clustering methods Kernel Potential energy Indexes Density peaks clustering adaptive algorithm K-nearest neighbor Gini coefficient

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COMPILING FUNCTIONAL PARALLELISM ON DISTRIBUTED-MEMORY SYSTEMS

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IEEE PARALLEL & DISTRIBUTED TECHNOLOGY 1994年第1期2卷 64-76页

作者： PANDE, SS AGRAWAL, DP MAUNEY, J N CAROLINA STATE UNIV DEPT ELECT & COMP ENGNBOX 7911RALEIGHNC 27695

We have developed an automatic compilation method that combines data- and code-based approaches to schedule a program's functional parallelism onto distributed memory systems. Our method works with Sisal, a parallel functional language, and replaces the back end of the Optimizing Sisal Compiler so that it produces code for distributed memory systems. Our extensions allow the compiler to generate code for Intel's distributed-memory Touchstone iPSC/860 machines (Gamma, Delta, and Paragon). The modified compiler can generate a partition that minimizes program completion time (for systems with many processors) or the required number of processors (for systems with few processors). To accomplish this, we have developed a heuristic algorithm that uses the new concept of threshold to treat the problem of scheduling as a trade-off between schedule length and the number of required processors. Most compilers for distributed memory systems force the programmer to partition the data or the program code. This modified version of a Sisal compiler handles both tasks automatically in a unified framework, and lets the programmer compile for a chosen number of processors.

关键词： Parallel processing Program processors Processor scheduling Optimizing compilers Programming profession Scheduling algorithm Heuristic algorithms partitioning algorithms Minimization methods Costs

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On Consistent Entropy-Regularized k-Means Clustering With Feature Weight Learning: Algorithm and Statistical Analyses

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 4779-4790页

作者： Chakraborty, Saptarshi Paul, Debolina Das, Swagatam Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Stanford Univ Dept Stat Stanford CA 94305 USA Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Clusters in real data are often restricted to low-dimensional subspaces rather than the entire feature space. Recent approaches to circumvent this difficulty are often computationally inefficient and lack theoretical justification in terms of their large-sample behavior. This article deals with the problem by introducing an entropy incentive term to efficiently learn the feature importance within the framework of center-based clustering. A scalable block-coordinate descent algorithm, with closed-form updates, is incorporated to minimize the proposed objective function. We establish theoretical guarantees on our method by Vapnik-Chervonenkis (VC) theory to establish strong consistency along with uniform concentration bounds. The merits of our method are showcased through detailed experimental analysis on toy examples as well as real data clustering benchmarks.

关键词： Clustering algorithms Linear programming Entropy partitioning algorithms Behavioral sciences Standards Signal to noise ratio Clustering entropy regularization feature weighting strong consistency Vapnik-Chervonenkis (VC) theory

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Early Termination of CU Partition Based on Boosting Neural Network for 3D-HEVC Inter-Coding

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IEEE ACCESS 2022年 10卷 13870-13883页

作者： Bakkouri, Siham Elyousfi, Abderrahmane Ibn Zohr Univ Fac Sci Comp Syst & Vis Lab Agadir 80000 Morocco Ibn Zohr Univ Natl Engn Sch Appl Sci Dept Comp Sci Agadir 80000 Morocco

As an extension of the High Efficiency Video Coding (HEVC) standard, the 3D-HEVC needs to encode multiple texture videos and depth maps components. In the 3D-HEVC inter-coding test model, a large variety of Coding Unit (CU) sizes are adopted to select the one with the lowest Rate-Distortion (RD) cost as the best CU size. This technique provides the highest achievable coding efficiency, but it brings a huge computational complexity which limits 3D-HEVC from practical applications. In this paper, early termination of CU encoding is proposed to reduce the complexity caused by the CU size splitting process. The proposed algorithm is based on CU homogeneity and a boosting neural network clustering algorithm. The algorithm contained three main steps. The first step is for the extraction of various features from the original encoder. Then, the selection of the features, which had a high correlation with CU partition using a machine learning algorithm. In the second step, a boosting neural network model is used for training the selected features to derive the threshold values for our proposed algorithm. In the final step, an efficient early termination of CU splitting is released for texture videos and depth maps based on the extracted thresholds from the training model. The experimental results show that the proposed algorithm reduces a significant encoding time, while the loss in coding efficiency is negligible.

关键词： Encoding Videos Classification algorithms Machine learning algorithms Boosting Prediction algorithms partitioning algorithms 3D-HEVC feature selection machine learning inter prediction boosting neural network

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HDIHT: A High-Accuracy Distributed Iterative Hard Thresholding Algorithm for Compressed Sensing

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IEEE ACCESS 2020年 8卷 49180-49186页

作者： Chen, Xiaming Qi, Zhuang Xu, Jianlong Shantou Univ Dept Comp Sci Shantou 515063 Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou 515063 Peoples R China

Iterative hard thresholding (IHT) is a beneficial tool for the recovery of sparse vectors in compressed sensing. In this study, we propose a high-accuracy distributed iterative hard thresholding algorithm (HDIHT) with explicit consideration given to the case in which noise is generated. The results of our theoretical analysis show that it is possible to cancel the noise in the HDIHT compared to the IHT. The performance of the HDIHT in the case including noise was equivalent to the classic IHT in the noise-free case. A numerical experiment is also included, and the results are in accordance with the theoretical analysis.

关键词： Compressed sensing Iterative algorithms Random variables Sparse matrices Licenses Estimation partitioning algorithms Distributed learning iterative hard thresholding compressed sensing

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