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Generalised Median of a Set of Correspondences Based on the Hamming Distance

Generalised Median of a Set of Correspondences Based on the ...

Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR) and Statistical Techniques in Pattern Recognition (SPR)

作者： Francisco Moreno-Garcia, Carlos Serratosa, Francesc Cortes, Xavier Univ Rovira & Virgili Dept Engn Informat & Matemat Tarragona Spain

ISBN: (纸本)9783319490557;9783319490540

A correspondence is a set of mappings that establishes a relation between the elements of two data structures (i.e. sets of points, strings, trees or graphs). If we consider several correspondences between the same two structures, one option to define a representative of them is through the generalised median correspondence. In general, the computation of the generalised median is an NP-complete task. In this paper, we present two methods to calculate the generalised median correspondence of multiple correspondences. The first one obtains the optimal solution in cubic time, but it is restricted to the Hamming distance. The second one obtains a sub-optimal solution through an iterative approach, but does not have any restrictions with respect to the used distance. We compare both proposals in terms of the distance to the true generalised median and runtime.

关键词： Correspondence Mappings Hamming distance Generalised median linear assignment problem

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A robust point-matching algorithm for autoradiograph alignment

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Medical Image Analysis 1997年第4期1卷 379-398页

作者： Rangarajan, Anand Chui, Haili Mjolsness, Eric Pappu, Suguna Davachi, Lila Goldman-Rakic, Patricia Duncan, James Department of Diagnostic Radiology Yale University School of Medicine New Haven CT 06520-8042 333 Cedar Street United States Department of Electrical Engineering Yale University New Haven CT United States Department of Computer Science University of California San Diego San Diego CA United States Department of Diagnostic Radiology Yale University New Haven CT United States Section of Neurobiology Yale University New Haven CT United States

We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local, natural and artifactual differences between the autoradiograph slices. We have used the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated its robustness property using synthetically generated spatial mappings and provided an anecdotal visual comparison with the well-known iterated closest-point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.

关键词： 3-D reconstruction Alignment Correspondence Deterministic annealing linear assignment problem Outlier rejection Permutation matrix Point matching Primate autoradiographs Registration Robustness Similarity transform Softassign Spatial mapping

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Optimal user pairing using the shortest path algorithm 15

Optimal user pairing using the shortest path algorithm

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15th IEEE International Conference on Advanced Networks and Telecommunications Systems (IEEE ANTS)

作者： Katta, Kanchana Mishra, Ramesh Ch Deka, Kuntal IIIT Senapati Imphal Manipur India IIT Goa Ponda India

ISBN: (纸本)9781665448932

In Power Domain Non-Orthogonal Multiple Access, user pairing is based on a distance difference between users as well as power allocation. When the distance difference between the users becomes small, the interference caused by users can affect the system performance. Therefore, it is very important to implement an efficient pairing algorithm in NOMA systems to maintain a minimum distance and pair with the shortest path among the users available in the same cell. In this paper, we have proposed the shortest path algorithm for user pairing which separates the strong and weak users in order to promote massive connectivity. This algorithm performs better for the weak users when we compare it to the conventional Hungarian Algorithm in terms of user capacity. Further, results show that the proposed method achieves an average BER performance is similar to the Hungarian algorithm-based scheme but with much lower complexity.

关键词： User Pairing linear assignment problem Hungarian Algorithm Shortest Path Algorithm

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HUNIPU: Efficient Hungarian Algorithm on IPUs 40

HUNIPU: Efficient Hungarian Algorithm on IPUs

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40th International Conference on Data Engineering Workshop (ICDEW)

作者： Huang, Cheng Mathiasen, Alexander Dean, Josef Mottin, Davide Assent, Ira Aarhus Univ Aarhus Denmark Graphcore Bristol Avon England

ISBN: (纸本)9798350384048;9798350384031

The Hungarian algorithm is a fundamental approach for a number of matching problems that find corresponding elements in two sets. For instance, the optimal alignment of proteins. Due its computational complexity, this algorithm takes several hours for only a few thousand elements on a desktop computer. The most common solution to increase the efficiency is parallelization using GPUs. However, GPUs run the same operation in all the threads in a warp. This constraint is a limitation for the Hungarian algorithm that requires to find, in multiple steps, the best matching among a variable number of candidates. In this paper, we introduce HUNIPU, a parallel version of the Hungarian algorithm optimized for a novel architecture called Intelligence Processing Unit (IPU). IPUs is a promising avenue for the Hungarian algorithm since each core can independently perform a distinct operation. To benefit from the computational power of the IPU, we design HUNIPU to exploit the computational model of the IPU. We provide a smooth introduction to the IPU model and its challenges and show its flexibility and potential for numerous algorithmic advances. In our extensive experiments, the HUNIPU outperforms the best GPU algorithm on cuttingedge A100 GPU running 6 Chi faster on synthetic datasets and up to 32 Chi on real datasets for graph alignment.

关键词： IPU GPU Hungarian Algorithm linear assignment problem

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A contribution to quadratic assignment problems

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Optimization 1990年第6期21卷 933-943页

作者： Bookhold, Inks University “E. M. Arndt” Greifswakl Dept. of Mathematics United Kingdom

We discuss special eases of the quadratic assignment problem (QAP) being polynomially solvable. In particular we give an algebraic condition for the cost; Matrices of a QAP which guarantees that it is equivalent with a linear assignment problem. Based on these results we develop an approximation algorithm for QAPs with non-negative symmetric cost matrices.

关键词： approximation algorithm linear assignment problem polynomial solvable special case Quadratic assignment problem

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Automatic trajectory measurement of large numbers of crowded objects

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OPTICAL ENGINEERING 2013年第6期52卷 067003-067003页

作者： Li, Hui Liu, Ye Chen, Yan Qiu Fudan Univ Sch Comp Sci Shanghai 201203 Peoples R China

Complex motion patterns of natural systems, such as fish schools, bird flocks, and cell groups, have attracted great attention from scientists for years. Trajectory measurement of individuals is vital for quantitative and high-throughput study of their collective behaviors. However, such data are rare mainly due to the challenges of detection and tracking of large numbers of objects with similar visual features and frequent occlusions. We present an automatic and effective framework to measure trajectories of large numbers of crowded oval-shaped objects, such as fish and cells. We first use a novel dual ellipse locator to detect the coarse position of each individual and then propose a variance minimization active contour method to obtain the optimal segmentation results. For tracking, cost matrix of assignment between consecutive frames is trainable via a random forest classifier with many spatial, texture, and shape features. The optimal trajectories are found for the whole image sequence by solving two linear assignment problems. We evaluate the proposed method on many challenging data sets. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： trajectory measurement variance minimization active contour dual ellipse locator trainable assignment cost matrix linear assignment problem

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HyLAC: Hybrid linear assignment solver in CUDA

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2024年 187卷

作者： Kawtikwar, Samiran Nagi, Rakesh Univ Illinois Ind & Enterprise Syst Engn Champaign IL 61820 USA Singapore Univ Technol & Design Engn Syst & Design Singapore Singapore

The linear assignment problem (LAP) is a fundamental combinatorial optimization problem with a wide range of applications. Over the years, significant progress has been made in developing efficient algorithms to solve the LAP, particularly in the realm of high-performance computing, leading to remarkable reductions in computation time. In recent years, hardware improvements in General Purpose Graphics Processing Units (GPGPUs) have shown promise in meeting the ever-increasing compute bandwidth requirements. This has attracted researchers to develop GPU-accelerated algorithms to solve the LAP. Recent work in the GPU domain has uncovered parallelism available in the problem structure to achieve significant performance improvements. However, each solution presented so far targets either sparse or dense instances of the problem and has some scope for improvement. The Hungarian algorithm is one of the most famous approaches to solving the LAP in polynomial time. Hungarian algorithm has classical O(N-4) (Munkres') and tree based O(N-3) (Lawler's) implementations. It is well established that the Munkres' implementation is faster for sparse LAP instances while the Lawler's implementation is faster for dense instances. In this work, we blend the GPU implementations of Munkres' and Lawler's to develop a Hybrid GPU-accelerated solver for LAP that switches between the two implementations based on available sparsity. Also, we improve the existing GPU implementations to reduce memory contention, minimize CPU-GPU synchronizations, and coalesced memory access. The resulting solver (HyLAC) works faster than existing CPU/GPU LAP solvers for sparse as well as dense problem instances. HyLAC achieves a speedup of up to 6.14x over existing state-of-the-art GPU implementation when run on the same hardware. We also develop an implementation to solve a list of small LAPs (tiled LAP), which is particularly useful in the optimization domain. This tiled LAP solver performs 22.59x faster than t

关键词： Hungarian algorithm linear assignment problem GPU-accelerated graph algorithms High-performance computing Parallel algorithm

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Integrated multi-objective optimization and agent-based building occupancy modeling for space layout planning

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JOURNAL OF BUILDING ENGINEERING 2021年 34卷

作者： Dorrah, Dalia H. Marzouk, Mohamed Cairo Univ Struct Engn Dept Fac Engn Giza 12613 Egypt

With the construction industry shifting towards sustainable practices, it has become critical to incorporate sustainability measures in the design and operation of buildings. Space layout problem (SLP) in buildings is a classical problem of allocating activities to spaces with the intention of satisfying functional and proximity requirements. Research done on SLPs focused on the relative position of building activities with limited consideration of the building performance, or the comfort and well-being of its occupants. In light of this, this research proposes a two-component framework for solving SLPs in multi-purpose buildings based on their post-occupancy performance. The first component addresses the modeling and simulation of building activities. This follows a modular approach in activity design and operation, and integrates agent-based modeling (ABM) and discrete event simulation (DES) techniques with the queuing theory for modeling occupancy patterns and behaviors in each studied module. Three occupants-based metrics are set for assessing building performance which are energy consumption, flow patterns, and satisfaction. The second component focuses on finding the optimal solution for the layout problem based on the simulation and performance metrics. The multi-objective optimization problem is formulated as a linear assignment minimization problem. A case study of an administrative building is presented to illustrate the practical use of the framework. The results indicated that the framework provides a holistic yet detailed assessment of building performance. Moreover, it can be effectively incorporated as a decision support tool for solving large-scale layout problems to enhance the global and local operation of buildings with versatile activities.

关键词： Space layout problem Agent-based occupancy modeling Modularity approach Queuing theory linear assignment problem Multi-objective optimization

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A graph space optimal transport distance as a generalization of L^p distances: application to a seismic imaging inverse problem

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INVERSE problemS 2019年第8期35卷 085001-085001页

作者： Metivier, L. Brossier, R. Merigot, Q. Oudet, E. Univ Grenoble Alpes CNRS LJK Grenoble France Univ Grenoble Alpes Inst Sci Terre ISTerre Grenoble France Univ Paris Sud LMO Orsay France

Optimal transport distance is an appealing tool to measure the discrepancy between datasets in the frame of inverse problems, for its ability to perform global comparisons and its convexity with respect to shifted patterns in the compared quantities. However, solving inverse problems might require to compare signed quantities, while the optimal transport theory has been developed for the comparison of probability measures. In this study we propose to circumvent this difficulty by applying optimal transport to the comparison of the graph of the data rather than the data itself. We investigate this approach in the frame of seismic imaging, where each channel of the oscillatory data is interpreted as a discrete point cloud. We demonstrate that the corresponding misfit function can be computed through the solution of series of linear sum assignment problem (LSAP), while, based on the adjoint state technique, its gradient can be computed from the assignment solution of these LSAP. We illustrate how this approach yields a convex misfit function in the frame of seismic imaging using the full waveform. We show how an efficient strategy, based on a specific LSAP solver, the auction algorithm, can be designed. We illustrate the interest of the approach on a realistic 2D visco-acoustic seismic imaging problem. The proposed strategy relaxes the constraint on the accuracy of the initial model, outperforming the conventional least-squares approach and a previously proposed optimal transport based approach. The computational time increases by only a few percents compared with the least-squares approach, opening the way to applications to 3D field data in the near future.

关键词： seismic imaging optimal transport full waveform inversion linear assignment problem auction algorithm

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Deep Reinforcement Learning for Data Association in Cell Tracking

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2020年 8卷 298-298页

作者： Wang Junjie Su Xiaohong Zhao Lingling Zhang Jun Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Heilongjiang Prov Land Reclamat Headquarters Gen Dept Rehabil Harbin Peoples R China

Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose a deep reinforcement learning method to associate the detected targets between frames. According to the dynamic model of each target, the cost matrix is produced by conjointly considering various features of targets and then used as the input of a neural network. The proposed neural network is trained using reinforcement learning to predict a distribution over the association solution. Furthermore, we design a residual convolutional neural network that results in more efficient learning. We validate our method on two applications: the multiple target tracking simulation and the ISBI cell tracking. The results demonstrate that our approach based on reinforcement learning techniques could effectively track targets following different motion patterns and show competitive results.

关键词： cell tracking linear assignment problem deep learning deep reinforcement learning data association residual CNN

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