Anatomical landmarks are useful as the primitive anatomical knowledge for medical image understanding. In this study, we construct a unified framework for automated detection of anatomical landmarks distributed within...
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ISBN:
(纸本)9780819485045
Anatomical landmarks are useful as the primitive anatomical knowledge for medical image understanding. In this study, we construct a unified framework for automated detection of anatomical landmarks distributed within the human body. Our framework includes the following three elements;(1) initial candidate detection based on a local appearance matching technique based on appearance models built by PCA and the generative learning, (2) false positive elimination using classifier ensembles trained by MadaBoost, and (3) final landmark set determination based on a combination optimization method by Gibbs sampling with a priori knowledge of inter-landmark distances. In evaluation of our methods with 50 data sets of body trunk CT, the average sensitivity in detecting candidates of 165 landmarks was 0.948 +/- 0.084 while 55 landmarks were detected with 100 % sensitivity. Initially, the amount of false positives per landmark was 462.2 +/- 865.1 per case on average, then they were reduced to 152.8 +/- 363.9 per case by the MadaBoost classifier ensembles without miss-elimination of the true landmarks. Finally 89.1 % of landmarks were correctly selected by the final combination optimization. These results showed that our framework is promising for an initial step for the subsequent anatomical structure recognition.
We propose an algorithm to compute a greatest common divisor (GCD) of univariate polynomials with large integer coefficients on Graphics processing Units (GPUs). At the highest level, our algorithm relies on modular t...
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Gradient domain processing is a computationally expensive imageprocessing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this c...
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Gradient domain processing is a computationally expensive imageprocessing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this challenge, parallel algorithms are being developed to make this class of techniques applicable to the largest images available with running times that are more acceptable to the users. To this end we target the most ubiquitous form of computing power available today, which is small or medium scale clusters of commodity hardware. Such clusters are continuously increasing in scale, not only in the number of nodes, but also in the amount of parallelism available within each node in the form of multicore CPUs and GPUs. In this paper we present a hybrid parallel implementation of gradient domain processing for seamless stitching of gigapixel panoramas that utilizes MPI, threading and a CUDA based GPU component. We demonstrate the performance and scalability of our implementation by presenting results from two GPU clusters processing two large data sets.
Implementation specific computation modules hold the key to the success of fast DSP and Embedded systems. Exponential encoders, dedicated multipliers, barrel shifters and accumulators are common units available on DSP...
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ISBN:
(纸本)9789898425485
Implementation specific computation modules hold the key to the success of fast DSP and Embedded systems. Exponential encoders, dedicated multipliers, barrel shifters and accumulators are common units available on DSPs. The family of constant divider circuits of the form 2p±1, which are useful for imageprocessing, statistical processing like histograms etc., is the specific focus of this paper. This family is largely dominated by the Residue Number System (RNS), Petry and Srinivasan algorithms and the Shuo-Yen Robert-Li algorithm. While these algorithms offer various trade-offs in terms of accuracy, memory footprint, power consumption and timing behavior, none of these methods are suited for processing serialized inputs, dividend inputs with apriori unknown bit length and the circuits have to be replaced with change in input bit length. The circuit size also grows enormously for large input lengths along with a reduction in accuracy. These methods are suited only for integer division and are unsuited for extension to floating/fixed point division. In this paper a novel constant divider algorithm is offered, which overcomes the above mentioned limitations while handling arbitrary length, serial/ parallel data and producing fullprecision, full-accuracy, floating point capable results with constant circuit requirements and comparable timing to state of the art methods.
Gaussian Mixture Models (GMMs) are widely used among scientists e.g. in statistic toolkits and data mining procedures. In order to estimate parameters of a GMM the Maximum Likelihood (ML) training is often utilized, m...
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The proceedings contain 525 papers. The topics discussed include: convergence of the stochastic age-structured population system with diffusion;parallel computer processing systems are better than serial computer proc...
ISBN:
(纸本)9783642233562
The proceedings contain 525 papers. The topics discussed include: convergence of the stochastic age-structured population system with diffusion;parallel computer processing systems are better than serial computer processing systems;the traits of dual multiple ternary fuzzy frames of translates with ternary scaling functions;the implementation of NPKI system based LDAP;research on backstepping integral control of two order nonlinear systems with input nonlinearity;research on high order sliding mode control of three order systems with input nonlinearity;research on teaching methods of motor drive technology based on information technology;on relativity of probability and information;pattern synthesis of antennas based on particle swarm optimization algorithm;critical condition for parametric resonance of axially mass-loaded string;research on medieval english literature teaching based on data analysis;and research of the distributed heterogeneous database conversion mechanism.
Large-scale data processing needs of enterprises today are primarily met with distributed and parallel computing in data centers. MapReduce has emerged as an important programming model for these environments. Since t...
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ISBN:
(纸本)9781424499212
Large-scale data processing needs of enterprises today are primarily met with distributed and parallel computing in data centers. MapReduce has emerged as an important programming model for these environments. Since today's data centers run many MapReduce jobs in parallel, it is important to find a good scheduling algorithm that can optimize the completion times of these jobs. While several recent papers focused on optimizing the scheduler, there exists very little theoretical understanding of the scheduling problem in the context of MapReduce. In this paper, we seek to address this problem by first presenting a simplified abstraction of the MapReduce scheduling problem, and then formulate the scheduling problem as an optimization problem. We devise various online and offline algorithms to arrive at a good ordering of jobs to minimize the overall job completion times. Since optimal solutions are hard to compute (NP-hard), we propose approximation algorithms that work within a factor of 3 of the optimal. Using simulations, we also compare our online algorithm with standard scheduling strategies such as FIFO, Shortest Job First and show that our algorithm consistently outperforms these across different job distributions.
With the remarkable increase in the number of DNA and proteins sequences, it is very important to study the performance of multiple pattern matching algorithms when querying sequence patterns in biological sequence da...
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ISBN:
(纸本)9789898425362
With the remarkable increase in the number of DNA and proteins sequences, it is very important to study the performance of multiple pattern matching algorithms when querying sequence patterns in biological sequence databases. In this paper, we present a performance study of the running time of well known multiple pattern matching algorithms on widely used biological sequence databases containing the building blocks of nucleotides (in the case of nucleic acid sequence databases) and amino acids (in the case of protein sequence databases).
The proceedings contain 525 papers. The topics discussed include: convergence of the stochastic age-structured population system with diffusion;parallel computer processing systems are better than serial computer proc...
ISBN:
(纸本)9783642233234
The proceedings contain 525 papers. The topics discussed include: convergence of the stochastic age-structured population system with diffusion;parallel computer processing systems are better than serial computer processing systems;the traits of dual multiple ternary fuzzy frames of translates with ternary scaling functions;the implementation of NPKI system based LDAP;research on backstepping integral control of two order nonlinear systems with input nonlinearity;research on high order sliding mode control of three order systems with input nonlinearity;research on teaching methods of motor drive technology based on information technology;on relativity of probability and information;pattern synthesis of antennas based on particle swarm optimization algorithm;critical condition for parametric resonance of axially mass-loaded string;research on medieval english literature teaching based on data analysis;and research of the distributed heterogeneous database conversion mechanism.
The proceedings contain 525 papers. The topics discussed include: convergence of the stochastic age-structured population system with diffusion;parallel computer processing systems are better than serial computer proc...
ISBN:
(纸本)9783642233449
The proceedings contain 525 papers. The topics discussed include: convergence of the stochastic age-structured population system with diffusion;parallel computer processing systems are better than serial computer processing systems;the traits of dual multiple ternary fuzzy frames of translates with ternary scaling functions;the implementation of NPKI system based LDAP;research on backstepping integral control of two order nonlinear systems with input nonlinearity;research on high order sliding mode control of three order systems with input nonlinearity;research on teaching methods of motor drive technology based on information technology;on relativity of probability and information;pattern synthesis of antennas based on particle swarm optimization algorithm;critical condition for parametric resonance of axially mass-loaded string;research on medieval english literature teaching based on data analysis;and research of the distributed heterogeneous database conversion mechanism.
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