image registration is a classical problem that addresses the problem of finding a geometric transformation that best aligns two images. Since the amount of multisensor remote sensing imagery are growing tremendously, ...
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ISBN:
(纸本)9783540680673
image registration is a classical problem that addresses the problem of finding a geometric transformation that best aligns two images. Since the amount of multisensor remote sensing imagery are growing tremendously, the search for matching transformation with mutual information is very time-consuming and tedious, and fast and automatic registration of images from different sensors has become critical in the remote sensing framework. So the implementation of automatic mutual information based image registration methods on high performance machines needs to be investigated. First, this paper presents a parallel implementation of a mutual information based image registration algorithm. It takes advantage of cluster machines by partitioning of data depending on the algorithm's peculiarity. Then, the evaluation of the parallel registration method has been presented in theory and in experiments and shows that the parallel algorithm has good parallel performance and scalability.
We present a new algorithm for feature extraction in hyperspectral images based on source separation and parallel computing. In source separation, given a linear mixture of sources, the goal is to recover the componen...
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ISBN:
(纸本)0819464821
We present a new algorithm for feature extraction in hyperspectral images based on source separation and parallel computing. In source separation, given a linear mixture of sources, the goal is to recover the components by producing an unmixing matrix. In hyperspectral imagery, the mixing transform and the separated components can be associated with endmembers and their abundances. Source separation based methods have been employed for target detection and classification of hyperspectral images. However, these methods usually involve restrictive conditions on the nature of the results such as orthogonality (in Principal Component Analysis - PCA and Orthogonal Subspace Projection - OSP) of the endmembers or statistical independence (in Independent Component Analysis - ICA) of the abundances nor do they fully satisfy all the conditions included in the Linear Mixing Model. Compared to this, our approach is based on the Nonnegative Matrix Factorization (NMF), a less constraining uninixing method. NMF has the advantage of producing positively defined data, and, with several modifications that we introduce also ensures addition to one. The endmember vectors and the abundances are obtained through a gradient based optimization approach. The algorithm is further modified to run in a parallel environment. The parallel NMF (P-NMF) significantly reduces the time complexity and is shown to also easily port to a distributed environment. Experiments with in-house and Hydice data suggest that NMF outperforms ICA, PCA and OSP for unsupervised endmember extraction. Coupled with its parallel implementation, the new method provides an efficient way for unsupervised unmixing further supporting our efforts in the development of a real time hyperspectral sensing environment with applications to industry and life sciences.
The proceedings contain 52 papers. The topics discussed include: optimized integral imaging display by global pixel mapping;LCD-based stereoscopic imaging system;real-time automated 3D visualization and recognition of...
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ISBN:
(纸本)0819463906
The proceedings contain 52 papers. The topics discussed include: optimized integral imaging display by global pixel mapping;LCD-based stereoscopic imaging system;real-time automated 3D visualization and recognition of biological microorganisms;integral image compression methods;stereoscopic conversion of two-dimensional movie encoded in MPEG-2;an algorithm for synthesizing elemental images using the line of sight in three-dimensional integral imaging;3D sensing and visualization of occluded objects;an optical parallelprocessing for multiplier modulo using an optical interferometer;spatial modulators exploiting a multi-photon light scattering in crystals;measurement of compression defects in phase-shifting digital holographic data;digital inline holography of biological specimens;experimental synthesis of general complex fields an amplitude modulator;optical processor for solving the traveling salesman problem;and passive 3D imaging using polarimetric diversity.
Metaheuristics are very useful methods because they can find (approximate) solutions of a great variety of problems. One of them, which interests us, is graph partitioning. We present a new metaheuristic based on nucl...
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ISBN:
(纸本)9781424400546
Metaheuristics are very useful methods because they can find (approximate) solutions of a great variety of problems. One of them, which interests us, is graph partitioning. We present a new metaheuristic based on nuclear fusion and fission of atoms. This metaheuristic, called fusion fission, is compared to other classical algorithms. First, we present spectral and multilevel algorithms which are used to solve partitioning problems. Secondly, we present two metaheuristics applied to partitioning problems: simulated annealing and ant colony algorithms. We show that fusion fission gives good results, compared to the other algorithms. We demonstrate on a problem of air traffic control that metaheuristics methods can give better results than specific methods
The following topics are dealt with: parallel computing; distributed computing; interconnection networks; communication and telecommunication; imageprocessing and computer graphics; access control and authorization; ...
The following topics are dealt with: parallel computing; distributed computing; interconnection networks; communication and telecommunication; imageprocessing and computer graphics; access control and authorization; wireless networks and mobile computing; database applications and data mining; parallelizing compilers; parallel/distributed architectures; intelligent computing and neural networks; Internet computing; network routing and communication algorithms; Web technologies; ubiquitous computing systems; task mapping and job scheduling; security protocols; computer networks; formal methods and programming languages; intrusion detection and survivability; grid computing systems; applied cryptography; P2P and ad hoc networks; and reliability and fault-tolerance
This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. Local rules for particle movement in GPM, parallel algorithm and its implementation structure...
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This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. Local rules for particle movement in GPM, parallel algorithm and its implementation structure to generate the desired predictive coding are discussed. The proposed GPM approach has advantages in terms of encoding speed, parallelism, scalability, simplicity, and easy hardware implementation over other sequential lossless compression methods
Maximum likelihood (ML) estimation is used during tomosynthesis mammography reconstruction. A single reconstruction involves the processing of high-resolution projection images, which is both compute-intensive and tim...
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Maximum likelihood (ML) estimation is used during tomosynthesis mammography reconstruction. A single reconstruction involves the processing of high-resolution projection images, which is both compute-intensive and time-consuming. This workload is presently a bottleneck in the accurate diagnosis of breast cancer during screening. This paper presents our parallelization work on an ML algorithm using three different partitioning models: no inter-communication, overlap with inter-communication and non-overlap model. These models are evaluated to obtain the best reconstruction performance given a range of computing environments with different computational power and network speed. Our test results show that the non-overlap method outperforms the other two methods on all five computing platforms evaluated. This parallelization of ML has enabled tomosynthesis to become a viable technology in the breast screening clinic, reducing reconstruction time from 3 hours on a Pentiumivworkstation to 6 minutes on a 32-node Pentiumiv cluster
Many applications commonly found in digital signal processing and imageprocessing applications can be represented by data-flow graphs (DFGs). In our previous work, we proposed a new technique, extended retiming, whic...
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Many applications commonly found in digital signal processing and imageprocessing applications can be represented by data-flow graphs (DFGs). In our previous work, we proposed a new technique, extended retiming, which can be combined with minimal unfolding to transform a DFG into one which is rate-optimal. The result, however, is a DFG with split nodes, a concise representation for pipelined schedules. This model and the extraction of the pipelined schedule it represents have heretofore not been explored. In this paper, we develop new results regarding the construction of such graphs. We develop scheduling algorithms for such graphs, and then discuss a way to reduce the hardware requirements of such schedules. In the process, we state and prove a tight upper bound on the minimum number of processors required to execute the static schedule produced by our algorithms. We also construct an unfolding algorithm for split-node graphs and combine it with our scheduling methods to achieve rate-optimality in all cases. Finally, we demonstrate our methods on a specific example.
Spiral Architecture is a relatively new and powerful approach to general-purpose machine vision system. n this novel architecture, Spiral Addition and Multiplication achieve imageprocessing. As we all nown, fractal i...
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ISBN:
(纸本)1932415610
Spiral Architecture is a relatively new and powerful approach to general-purpose machine vision system. n this novel architecture, Spiral Addition and Multiplication achieve imageprocessing. As we all nown, fractal image compression methods have maximal image compression ratio, at the cost Of slow coding speed. This paper presents an algorithm to achieve high image compression ratio without slow coding speed on Spiral Architecture, which also improves the Spiral Architecture s usage in imageprocessing.
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