Theoretical and experimental results of investigations which are leading to the digital Tv-based systems for objects in motion against a non-steady-state background detectors design are presented. Specifically, the pe...
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ISBN:
(纸本)0819423106
Theoretical and experimental results of investigations which are leading to the digital Tv-based systems for objects in motion against a non-steady-state background detectors design are presented. Specifically, the peculiarities of proposed adaptive threshold-based method of detection was explored for wide range of meteorological and observing conditions. The effect of multiplication objects marks for the spatially distributed moving objects was analyzed and several approaches to consolidation of them are proposed. They are based on threshold value back-feed varying and nonlinear cluster analysis of structures and images in question. The suboptimal algorithm of threshold estimation is developed and discussed. The detected object's motion characteristics influence on the characteristics of image elements is finally experimentally explored Relying on such analysis we propose the combined approach to the motion detection problem. It includes both spatial and temporal structural and statistical characteristics of image element's estimation and simultaneous threshold development of frame's tandem difference and cluster analysis of a resulting 2-dimensional and 3-dimensional bit-matrix range. Some results of image characteristics' influence on detection efficiency are cited and tabulated. The analysis made it apparent that proposed method is highly efficient for the situation of rapidly varying background, when the traditional methods and algorithms have not met with success. The contrast-based methods in this conditions are used in some modern motion detection systems, but seems to be failed as well for meteorological disturbances such as rain, snowfall, etc., presence. The above methods and algorithms make it possible to design one-channel motion detector for detecting spatially distributed objects against non-steady-state background based on Intel DX-4/120 or Pentium processor. Specially developed framegrabber is used for digitizing of original B&W Tv signal.
This paper presents a method for unsupervised segmentation of polarimetric SAR data into classes of homogeneous microwave backscatter characteristics. Clustering of polarimetric backscatter are obtained either by the ...
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ISBN:
(纸本)0819423629
This paper presents a method for unsupervised segmentation of polarimetric SAR data into classes of homogeneous microwave backscatter characteristics. Clustering of polarimetric backscatter are obtained either by the CMF-NSO ( unsupervised fuzzy partition - optimal number of classes) or by SEM (Stochastic Estimation Maximization) algorithm. These algorithms carry out the classification without a priori assumptions on the number of classes in the data set. Assessment of cluster validity is based on performance measures using hypervolume v or CS function criteria. The later measures the overall average compactness and separation of a fuzzy-partition. The CMF-NSO algorithm performs well in situations of large variability of cluster shapes and densities. Given the clusters of polarimetric backscatter, the entire image is segmented using a MAP (Maximun A Posteriori) estimation. Implementation of the MAP technique is accomplished by an ICM (Iterative Conditional Modes) algorithm. Results, using fully polarimetric SAR forest data, obtained by the CMF-NSO following by the ICM algorithm with a K-distribution model are quite satisfactory.
The paper starts with a review of integration techniques for calculating height maps from dense gradient fields. There exist a few proposals of local integration methods, and two proposals for global optimization. Sev...
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This paper addresses the problem of the identification of nonlinear dynamic systems using modularly structured neural network with the new learning algorithm for the learning of both gating and expert networks weights...
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ISBN:
(纸本)0818674563
This paper addresses the problem of the identification of nonlinear dynamic systems using modularly structured neural network with the new learning algorithm for the learning of both gating and expert networks weights. Here we start with the standard learning procedure for such networks in the sense that the problem of learning is formulated and treated as a mixture estimation problem in which the log-likelihood function should be maximised. But, as opposite to the established methods for modular networks we combine the gradient type of learning for gating weights with a least-squares algorithm for the learning of expert networks weights, and the experts are simple one-layer nets with a single linear output unit, The very result of such an approach is a simple structured modular network with improved learning and it seems with good capabilities for the identification of general nonlinear dynamic systems. The identifications of a discrete-time nonlinear system corrupted with noise and of a real world system are presented. Later process represents the identification of the positioning of a car engine throttle valve from real data set (3000 samples of measured noisy data).
From the Publisher: February 29-March 3, 1996, San Diego, California Evolutionary programming, originally conceived by Lawrence J. Fogel in 1960, is a stochastic and optimization method similar to genetic algorithms, ...
ISBN:
(纸本)9780262061902
From the Publisher: February 29-March 3, 1996, San Diego, California Evolutionary programming, originally conceived by Lawrence J. Fogel in 1960, is a stochastic and optimization method similar to genetic algorithms, but instead emphasizes the behavioral linkage between parents and their offspring, rather than emulating specific genetic operators as observed in nature. Evolutionary Programming v will serve as a reference and forum for researchers investigating applications and theory of evolutionary programming and other related areas in evolutionary and natural computation. Chapters describe original, unpublished research in evolutionary programming, evolution strategies, genetic algorithms and genetic programming, artificial life, cultural algorithms, and other dynamic models that rely on evolutionary principles. Topics include the use of evolutionary simulations in optimization, neural network training and design, automatic control, imageprocessing and other applications, as well as mathematical theory or empirical analysis providing insight into the behavior of such algorithms. Of particular interest are applications of simulated evolution to problems in biology and economics. A Bradfor Book. Complex Adaptive systems series
We have developed a new ultrasound scan conversion algorithm that can be executed very efficiently on modern microprocessors. Our algorithm is designed to handle the address calculations and input and output (I/O) dat...
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We have developed a new ultrasound scan conversion algorithm that can be executed very efficiently on modern microprocessors. Our algorithm is designed to handle the address calculations and input and output (I/O) data loading concurrently with the interpolation. The processing unit's computing power can be dedicated to performing pixel interpolations while the other operations are handled by an independent direct memory access (DMA) controller. By making intelligent use of the I/O transfer capabilities of the DMA controller, the algorithm avoids spending the processing unit's valuable computing cycles in address calculations and nonactive pixel blanking. Furthermore, the new approach speeds up the computation by utilizing the ability of superscalar and very long instruction word (vLIW) processors to perform multiple operations in parallel. Our scan conversion algorithm was implemented on a multimedia and imaging system based on the Texas Instruments TMS320C80 Multimedia video Processor (MvP). Computing cycles are spent only on predeterminable nonzero output pixels. For example, an execution time of 11.4 ma was achieved when there are 101,829 nonzero output pixels. This algorithm demonstrates a substantial improvement over previous scan conversion algorithms, and its optimized implementation enables modern commercially available programmable processors to support scan conversion at video rates. (C) 1996 Academic Press.
For imageprocessing using sliding window mode, it is shown that the algorithms realizing the parallel-recursive calculation of the convolution with the approximation of the impulse response FIR-filter by polynomial b...
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For imageprocessing using sliding window mode, it is shown that the algorithms realizing the parallel-recursive calculation of the convolution with the approximation of the impulse response FIR-filter by polynomial bases are the best suited.
In precise measurement of objects, geometric characteristics widely use the phase-shifting interferometric systems. Simple data processingalgorithms are usually realized, but it is difficult to optimize such systems ...
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ISBN:
(纸本)0819422118
In precise measurement of objects, geometric characteristics widely use the phase-shifting interferometric systems. Simple data processingalgorithms are usually realized, but it is difficult to optimize such systems for accuracy increasing on general totality of measured data because of nonlinear data transformation. Besides that an important problem is the noise-immune phase unwrapping on intervals more than 2π rad. The proposed interferometric system is free from these disadvantages. In the system new method and algorithm of phase estimation are realized, which are based on Markov theory of optimal nonlinear filtering. The main advantages of proposed system are the following: data processing in real time scale, solving the phase unwrapping problem and minimization of phase errors in conditions of influence of phase fluctuations and noise correlated with the signal. Phase restoration error in typical measurement conditions dies not exceed 0.15 rad. on criterion peak- valley, while rms-error does not exceed 0.05 rad. The system provides the possibility to solve the synthesis and optimization problems of wide class of multidimensional, unstationary and nonlinear systems.
We propose vLSI architectures for implementing tree-structured image coding algorithms. A simple data partitioning and mapping technique is used for each processor to have a balanced work load and to work independentl...
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We propose vLSI architectures for implementing tree-structured image coding algorithms. A simple data partitioning and mapping technique is used for each processor to have a balanced work load and to work independently of each other. This technique leads to a simple memory access and processor architecture. The proposed parallel architecture has a high throughput rate and is area efficient. It can also be used to realize low-power designs.
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