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检索条件"主题词=Algorithm development"

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Synthetic multispectral data sets for testing remote sensing algorithms and processing systems

Synthetic multispectral data sets for testing remote sensing...

引用

Conference on Advanced and Next-Generation Satellites II

作者： Fleig, AJ Masuoka, EJ Yang, K Univ. of Maryland and NASA Goddard Space Flight Ctr. (United States) NASA Goddard Space Flight Ctr. (United States) Science Applications International Corp. (United States)

ISBN: (纸本)0819423610

At the same time that we develop new sensors we also need to produce algorithms and processing systems to analyze the data in an operational mode shortly after launch. To develop and test the algorithms and processing systems we need test data. Yet new sensors are often designed to produce combinations of measurements that have never been made before. To resolve this dilemma for the Moderate Resolution Imaging Spectroradiometer (MODIS) to be launched in 1998 on the Earth Observing System (EOS) AM platform we are producing synthetic data to test the programming and operational aspects of the algorithms and processing systems. Both MODIS and the resulting synthetic data provide measurements at 36 wavelengths ranging from the visible (400 nanometers) well into the infrared (14 micrometers) day and night over the entire globe. The test data covers many of the physical conditions MODIS will observe with a full range of surface and atmospheric characteristics over land and sea with correct instrument and orbital characteristics. The data is sufficiently representational of the radiances MODIS would observe that the processing algorithms run to completion in a reasonable manner and use computing resources similar to those expected with real flight data. Although the simulation is not detailed enough to support theoretical investigations it has proven invaluable in implementing new concepts into operational code. So far we have provided hundreds of gigabytes of data covering many test cases. We describe requirements for synthetic data, tell how the data is produced, what characteristics it models, what limitations it has and what sorts of tests it supports. We show examples of the resulting data sets and describe our plans for future improvements. Sample synthetic MODIS data sets are available and we tell where and how to obtain them.

关键词： synthetic data test data remote sensing algorithm development

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LUCI, A PORTABLE SOFTWARE PACKAGE FOR DIFFERENT FIELDS OF IMAGE-PROCESSING RESEARCH

LUCI, A PORTABLE SOFTWARE PACKAGE FOR DIFFERENT FIELDS OF IM...

引用

CONF ON IMAGE PROCESSING AND INTERCHANGE : IMPLEMENTATION AND SYSTEMS

作者： BELLON, E WAMBACQ, P SCHOETERS, F LUCKERMANS, S MARCHAL, G SUETENS, P Katholieke Univ. Leuven (Belgium)

ISBN: (纸本)0819408131

A software package to support advanced research in the field of image processing and image analysis must have sufficiently powerful features, especially if it is to be used in a variety of application domains. In this contribution, we present the concepts of the LUCI image processing package, which has been developed in order to meet requirements that have been formulated as a result of previous experiences in our research group. These include support for multi-dimensional images and programming constructs to perform operations on those pixels that are contained in areas of arbitrary shape. In addition, we briefly present support tools for managing this software and its documentation.

关键词： Image processing 3D image processing Computer programming Software development Image compression Document management algorithm development Human-machine interfaces Image analysis Standards development

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Extraction of text lines and text blocks on document images based on statistical modeling

Extraction of text lines and text blocks on document images ...

引用

3rd Conference on Document Recognition

作者： Chen, S Haralick, RM Phillips, IT CAERE CORP LOS GATOSCA 95030

ISBN: (纸本)0819420344

In this paper, we developed statistical models to characterize the text line and text block structures on document images using the text word bounding boxes. We posed the extraction problem as finding the text lines and text blocks that maximize the Bayesian probability of the text lines and text blocks by observing the text word bounding boxes. We derived the so-called probabilistic linear displacement model (PLDM) to model the text line structures from text word bounding boxes. We also developed an augmented PLDM model to characterize the text block structures from text line bounding boxes. By systematically gathering statistics from a large population of document images, we are able to validate our models experimentally and determine the proper model parameters. We designed and implemented an iterative algorithm that utilized these probabilistic models to extract the text lines and text blocks. The quantitative performances of the algorithm in terms of the rates of miss, false, correct, splitting, merging and spurious detections of the text lines and text blocks were reported.

关键词： Detection and tracking algorithms Statistical analysis Astatine Binary data Statistical modeling algorithm development Palladium Performance modeling Image processing algorithms and systems Image segmentation

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APPLICATION OF NEURAL NETWORKS TO HYPERSPECTRAL IMAGE-ANALYSIS AND INTERPRETATION 3

APPLICATION OF NEURAL NETWORKS TO HYPERSPECTRAL IMAGE-ANALYS...

引用

3RD ANNUAL INTERNATIONAL CONF ON APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

作者： SHEN, SS HORBLIT, BD Lockheed Palo Alto Research Lab. (United States)

ISBN: (纸本)0819408743

The newest airborne and spaceborne imaging sensors, unlike their predecessors, generate data at tens to hundreds of wavelengths simultaneously. The advent of these imaging spectrometers greatly increases the detail of the information available to the analyst, but the concomitant disadvantages of vastly increased data volume and complexity have stimulated the search for more efficient and accurate hyperspectral data exploitation techniques. This paper describes two neural network models used in conjunction to analyze hyper-spectral image data. The first neural network is based on an unsupervised backward error propagation model. This network generates without supervision low dimensional discriminant feature vectors from the original high dimensional signals. These features are analyzed by the second neural network, which is a layered feed-forward neural network based on statistical principles, namely Parzen window density estimation and Bayes decision rule. This network estimates class-conditional probability density functions using training samples and classifies data points so that the expected risk is minimized. Results of applying these two neural networks to 210-band AVIRIS data are presented.

关键词： Neural networks Image analysis Data modeling Statistical analysis Artificial neural networks Spectral resolution Absorption algorithm development Hyperspectral imaging Spectrometers

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MODIFIED BACKPROPAGATION NEURAL NETWORK WITH APPLICATIONS TO IMAGE COMPRESSION 3

MODIFIED BACKPROPAGATION NEURAL NETWORK WITH APPLICATIONS TO...

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3RD ANNUAL INTERNATIONAL CONF ON APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

作者： KENUE, SK General Motors Research Labs. (United States)

ISBN: (纸本)0819408743

The back-propagation neural network algorithm is an iterative technique for learning the relationship between an input and output. This algorithm has been successfully used in many real-world applications; however, it suffers from slow convergence problems and can get struck in local minima of the weight-error surface. A generalization of previously proposed activation functions has been developed using a free parameter for improved convergence. A modified algorithm based on these function is suggested by bounding the input data to the derivative evaluation in the backwards pass. The modified algorithm has demonstrated superior performance on the standard parity and encoder problems. Finally, a histogram normalization technique is presented for image data compression for improved convergence.

关键词： Image compression Computer programming Evolutionary algorithms Signal to noise ratio Neural networks Artificial neural networks algorithm development Matrices Data compression Neurons

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Characterizing point spread signals for subsurface synthetic imaging

Characterizing point spread signals for subsurface synthetic...

引用

2nd International Conference on Subsurface Sensing Technologies and Applications

作者： Rodriguez, D Univ Puerto Rico Dept Elect & Comp Engn Computat Signal Proc Grp Mayaguez PR 00681 USA

ISBN: (纸本)0819437743

This work concentrates on the analysis of radar signature signals for the characterization and efficient computation of finite discrete two-dimensional point spread functions. A general simple model for ground penetrating raw data image formation is assumed in order to concentrate on the efficient computation of point spread functions. The point spread functions are used as impulse response functions for the simulation of high resolution image of two dimensional synthetic aperture imaging kernels. A methodology has been developed to serve as a tool aid in the analysis, design, and efficient implementation of one-dimensional and two-dimensional fast Fourier transform (FFT) algorithms prevalent in SAR image formation operations with the idea in mind of reducing the computational effort and improving the hardware implementation process. Kronecker products algebra, a branch of finite dimensional multilinear algebra, has been demonstrated to be a useful tool aid in the devel-opment of fast algorithms for unitary transformations and in the identification of similarities and differences among FFT computational frameworks.

关键词： Image acquisition Synthetic aperture radar algorithm development Image processing Point spread functions Fourier transforms Data modeling Signal processing Radar Image resolution

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Elimination of direct derivative along source trajectory for accurate image reconstruction in helical cone-beam CT

Elimination of direct derivative along source trajectory for...

引用

Medical Imaging 2004 Conference

作者： Zou, Y Pan, XC Univ Chicago Dept Radiol Chicago IL 60637 USA

ISBN: (纸本)0819452831

An important breakthrough in helical cone-beam reconstruction is the development of Katsevich's algorithms. which appear to have numerous advantages over the algorithms developed previously. The original algorithm proposed by Katsevich has a simple form and requires only once the computation of the data filtering. It, however, invokes a derivative of the data function with respect to the rotation angle along the helical trajectory and thus makes the algorithm numerically susceptible to the sample aliasing along the helical trajectory. A modified version of the algorithm later developed by Katsevich that avoids the explicit computation of the derivative of the data function along the helical trajectory. However, this modified algorithm contains more terms than does the original algorithm and involves two different filtering of the data function. Therefore, this modified algorithm is computationally more complex and demanding than does the original Katsevich''s algorithm. In this work, based upon the original Katsevich's algorithm, we present a new algorithm that not only avoids explicit computation of the derivative of the data function along the helical trajectory but also requires only one filtering of the data function. Therefore, in general, our algorithm is quantitatively more accurate than the original Katsevich's algorithm and is computationally more efficient than the modified Katsevich's algorithm.

关键词： Reconstruction algorithms algorithm development Sensors Detection and tracking algorithms Image restoration Computed tomography algorithms 3D image processing Computer simulations Radiology

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Signal to clutter measurement and ATR performance

Signal to clutter measurement and ATR performance

引用

Conference on Automatic Target Recognition VIII

作者： Sims, SRF USA Aviat & Missile Command Missile Res Dev & Eng Ctr IPMGRDAMSAM Redstone Arsenal AL 35898 USA

ISBN: (纸本)0819428205

Many different ATR algorithms have had their performance quantified for more than twenty years. These algorithms have been tested on data sets with significantly varying difficulty, however, the quantification of the data set difficulty has previously only been coarsely partitioned based on target operational state and the meteorological environment. Also, this has typically been done without mention of the correlation between the training and test set. In this paper we show the quantification of the signal to clutter measure (SCM) versus ATR performance, specifically the typical probabilities of detection (P_d), recognition (P_r) and false alarm rates directly. This SCM provides a basis for knowing what these performance statistics actually mean, since a 'good' or 'bad' set of performance numbers taken without quantified knowledge of the difficulty level of the data generally does not reflect the limitations or capabilities of the ATR algorithm(s) or provide an especially relevant basis for comparison.

关键词： Automatic target recognition Particle filters Detection and tracking algorithms Target detection Target recognition Image filtering Signal detection algorithm development Meteorology Missiles

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OPTICAL WAVELET PROCESSOR FOR WAVELETS DEFINED IN THE TIME-DOMAIN

OPTICAL WAVELET PROCESSOR FOR WAVELETS DEFINED IN THE TIME-D...

引用

Conference on Photonics for Processors, Neural Networks, and Memories

作者： ZHANG, Y KANTERAKIS, EG KATZ, A WANG, JM INTERDIGITAL TELECOM DIV INTERDIGITAL COMMUN CORPGREAT NECKNY 11021

ISBN: (纸本)0819412759

In this paper, we present an optical implementation for 1D wavelet processing which is able to represent wavelets defined in the time domain and deliver complex coefficients. The system is the combination of our previously reported Smartt interferometry based wavelet processor and a modified version of VanderLugt filter. Haar wavelet, which requires complex representation in the frequency domain, was used to demonstrate the system operation.

关键词： Wavelets Wavelet transforms Fourier transforms Interferometry Sensors algorithm development Filtering (signal processing) Modulators Optical filters Transmittance

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Robust adaptive signal-level fusion for enhanced target detection

Robust adaptive signal-level fusion for enhanced target dete...

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Signal Processing, Sensor Fusion, and Target Recognition V Conference

作者： Kadar, I NORTHROP GRUMMAN CORP ADV TECHNOL & DEV CTRRES & TECHNOL DEVBETHPAGENY 11714

ISBN: (纸本)0819421367

In a series of prior papers Kadar et al have designed, developed and experimentally evaluated a family of centralized detection algorithms for multi-frequency radars operating in various degrees of clutter. Previously, two centralized fusion algorithms, called non-coherent integration (NCI) and T-squared (TSQ), followed by adaptive constant false alarm (ACFAR) post-processing were evaluated. In this paper, an optimum adaptive CFAR version of the T- squared algorithm, called ATSQ, is developed. The performance of ATSQ is compared with the NCI and TSQ algorithms using measured data in clutter. The fusion performance comparisons are presented in terms of receiver operating characteristics (ROC). It is shown that the new ATSQ algorithm is robust to changes of the background clutter. Its ROC performance is near the optimum achievable, and is significantly better than either NCI or TSQ.

关键词： algorithm development Signal detection Target detection Detection and tracking algorithms Radar Receivers

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