Wide area airborne surveillance (WAAS) systems are a new class of remote sensing imagers which have many military and civilian applications. These systems are characterized by long loiter times (extended imaging time ...
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ISBN:
(纸本)9780819484086
Wide area airborne surveillance (WAAS) systems are a new class of remote sensing imagers which have many military and civilian applications. These systems are characterized by long loiter times (extended imaging time over fixed target areas) and large footprint target areas. These characteristics complicate moving object detection and tracking due to the large image size and high number of moving objects. This research evaluates existing object detection and tracking algorithms with WAAS data and provides enhancements to the processing chain which decrease processing time and maintain or increase tracking accuracy. Decreases in processing time are needed to perform real-time or near real-time tracking either on the WAAS sensor platform or in ground station processing centers. Increased tracking accuracy benefits real-time users and forensic (off-line) users.
Smoothing filter is the method of choice for image preprocessing and pattern recognition. We present a new concurrent method for smoothing 2D object in binary case. Proposed method provides a parallel computation whil...
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ISBN:
(纸本)9780819484086
Smoothing filter is the method of choice for image preprocessing and pattern recognition. We present a new concurrent method for smoothing 2D object in binary case. Proposed method provides a parallel computation while preserving the topology by using homotopic transformations. We introduce an adapted parallelization strategy called split, distribute and merge (SDM) strategy which allows efficient parallelization of a large class of topological operators including, mainly, smoothing, skeletonization, and watershed algorithms. To achieve a good speedup, we cared about task scheduling. Distributed work during smoothing process is done by a variable number of threads. Tests on 2D binary image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2x Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 5.2 thus a cadency of 32 images per second is achieved.
In many applications where communication delays are present, measurements with earlier time stamps can arrive out-of-sequence, i.e., after state estimates have been obtained for the current time instant. To incorporat...
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ISBN:
(纸本)9780819486240
In many applications where communication delays are present, measurements with earlier time stamps can arrive out-of-sequence, i.e., after state estimates have been obtained for the current time instant. To incorporate such an Out-Of-Sequence Measurement (OOSM), many algorithms have been proposed in the literature to obtain or approximate the optimal estimate that would have been obtained if the OOSM had arrived in-sequence. When OOSM occurs repeatedly, approximate estimations as a result of incorporating one OOSM have to serve as the basis for incorporating yet another OOSM. The question of whether the "approximation of approximation" is well behaved, i.e., whether approximation errors accumulate in a recursive setting, has not been adequately addressed in the literature. This paper draws attention to the stability question of recursive OOSM processing filters, formulates the problem in a specific setting, and presents some simulation results that suggest that such filters are indeed well-behaved. Our hope is that more research will be conducted in the future to rigorously establish stability properties of these filters.
A notion of sparse dual frames for a given non-exact frame is introduced. The sparse dual frame is motivated in a study of compressed sensing problems where signal are sparse with respect to a redundant and coherent d...
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ISBN:
(纸本)9780819487483
A notion of sparse dual frames for a given non-exact frame is introduced. The sparse dual frame is motivated in a study of compressed sensing problems where signal are sparse with respect to a redundant and coherent dictionary (frames). A sparse-dual-based l(1)-analysis is thereby proposed. We show that sparse dual frames are locally stable. An error bound ensuring the correct signal recovery is obtained. More importantly, solutions to very hard problems in compressed sensing with redundant dictionaries that are otherwise completely unsuccessful by known algorithms of l(1)-synthesis and the l(1)-analysis are seen as satisfactory, by the new sparse-dual-based approach and an alternating iterative algorithm that we propose. Examples are provided.
Hyperspectral pixels are acquired in hundreds of narrow and continuous spectral bands, and the hyperspectral data cubes typically contain hundreds of megabytes. Analysis and processing of the high-dimensional hyperspe...
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ISBN:
(纸本)9780819486226
Hyperspectral pixels are acquired in hundreds of narrow and continuous spectral bands, and the hyperspectral data cubes typically contain hundreds of megabytes. Analysis and processing of the high-dimensional hyperspectral data are computationally expensive and memory inefficient. However, there is a large amount of redundancy between neighboring spectral bands and the hyperspectral pixels lie in a much lower dimensional subspace. Therefore, numerous techniques can be applied to reduce the dimensionality while maintaining the structure of the data. This would lead to a significant reduction of the complexity of the imaging system, as well as an improvement of the computational efficiency of the detectionalgorithms. In this paper, we explore the use of several dimensionality reduction techniques that can be easily integrated into the imaging sensors. We also investigate their effect on the performance of classical target detection techniques for hyperspectral images, including spectral matched filters (SMF), matched subspace detectors (MSD), support vector machines (SVM), and RX anomaly detection algorithm. Specifically, each N-dimensional spectral pixel is embedded to an M-dimensional measurement space with M << N by a linear transformation (e.g., random measurement matrices, uniform downsampling, PCA). The SMF, MSD, SVM, and RX detectors are then applied to the M-dimensional measurement vectors to detect the targets of interests and their detection performances are compared to those obtained from the entire N-dimensional spectrum by the receiver operating characteristics curves. Through extensive experiments on several HSI datasets, we demonstrate that only 1/5 to 1/3 measurements (i.e., the compression ratio M/N is 1/5 similar to 1/3) are necessary to achieve detection performance comparable to that obtained by exploiting the full N-dimensional pixels.
It is not currently known if it is possible to accurately form a synthetic aperture radar image from N data points in provable near-linear complexity, where accuracy is defined as the l(2) error between the full O(N-2...
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ISBN:
(纸本)9780819486257
It is not currently known if it is possible to accurately form a synthetic aperture radar image from N data points in provable near-linear complexity, where accuracy is defined as the l(2) error between the full O(N-2) backprojection image and the approximate image. To bridge this gap, we present a backprojection algorithm with complexity O(log(1/c) N log N), with c the tunable pixelwise accuracy. It is based on the butterfly scheme, which works for vastly more general oscillatory integrals than the discrete Fourier transform. Unlike previous methods this algorithm allows the user to directly choose the amount of acceptable image error based on a well-defined metric. Additionally, the algorithm does not invoke the far-field approximation or place restrictions on the antenna flight path, nor does it impose the frequency-independent beampattern approximation required by time-domain backprojection techniques.
Managing large document databases is an important task today. Being able to automatically compare document layouts and classify and search documents with respect to their visual appearance proves to be desirable in ma...
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ISBN:
(纸本)9780819484161
Managing large document databases is an important task today. Being able to automatically compare document layouts and classify and search documents with respect to their visual appearance proves to be desirable in many applications. We measure single page documents' similarity with respect to distance functions between three document components: background, text, and saliency. Each document component is represented as a Gaussian mixture distribution;and distances between different documents' components are calculated as probabilistic similarities between corresponding distributions. The similarity measure between documents is represented as a weighted sum of the components' distances. Using this document similarity measure, we propose a browsing mechanism operating on a document dataset. For these purposes, we use a hierarchical browsing environment which we call the document similarity pyramid. It allows the user to browse a large document dataset and to search for documents in the dataset that are similar to the query. The user can browse the dataset on different levels of the pyramid, and zoom into the documents that are of interest.
Modern tracking and fusion settings involve multiple platforms in different locations, tracking different target tracks, focusing on different regions of interest, while using different update rates and sensor resolut...
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ISBN:
(纸本)9780819486240
Modern tracking and fusion settings involve multiple platforms in different locations, tracking different target tracks, focusing on different regions of interest, while using different update rates and sensor resolutions with the goal of providing increased situation awareness in the region by fusing together the diversity of information from each platform. In this paper, a decentralized, distributed fusion architecture is presented along with results and trade studies comparing performance to that of a centralized fusion architecture. The decentralized distributed architecture is designed to work with legacy tracking systems and uses an efficient message passing scheme to share information and coordinate tracks across a diverse group of platforms. This system does not rely on a central node and allows for track information to be maintained at the local level while utilizing track information from other platforms to increase situation awareness. We compare the performance between our distributed approach and a centralized system using simulated airborne sensors operating in overlapping regions of interest with target densities and routes chosen to demonstrate tradeoffs between the different architectures. Preliminary results show that the decentralized distributed system provides similar performance to the centralized fusion system in terms of situation awareness relative to traditional tracking metrics, but at the cost of using an increased communication bandwidth to provide frequent updates to neighboring platforms. Results demonstrate the tradeoff between flexibility and optimality - configuration of the distributed decentralized system to provide increased flexibility and robustness comes at the cost of reduced situation awareness as compared to the centralized system.
Voice conversion (VC) is a process which modifies the speech signal produced by one source speaker so that it sounds like another target speaker. In this paper the transformation is determined by using equal Arabic ut...
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ISBN:
(纸本)9780819489326
Voice conversion (VC) is a process which modifies the speech signal produced by one source speaker so that it sounds like another target speaker. In this paper the transformation is determined by using equal Arabic utterances from source and target speakers;these utterances are time-aligned using dynamic time warping algorithm. A conversion function based on Gaussian mixture model (GMM) is used for transforming the spectral envelope described by line spectral frequencies (LSF) and the residuals are converted using three residual prediction techniques. We also compare between these techniques in the conversion of some Arabic utterances. The quality of the transformed utterances is measured using subjective and objective evaluations.
Recently watermarking algorithms of digital images based on singular value decomposition (SVD) have been proposed. Most SVD-based watermarking techniques use singular values as the embedding watermark information. The...
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Recently watermarking algorithms of digital images based on singular value decomposition (SVD) have been proposed. Most SVD-based watermarking techniques use singular values as the embedding watermark information. These SVD-based techniques are advantageous for watermarking images since slight changes in the singular values do not significantly affect the image quality. However, it has been reported that these SVD-based watermarking algorithms, as implemented, suffer from a very high probability of false positive detections of watermarks. The false positive detection vulnerability of two recent SVD-based watermarking schemes is exposed. Two solutions are proposed to mitigate this vulnerability without changing the design principle of these watermarking algorithms. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3327935]
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