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Conference on Signal Processing, Sensor Fusion, and Target Recognition XXI

作者： Mahler, Ronald El-Fallah, Adel Lockheed Martin MS2 Eagan MN USA Scientific Syst Co Inc Woburn MA USA

ISBN: (纸本)9780819490704

Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most familiar sensors, such as surveillance and imaging radars, violate assumption (c). This is because they are actually superpositional-that is, any measurement is a sum of signals generated by all of the targets in the scene. At this conference in 2009, the first author derived exact formulas for PHD and CPHD filters that presume general superpositional measurement models. Unfortunately, these formulas are computationally intractable. In this paper, we modify and generalize a Gaussian approximation technique due to Thouin, Nannuru, and Coates to derive a computationally tractable superpositional-CPHD filter. Implementation requires sequential Monte Carlo (particle filter) techniques.

关键词： Filtering (signal processing) Sensors Diffusion weighted imaging Linear filtering detection and tracking algorithms Electronic filtering Signal generators Surveillance Gaussian filters Radar

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Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system

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JOURNAL OF ELECTRONIC IMAGING 2012年第4期21卷 043008-043008页

作者： Lettsome, Clyde A. Tsai, Yi-Chang Kaul, Vivek Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA

We incorporate, evaluate, and assess the feasibility of using filter banks in automated pavement distress systems from a system level. We integrate a novel filter-bank-based distress segmentation method, which, unlike previously researched methods, does not depend on highpass data. In addition, we incorporate the standard Said Pearlman set partitioning in hierarchical trees compression coder into the automated pavement distress system, which is a first in this area of research. A third contribution of the research is a statistical detection algorithm that assists in overall system performance. Preliminary testing using images provided by the Georgia Department of Transportation demonstrate the promise of the proposed method. (c) 2012 SPIE and IS&T. [DOI: 10.1117/***.21.4.043008]

关键词： detection and tracking algorithms Edge detection Wavelets Matrices

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Reliable tracking algorithm for multiple reference frame motion estimation

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JOURNAL OF ELECTRONIC IMAGING 2011年第3期20卷 033003-033003-14页

作者： Lee, Tsz-Kwan Chan, Yui-Lam Fu, Chang-Hong Siu, Wan-Chi Hong Kong Polytech Univ Ctr Signal Proc Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China

Multiple reference frame motion estimation (MRF-ME) is one of the most crucial tools in H.264/AVC to improve coding efficiency. However, it disciplines an encoder by giving extra computational complexity. The required computation proportionally expands when the number of reference frames used for motion estimation increases. Aiming to reduce the computational complexity of the encoder, various motion vector (MV) composition algorithms for MRF-ME have been proposed. However, these algorithms only perform well in a limited range of reference frames. The performance deteriorates when motion vector composition is processed from the current frame to a distant reference frame. In this paper, a reliable tracking mechanism for MV composition is proposed by utilizing only the relevant areas in the target macroblock and taking different paths through a novel selection process from a set of candidate motion vectors. The proposed algorithm is especially suited for temporally remote reference frames in MRF-ME. Experimental results show that compared with the existing MV composition algorithms, the proposed one can deliver a remarkable improvement on the rate-distortion performance with similar computational complexity. (C) 2011 SPIE and IS&T. [DOI: 10.1117/1.3605574]

关键词： algorithms detection and tracking algorithms Motion estimation Video

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Steganalysis in high dimensions: Fusing classifiers built on random subspaces

Steganalysis in high dimensions: Fusing classifiers built on...

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Conference on Media Watermarking, Security, and Forensics III

作者： Kodovsky, Jan Fridrich, Jessica SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA

ISBN: (纸本)9780819484178

By working with high-dimensional representations of covers, modern steganographic methods are capable of preserving a large number of complex dependencies among individual cover elements and thus avoid detection using current best steganalyzers. Inevitably, steganalysis needs to start using high-dimensional feature sets as well. This brings two key problems - construction of good high-dimensional features and machine learning that scales well with respect to dimensionality. Depending on the classifier, high dimensionality may lead to problems with the lack of training data, infeasibly high complexity of training, degradation of generalization abilities, lack of robustness to cover source, and saturation of performance below its potential. To address these problems collectively known as the curse of dimensionality, we propose ensemble classifiers as an alternative to the much more complex support vector machines. Based on the character of the media being analyzed, the steganalyst first puts together a high-dimensional set of diverse " prefeatures" selected to capture dependencies among individual cover elements. Then, a family of weak classifiers is built on random subspaces of the prefeature space. The final classifier is constructed by fusing the decisions of individual classifiers. The advantage of this approach is its universality, low complexity, simplicity, and improved performance when compared to classifiers trained on the entire prefeature set. Experiments with the steganographic algorithms nsF5 and HUGO demonstrate the usefulness of this approach over current state of the art.

关键词： Steganalysis Machine learning Matrices Databases Calibration Error analysis Sensors Ferroelectric LCDs detection and tracking algorithms Computer engineering

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A Bayesian approach to Activity detection in video using Multi-Frame Correlation Filters

A Bayesian approach to Activity Detection in video using Mul...

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Conference on Automatic Target Recognition XXI

作者： Mahalanobis, Abhijit Stanfill, Robert Chen, Kenny Martin, Lockheed Missiles & Fire Control Orlando FL 32819 USA

ISBN: (纸本)9780819486233

Multi-frame correlation filters have been recently reported in the literature for the detection of moving objects. Introduced by Kerekes and Kumar [5], this technique uses a motion model to accumulate evidence over time in a Bayesian framework to improve the receiver operating characteristic (ROC) curve. In this paper, we generalize the approach to not only detect objects, but also their activities by using separate motion models to represent each activity. We also discuss results of preliminary simulations using publicly released aerial data set to illustrate the concept.

关键词： Motion models Video Image filtering detection and tracking algorithms Sensors Video surveillance Target detection Linear filtering Receivers Surveillance

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Multiple-Agent Adaptation in Whole-book Recognition

Multiple-Agent Adaptation in Whole-book Recognition

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Conference on Document Recognition and Retrieval XVIII

作者： Xiu, Pingping Baird, Henry S. Lehigh Univ CSE Dept Bethlehem PA 18017 USA

ISBN: (纸本)9780819484116

In order to accurately recognize textual images of a book, we often employ various models including iconic model (for character classification), dictionary (for word recognition), character segmentation model, etc., which are derived from prior knowledge. Imperfections in these models affect recognition performance inevitably. In this paper, we propose an unsupervised learning technique that adapts multiple models on-the-fly on a homogeneous input data set to achieve a better overall recognition accuracy fully automatically. The major challenge for this unsupervised learning process is, how to make models improve rather than damage one another? In our framework, models measure disagreements between their input data and output data. We propose a policy based on disagreements to adapt multiple models simultaneously (or alternately) safely. We will construct a book recognition system based on this framework, and demonstrate its feasibility.

关键词： Data modeling Associative arrays Image segmentation Optical character recognition detection and tracking algorithms Error analysis Process modeling Image processing Machine learning Statistical modeling

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Joint Sparsity for Target detection

Joint Sparsity for Target Detection

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Conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII

作者： Chen, Yi Nasrabadi, Nasser M. Tran, Trac D. Johns Hopkins Univ Dept Elect & Comp Engn 3400 N Charles St Baltimore MD 21218 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9780819486226

In this paper, we propose a joint sparsity model for target detection in hyperspectral imagery. The key innovative idea here is that hyperspectral pixels within a small neighborhood in the test image can be simultaneously represented by a linear combination of a few common training samples, but weighted with a different set of coefficients for each pixel. The joint sparsity model automatically incorporates the inter-pixel correlation within the hyperspectral imagery by assuming that neighboring spectral pixels usually consists of similar materials. The sparse representations of the neighboring pixels are obtained by simultaneously decomposing the pixels over a given dictionary consisting of training samples of both the target and background classes. The recovered sparse coefficient vectors are then directly used for determining the label of the test pixels. Simulation results on several real hyperspectral images show that the proposed algorithm based on the joint sparsity model outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, adaptive subspace detectors, as well as binary classifiers such as support vector machines.

关键词： Chemical species Associative arrays Target detection detection and tracking algorithms Sensors Hyperspectral imaging Hyperspectral target detection Single mode fibers Astatine Statistical modeling

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Fast detection of Network Intrusion

Fast Detection of Network Intrusion

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Conference on Defense Transformation and Net-Centric Systems

作者： Chen, Xinjia Walker, Ernest Southern Univ Dept Elect Engn Baton Rouge LA 70813 USA

ISBN: (纸本)9780819486363

In this paper, we propose new network intrusion detection techniques which promptly detect malicious attacks and thus lower the resulting damage. Moreover, our approach rigorously control the probability of falsely implicating benign remote hosts as malicious. Such technique is especially suitable for detecting DoS attackers and port-scan attackers routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. Our method performs significantly faster and also more accurate than other current solutions.

关键词： detection and tracking algorithms Algorithm development Zinc Scanners Computer intrusion detection Internet Detector development Network security Probability theory Target detection

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Autonomous landing and ingress of micro-air-vehicles in urban environments based on monocular vision.

Autonomous landing and ingress of micro-air-vehicles in urba...

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Conference on Micro- and Nanotechnology Sensors, Systems, and Applications III

作者： Brockers, Roland Bouffard, Patrick Ma, Jeremy Matthies, Larry Tomlin, Claire CALTECH Jet Prop Lab Pasadena CA 91125 USA Univ Calif Berkeley Elect Engn & Comp Sci Berkeley CA USA

ISBN: (纸本)9780819486059

Unmanned micro air vehicles (MAVs) will play an important role in future reconnaissance and search and rescue applications. In order to conduct persistent surveillance and to conserve energy, MAVs need the ability to land, and they need the ability to enter (ingress) buildings and other structures to conduct reconnaissance. To be safe and practical under a wide range of environmental conditions, landing and ingress maneuvers must be autonomous, using real-time, onboard sensor feedback. To address these key behaviors, we present a novel method for vision-based autonomous MAV landing and ingress using a single camera for two urban scenarios: landing on an elevated surface, representative of a rooftop, and ingress through a rectangular opening, representative of a door or window. Real-world scenarios will not include special navigation markers, so we rely on tracking arbitrary scene features;however, we do currently exploit planarity of the scene. Our vision system uses a planar homography decomposition to detect navigation targets and to produce approach waypoints as inputs to the vehicle control algorithm. Scene perception, planning, and control run onboard in real-time;at present we obtain aircraft position knowledge from an external motion capture system, but we expect to replace this in the near future with a fully self-contained, onboard, vision-aided state estimation algorithm. We demonstrate autonomous vision-based landing and ingress target detection with two different quadrotor MAV platforms. To our knowledge, this is the first demonstration of onboard, vision-based autonomous landing and ingress algorithms that do not use special purpose scene markers to identify the destination.

关键词： Target detection Cameras detection and tracking algorithms Navigation systems Motion estimation Sensors Micro unmanned aerial vehicles Unmanned aerial vehicles 3D image processing Vehicle control

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Novel EO/IR sensor technologies

Novel EO/IR sensor technologies

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Conference on Infrared Sensors, Devices, and Applications and Single Photon Imaging II

作者： Lewis, Keith Sciovis Ltd Malvern WR14 2TE Worcs England

ISBN: (纸本)9780819487650

The requirements for advanced EO/IR sensor technologies are discussed in the context of evolving military operations, with significant emphasis on the development of new sensing technologies to meet the challenges posed by asymmetric threats. The Electro-Magnetic Remote Sensing (EMRS DTC) was established in 2003 to provide a centre of excellence in sensor research and development, supporting new capabilities in key military areas such as precision attack, battlespace manoeuvre and information superiority. In the area of advanced electro-optic technology, the DTC has supported work on discriminative imaging, advanced detectors, laser components/technologies, and novel optical techniques. This paper provides a summary of some of the EO/IR technologies explored by the DTC.

关键词： Sensors Polarimetry Target detection Cameras Imaging systems Sensor technology Staring arrays detection and tracking algorithms Image processing Target recognition

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