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检索条件"主题词=Detection and tracking algorithms"

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Recursive estimation methods for tracking of localized perturbations in absorption using diffuse optical tomography

Recursive estimation methods for tracking of localized pertu...

引用

Conference on Computational Imaging III

作者： Hamdi, A Miller, EL Boas, D Franceschini, MA Kilmer, ME Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)0819456470

Analysis of the quasi-sinusoidal temporal signals measured by a Diffuse Optical Tomography (DOT) instrument can be used to determine both quantitative and qualitative characteristics of functional brain activities arising from visual and auditory simulations, motor activities, and cognitive tasks performances. Once the activated regions in the brain are resolved using DOT, the temporal resolution of this modality is such that one can track the spatial evolution (both the location and morphology) of these regions with time. In this paper, we explore a state-estimation approach using Extended Kalman Filters to track the dynamics of functionally activated brain regions. We develop a model to determine the size, shape, location and contrast of an area of activity as a function of time. Under the assumption that previously acquired MRI data has provided us with a segmentation of the brain, we restrict the location of the area of functional activity to the thin, cortical sheet. To describe the geometry of the region, we employ a mathematical model in which the projection of the area of activity onto the plane of the sensors is assumed to be describable by a low dimensional algebraic curve. In this study, we consider in detail the case where the perturbations in optical absorption parameters arising due to activation are confined to independent regions in the cortex layer. We estimate the geometric parameters (axis lengths, rotation angle, center positions) defining the best fit ellipse for the activation area's projection onto the source-detector plane. At a single point in time, an adjoint field-based nonlinear inversion routine is used to extract the activated area's information. Examples of the utility of the method will be shown using synthetic data.

关键词： Brain Absorption Sensors Optical properties Filtering (signal processing) Data modeling detection and tracking algorithms Diffuse optical tomography Diffusion Optical tracking

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Statistical model of interpolation and its application to texture segmentation

Statistical model of interpolation and its application to te...

引用

8th International Workshop on Nondestructive Testing and Computer Simulations in Science and Engineering

作者： Bakhvalov, YN Potapov, AS Cherepovets State Univ Cherepovets 162600 Russia

ISBN: (纸本)0819458244

The work addresses the problem of pattern recognition as the task of n-dimensional interpolation of the probability density function with subsequent thresholding. Interpolating function that describes given learning data set is treated as a random process. The task is reduced to simultaneous linear equations. The proposed method can be considered as the construction of an RBF neural network with nonstandard transfer function. The transfer function is chosen to minimize energy of the random process spectrum. Applications of the developed algorithm to the task of texture segmentation are given and comparison with alternative approaches is carried out.

关键词： Image segmentation Neural networks Pattern recognition Correlation function detection and tracking algorithms 3D modeling Algorithm development Data modeling Evolutionary algorithms Statistical analysis

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Domain decomposition method for diffuse optical tomography

Domain decomposition method for diffuse optical tomography

引用

Conference on Computational Imaging III

作者： Kwon, K Son, IY Yazici, B Rensselaer Polytech Inst Troy NY 12181 USA

ISBN: (纸本)0819456470

Diffuse optical tomography is modelled as an optimization problem to find the absorption and scattering coefficients that minimize the error between the measured photon density function and the approximated one computed using the coefficients. The problem is composed of two steps: the forward solver to compute the photon density function and its Jacobian (with respect to the coefficients), and the inverse solver to update the coefficients based on the photon density function and its Jacobian attained in the forward solver. The resulting problem is nonlinear and highly ill-posed. Thus, it requires large amount of computation for high quality image. As such, for real time application, it is highly desirable to reduce the amount of computation needed. In this paper, domain decomposition method is adopted to decrease the computation complexity of the problem. Two level multiplicative overlapping domain decomposition method is used to compute the photon density function and its Jacobian at the inner loop and extended to compute the estimated changes in the coefficients in the outer loop. Local convergence for the two-level space decomposition for the outer loop is shown for the case when the variance of the coefficients is small.

关键词： Sensors Chemical elements Diffuse optical tomography Optical computing Diffusion Absorption detection and tracking algorithms Neon Image quality Scattering

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Conjugate gradient mojette reconstruction

Conjugate gradient mojette reconstruction

引用

Medical Imaging 2005 Conference

作者： Servières, M Idier, J Normand, N Guédon, JP Univ Nantes Ecole Polytech CNRS UMR 6597IVCIRCC&N F-44306 Nantes France

ISBN: (纸本)0819457213

Iterative methods are now recognized as powerful tools to solve inverse problems such as tomographic reconstruction. In this paper, the main goal is to present a new reconstruction algorithm made from two components. An iterative algorithm, namely the Conjugate Gradient (CG) method, is used to solve the tomographic problem in the least square (LS) sense for our specific discrete Mojette geometry. The results are compared (with the same geometry) to the corresponding Mojette Filtered Back Projection (FBP) method. In the fist part of the paper, we recall the discrete geometry used to define the projection M and backprojection M* operators. In the second part, the CG algorithm is presented within the context of the Mojette geometry. Noise is then added onto these Mojette projections with respect to the sampling and reconstructions are performed. Finally the Toeplitz block Toeplitz (TBT) character of M*M is demonstrated.

关键词： Reconstruction algorithms Tomography detection and tracking algorithms Image processing Inverse problems Iterative methods Medical imaging

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Unusual activity detection for persistent target surveillance

Unusual activity detection for persistent target surveillanc...

引用

Conference on Sensors, and Command, Control, Communications, and Intelligence (C31) Technologies for Homeland Security and Homeland Defense IV

作者： He, ZH Univ Missouri Dept Elect & Comp Engn Columbia MO 65203 USA

ISBN: (纸本)0819457639

The information generated by the integrated sensor suite is massive. Simply bringing the information to the decision makers is a cognitive disaster due to the information overload. Intelligent algorithms need to be developed to exploit and filter the information. In this work, we seek to develop content-level change detection and activity-level information fusion and filtering algorithms to detect unusual activities for persistent target surveillance applications.

关键词： Algorithm development detection and tracking algorithms Video surveillance Sensors Surveillance Target detection Video Image registration Unmanned aerial vehicles Infrared sensors

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Elements of target detection for ground sensor systems

Elements of target detection for ground sensor systems

引用

Conference on Unattended Ground Sensor Technologies and Applications VII

作者： Martin, L Lockheed Martin Missiles & Fire Control Orlando FL 32819 USA

ISBN: (纸本)0819457817

The primary application considered in this paper is target detection in infra-red (IR) imagery for ground surveillance application. The problem is characterized by the need to rapidly search large areas, and ascertain the presence and location of threat objects. The challenge is in not only processing large images rapidly, but also reducing false alarm rates while achieving high levels of correct target detections. Performance is a function of many factors including the clutter type and density, the quality of the image and target signature, and the adequacy of the target data base. Although many types of target detection algorithms exist, we use a method based on quadratic correlation filters. The results of independent tests and evaluations by Night Vision Laboratory are also presented.

关键词： Target detection Sensors Image processing Linear filtering Image filtering Target recognition Surveillance detection and tracking algorithms Infrared imaging Digital filtering

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High productivity object-oriented defect detection algorithms for the new modular die-to-database reticle inspection platform

High productivity object-oriented defect detection algorithm...

引用

21st European Mask and Lithography Conference (EMLC 2005)

作者： Avakaw, S Planar State Sci & Prod Concern Precis Machine Bl Minsk 220763 BELARUS

ISBN: (纸本)0819458309

The paper presents a description of one of the main elements of a new modular automatic reticle defect inspection platform - a defect detection sub-system. This platform is currently under active development at Planar Concern. This paper presents the results of the use of the object-oriented approach which was used in the development of the defect detection algorithms for the die-to-database reticle inspection system. Furthermore, the paper presents briefly the architecture and technology of the new modular automatic reticle inspection platform.

关键词： Reticles Inspection Defect detection detection and tracking algorithms Algorithm development Databases Data modeling Lithography Image enhancement Image processing

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Sequential collaborative processing for energy efficiency and fault tolerance in unattended ground sensor networks

Sequential collaborative processing for energy efficiency an...

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Conference on Unattended Ground Sensor Technologies and Applications VII

作者： Wang, XL Qi, HR Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)0819457817

Multiple target detection in sensor networks is a. challenging problem since the signal captured by individual sensor nodes is normally a linear/nonlinear weighted mixture of geometrically diverse source signals. Independent component analysis (ICA) has been widely used to solve the source estimation problem, but most of the algorithms assume the number of sources is fixed and is equal to the number of observations, which generally is not the case in sensor networks. Even though several methods are put forward for source number estimation, traditional centralized schemes hinder their application in sensor networks due to extremely constrained resource and scalability issues. In this paper, a sequential source number estimation framework is developed, where we assume the sensor network has been self-organized into clusters. The determination of possible number of targets at a sensor is only based on its local observation and the estimation result received from its previous sensor. Therefore, raw data transmission is avoided and only small packets of partial estimation results are transmitted through the networks. Based on the local estimation generated within each cluster, a posterior probability fusion method based on the Bayes' theorem is derived. Experimental results show that using the sequential processing approach with probability fusion, the detection probability and reliability are improved over the centralized scheme while significantly reducing network traffic and conserving resources.

关键词： Sensors Sensor networks Target detection Signal detection Unattended ground sensors detection and tracking algorithms Error analysis Independent component analysis Fusion energy Signal processing

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Discriminating man-made and natural objects in sidescan sonar imagery: human versus computer recognition performance

Discriminating man-made and natural objects in sidescan sona...

引用

15th Conference on Automatic Target Recognition

作者： Kessel, RT Myers, VL Def R&D Canada Halifax NS Canada

ISBN: (纸本)0819457922

Sidescan sonar is increasingly accepted as the sensor of choice for sea minehunting over large areas in shallow water. Automatic Target Recognition (ATR) algorithms are therefore being developed to assist and, in the case of autonomous vehicles, even replace the human operator as the primary recognition agent deciding whether an object in the sonar imagery is a mine or simply benign seafloor clutter. Whether ATR aids or replaces a human operator, a natural benchmark for judging the quality of ATR is the unaided human performance when ATR is not used. The benchmark can help when estimating the performance benefit (or cost) of switching from human to automatic recognition for instance, or when planning how human and machine should best interact in cooperative search operations. This paper reports a human performance study using a large library of real sonar images collected for the development and testing of ATR algorithms. The library features 234 mine-like man-made objects deployed for the purpose, as well as 105 instances of naturally occurring clutter. The human benchmark in this case is the average of ten human subjects expressed in terms of a receiver operating characteristic (ROC) curve. An ATR algorithm for man-made/natural object discrimination is also tested and compared with the human benchmark. The implications of its relative performance for the integration of ATR are considered.

关键词： Automatic target recognition Mining Algorithm development detection and tracking algorithms Target recognition Human subjects Speckle Object recognition Sensors Receivers

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Parameter adaptation for target recognition in LADAR

Parameter adaptation for target recognition in LADAR

引用

15th Conference on Automatic Target Recognition

作者： Stevens, MR Monnier, C Snorrason, MS Charles River Analyt Cambridge MA USA

ISBN: (纸本)0819457922

Automatic Target Recognition (ATR) algorithms are extremely sensitive to differences between the operating conditions under which they are trained and the extended operating conditions in which the fielded algorithms operate. For ATR algorithms to robustly recognize targets while retaining low false alarm rates, they must be able to identify the conditions under which they are operating and tune their parameters on the fly. In this paper, we present a method for tuning the parameters of a model based ATR algorithm using estimates of the current operating conditions. The problem has two components: 1) identifying the current operating conditions and 2) using that information to tune parameters to improve performance. In this paper, we explore the use of a reinforcement learning technique called tile coding for parameter adaptation. In tile coding, we first define a set of valid states describing the world (the operating conditions of interest, such as the level of obscuration). Next, actions (or parameter settings used by the ATR) are defined that are applied when in that state. Parameter settings for each operating condition are learned using an off-line reinforcement learning feedback loop. The result is a lookup table to select the optimal parameter settings for each operation condition. We present results on real LADAR imagery based on parameter tuning learned off-line using synthetic imagery.

关键词： LIDAR Automatic target recognition detection and tracking algorithms Data modeling Sensors 3D modeling Target recognition Image segmentation Target detection 3D acquisition

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