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Object localization/segmentation using generic shape priors

Object localization/segmentation using generic shape priors

引用

International conference on Pattern Recognition

作者： M. Fussenegger A. Opelt A. Pinz Institute of Electrical Measurement and Measurement Signal Processing Graz University of Technology Austria

Generally object segmentation is an ill-posed problem. Approaches that use only plain image information will often fail. To overcome these limitations, prior knowledge (like information of the object contour) can be added to the segmentation process. In this paper, we present a novel generic shape model. We use the expertise from the field of object class recognition, namely a boundary-fragment-model (BFM) as prior knowledge for our level set segmentation approach. Commonly, shape models need synthetically generated or pre-segmented training sets that are usually trained on one specific object or a small group of objects. With our new approach we are able to train shape models for whole categories, which makes the segmentation method much more flexible. Additionally we overcome the difficulty of the correct initialization and reduce the segmentation effort. Experimental results demonstrate the excellent performance of our method on different types of objects (categories)

关键词： Image segmentation Electric variables measurement Level set Training data signal processing Object segmentation Computer vision Probability density function Active shape model Solid modeling

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3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis

3D and Infrared Face Reconstruction from RGB data using Cano...

引用

International conference on Pattern Recognition

作者： M. Reiter R. Donner G. Langs H. Bischof Pattern Recognition and Image Processing Group Vienna University of Technology Austria Graz University of Technology Institute for Computer Graphics and Vision Austria

In this paper, we apply a multiple regression method based on canonical correlation analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy

关键词： Shape Face detection Lighting Image reconstruction Image analysis Image databases Pattern analysis Rendering (computer graphics) Image color analysis Pattern recognition

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New Adaptive Background Suppression Algorithm via One Step M-filter

New Adaptive Background Suppression Algorithm via One Step M...

引用

International conference on signal processing Proceedings (ICSP)

作者： Moufa Hu Yan Shen Zengping Chen National Key Laboratory of Automatic Target Recognition (ATR) National University of Defense Technology Changsha China College of Electronic Science and Engineering National University of Defense Technology Changsha China

Background suppression is a key technique to small and dim targets automatic detection and tracking. And adaptive background estimation is an efficient way to realize this technique. In this paper, the one step M-filter (OSMF) adaptive background suppression algorithm is presented. In the algorithm, targets and observing noises are considered as mixed noise interference of the image background and then the true image background is estimated adaptively via M-estimation. Firstly, initial value for background clutters is estimated applying the order statistics filter. then the final estimation of background clutters is given using the OSMF. Finally, background suppression is done by removing background clutters from original images. the proposed method is applied to simulations and real visible image data to demonstrate the robustness and effectiveness of the OSMF

关键词： Clutter Background noise Working environment noise Gaussian noise signal to noise ratio Wiener filter Target tracking Interference Aerospace control Charge coupled devices

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Tracking of two closely spaced objects using monopulse measurements

Tracking of two closely spaced objects using monopulse measu...

引用

17th conference on signal and data processing of small targets

作者： Isaac, A Zhang, X Willett, P Bar-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819459186

the ultimate goal of this paper is to track two closely spaced and unresolved targets using monopulse radar measurements, the quality of such tracking being a determinant of successful detection of target spawn. It explores statistical estimation techniques based on the maximum likelihood criterion and Gibbs sampling, and addresses concerns about the accuracy of the measurements delivered thereby. In particular, the Gibbs approach can deliver joint measurements (and the associated covariances) from both targets, and it is therefore natural to consider a joint filter. the ideas are compared, and amongst the various strategies discussed, a particle filter that operates directly on the monopulse measurements is especially promising.

关键词： Radar Filtering (signal processing) Detection and tracking algorithms Particle filters Target detection Particles signal processing Monte Carlo methods signal to noise ratio Statistical analysis

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Comparison of adaptive array processing schemes for land mine detection using hyperspectral imagery - art. no. 591301

Comparison of adaptive array processing schemes for land min...

引用

17th conference on signal and data processing of small targets

作者： Farrell, MD Mersereau, RM Georgia Inst Technol Ctr Signal & Image Proc Atlanta GA 30332 USA

ISBN: (纸本)0819459186

Adaptive techniques for detecting small or difficult targets in the midst of high noise and/or clutter have a rich history in the radar array processing community. However, the utility of these schemes is only beginning to be realized for multichannel electro-optical techniques, specifically hyperspectral imaging (HSI). the data products generated by hyperspectral sensors differ greatly from those of radar and sonar arrays, yet recent studies using HSI data have offered promising results for modified versions of common adaptive detectors. In this paper, we compare a series popular adaptive detection schemes applied to HSI data for the task of land mine detection. Experiments using real hyperspectral image cubes, not Simulations, are performed with data from both the visible-SWIR and LWIR regions. Results are presented for different mine types in a variety of scenes.

关键词： adaptive detection array processing hyperspectral imaging land mines

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Detection and localization of multiple unresolved extended objects via monopulse radar signal processing

Detection and localization of multiple unresolved extended o...

引用

17th conference on signal and data processing of small targets

作者： Zhang, X Willett, P Bax-Shalom, Y Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)0819459186

With high resolution radars, realistic objects should be considered as extended rather than point targets. If several closely-spaced targets fall within the same radar beam and between adjacent matched filter samples in range, the full monopulse information from all of these samples can and should be used for estimation, both of angle and of range (with the range to sub-bin accuracy). To detect and localize multiple unresolved extended targets, we establish a model for monopulse radar returns from extended objects, and use a maximum likelihood estimator to localize the targets. Rissanen's minimum description length (MDL) will be used to decide the number of existing extended objects. We compare the new extended target monopulse processing scheme with previously developed point target monopulse techniques to show the improvement in terms of the estimation of target locations, the detection of the number of existing targets, and the tracking performance with a multiple hypothesis tracker (MHT) using the output from the proposed extended target monopulse processor.

关键词： monopulse radar extended target unresolved target tracking

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Map-enhanced tracking

Map-enhanced tracking

引用

17th conference on signal and data processing of small targets

作者： Sworder, DD Boyd, JE Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819459186

Tracking moving vehicles has received less attention than its its aerial counterpart. the smooth velocity transitions common to aircraft are replaced with abrupt turns and and speed changes. though the kinematic evolution of a ground vehicle is more complex, the path is more restricted. For example, if target motion is constrained by a terrain map, the topography should be integrated into the tracking algorithm. this paper shows by means of an example that the Gaussian wavelet estimator is particularly suited to map-enhanced estimation.

关键词： nonlinear filters target tracking data fusion

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Joint detection and tracking of unresolved targets with a monopulse radar using a particle filter - art. no. 59131T

Joint detection and tracking of unresolved targets with a mo...

引用

17th conference on signal and data processing of small targets

作者： Nandakumaran, N Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this paper, a new algorithm is developed to jointly detect and track more than two targets from a single detected bin. this method involves the use of tracking data in detection. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which is proved to be effective for nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. It in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. that is, we evaluate the likelihoods of different hypotheses of possible combinations of targets being in different detected bins. the hypothesis testing is used to find the correct detection event. the particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data with more than unresolved two targets. Simulation results confirm the possible extraction and tracking of more than two targets jointly.

关键词： U nresolved targets closely-spaced targets monopulse radar multitarget tracking particle filters

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Efficiently identifying close track/observation pairs in continuous timed data

Efficiently identifying close track/observation pairs in con...

引用

17th conference on signal and data processing of small targets

作者： Kubica, J Moore, A Connolly, A Jedicke, R Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)0819459186

In this paper we examine new data structures and algorithms for efficient and accurate gating and identification of potential track/observation associations. Specifically, we focus on the problem of continuous timed data, where observations arrive over a range of time and each observation may have a unique time stamp. For example, the data may be a continuous stream of observations or consist of many small observed subregions. this contrasts with previous work in accelerating this task, which largely assumes that observations can be treated as arriving in batches at discrete time steps. We show that it is possible to adapt established techniques to this modified task and introduce a novel data structure for tractably dealing with very large sets of tracks. Empirically we show that these data structures provide a significant benefit in both decreased computational cost and increased accuracy when contrasted with treating the observations as if they occurred at discrete time steps.

关键词： data association algorithms data structures

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data association in target tracking using priority queues

Data association in target tracking using priority queues

引用

17th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T Farooq, M McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

data association is one of the main components of target tracking. While, in its simplest form, data association links a list of tracks to a list of measurements or links two lists of measurements (2-D association), the more complex problem involves assignment of multiple number of such lists (S-D association where S > 3). In target tracking, the presence of false detections (false alarms) and the absence of detections from some targets (missed detections) complicate the problem of data association further. In this work, we explore the possibility of applying track ordering in priority queues to solve the association problem more efficiently. the basic component of our algorithm is to form priority queues by permutations of the tracks. Each queue is served on a first-come-first-served basis, i.e., each track is assigned to the best measurement available based on its turn in the queue. It can be shown that the best solution to the 2-D problem can be obtained from one of these queues. However, the solution is computationally expensive even for a moderate number of targets. In this paper we show that due to redundancy only a small fraction of the total number of permutations is required to be evaluated to obtain the best solution.

关键词： Multisensor-multitarget tracking 2-D assignment algorithms priority queue m-best solutions

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