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

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Quantitative Image Restoration

Quantitative Image Restoration

引用

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

作者： Gladkova, Irina Grossberg, Michael Shahriar, Fazlul CUNY City Coll NOAA CREST New York NY 10031 USA

ISBN: (纸本)9780819481597

Even with the most extensive precautions and careful planning, space based imagers will inevitably experience problems resulting in partial data corruption and possible loss. Such a loss occurs, for example, when individual image detectors are damaged. For a scanning imager this results in missing lines in the image. Images with missing lines can wreak havoc since algorithms not typically designed to handle missing pixels. Currently the metadata stores the locations of missing data, and naive spatial interpolation is used to fill it in. Naive interpolation methods can create image artifacts and even statistically or physically implausible image values. We present a general method, which uses non-linear statistical regression to estimate the values of the missing data in a principled manner. A statistically based estimate is desirable because it will preserve the statistical structure of the uncorrupted data and avoid the artifacts of naive interpolation. It also means that the restored images are suitable as input for higher-level statistical products. Previous methods replaced the missing values with those of a single closely related band, by applying a function or lookup table. We propose to use the redundant information in multiple bands to restore the lost information. The estimator we present in this paper uses values in a neighborhood of the pixel to be estimated, and propose a value based on training data from the uncorrupted pixels. Since we use the spatial variations in other channels, we avoid the blurring inherent spatial interpolation, which have implicit smoothness priors.

关键词： Sensors Image restoration Clouds MODIS detection and tracking algorithms Image processing Imaging systems Statistical analysis Solids Image sensors

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THIN CIRRUS CLOUD detection - A PRELIMINARY-STUDY

THIN CIRRUS CLOUD DETECTION - A PRELIMINARY-STUDY

引用

CONF ON PASSIVE INFRARED REMOTE SENSING OF CLOUDS AND THE ATMOSPHERE

作者： RAMOSJOHNSON, MP RASMUSSEN, RG The Analytic Sciences Corp. (United States)

ISBN: (纸本)0819411701

Coincident lidar and satellite observations of thin and subvisual cirrus were collected to determine the probability of cirrus detection as a function of optical depth for several satellite systems. Satellite observations include those from DMSP (smooth), NOAA Polar Orbiter (GAC), GOES, GOES VAS and NOAA HIRS processed with the CO₂ slicing algorithm, and the RTNEPH. Different cirrus cloud detection techniques, namely, those of the RTNEPH, manual detection, Phillips Laboratory's (PL) multispectral image analysis scheme, and the CO₂ slicing algorithm were applied to the lidar-coincident satellite data. Each satellite image was examined for evidence of cirrus clouds at the lidar location. The binary (yes/no) results were then used in a nonlinear regression technique to determine the probability of detection as a function of optical depth. The results show that the VAS and HIRS data processed with the CO₂ slicing algorithm detected thin cirrus most of the time with probability of detection (POD) of 91% and 75%, respectively.

关键词： Satellites LIDAR Clouds Carbon dioxide detection and tracking algorithms Earth observing sensors Satellite imaging Binary data Image analysis Multispectral imaging

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Design of adversarial targets: fooling deep ATR systems 29

Design of adversarial targets: fooling deep ATR systems

引用

SPIE's Conference on Automatic Target Recognition XXIX

作者： Osahor, Uche M. Nasrabadi, Nasser M. West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781510626423

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition in various computer vision applications. However, recent findings have shown that such state of the art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input image. In this paper, we focus on deceiving Automatic Target Recognition(ATR) classiers. These classiers are built to recognize specified targets in a scene and also simultaneously identify their class types. In our work, we explore the vulnerabilities of DCNN-based target classifiers. We demonstrate signicant progress in developing infrared adversarial target by adding small perturbations to the input image such that the image perturbation cannot be easily detected. The algorithm is built to adapt to both targeted and non-targeted adversarial attacks. Our findings reveal promising results that reect serious implications of adversarial attacks.

关键词： Automatic target recognition Target detection Classification systems detection and tracking algorithms Target recognition Image classification Image segmentation Sensors Computing systems Evolutionary algorithms

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PROPAGATION OF ERRORS IN DIFFERENT PHASE-SHIFTING algorithms - A SPECIAL PROPERTY OF THE ARC TANGENT FUNCTION

PROPAGATION OF ERRORS IN DIFFERENT PHASE-SHIFTING ALGORITHMS...

引用

CONF ON INTERFEROMETRY : TECHNIQUES AND ANALYSIS

作者： LARKIN, KG OREB, BF CSIRO (Australia)

ISBN: (纸本)0819409286

The mathematical properties of the arctangent function are investigated, revealing the mechanism of error propagation in digital phase-shifting applications. The analysis gives a systematic approach to the assessment of errors and, as such, provides a valuable tool for understanding the propagation of errors in any phase-shifting algorithm.

关键词： Error analysis Phase shifts Phase shifting detection and tracking algorithms Interferometry Sensors Signal detection Aluminum Modulation Phase measurement

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Focus of attention in UWB SAR image

Focus of attention in UWB SAR image

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Conference on Radar Sensor Technology II

作者： Yen, LK Principe, JC Fisher, J Univ. of Florida (United States)

ISBN: (纸本)0819424811

The previous work demonstrate that detection based on the Laguerre transform exploits the ID resonance response of the metallic target in the ultra wideband (UWB) synthetic aperture radar (SAR) images'. The success is mainly due to the fact that Laguerre function space captures the information contained in the resonant response. However, in that case, only ID resonance response information were utilized. In this paper, a new CFAR statistics based on spatial resonance templates is proposed. Based on the GLRT, the new CFAR statistics is formulated using the spatial extent of targets' resonance responses in the UWB SAR scenario. In the end, we show that the proposed CFAR statistics yields more robust detection.

关键词： Target detection Synthetic aperture radar Statistical modeling Signal detection detection and tracking algorithms Silicon Interference (communication) Linear filtering Statistical analysis Computer engineering

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METHOD TO EXTRACT AND RECOGNIZE TOPOGRAPHIC SYMBOLS FROM CONTOUR MAPS

METHOD TO EXTRACT AND RECOGNIZE TOPOGRAPHIC SYMBOLS FROM CON...

引用

2ND INTERNATIONAL CONF ON AUTOMATIC OBJECT RECOGNITION

作者： HUANG, QM PAN, SJ ZHANG, TW Harbin Institute of Technology (China)

ISBN: (纸本)0819408654

To extract and recognize topographic symbols from topographic contour maps is a practical problem in the field of pattern recognition. In this paper, we try to put forward an effective method to solve this problem. By using this method, the symbols are first entracted from the topographic contour maps by eliminating the background of the symbols, and then they are recognized by a neural net, the Hamming net. Experimental results show the effectiveness of this method.

关键词： Binary data detection and tracking algorithms Object recognition Image enhancement Neural networks Pattern recognition Logic Computer science Light sources and illumination Ranging

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Biologically inspired feature based categorization of objects.

Biologically inspired feature based categorization of object...

引用

Conference on Human Vision and Electronic Imaging IX

作者： Mundhenk, TN Navalpakkam, V Makaliwe, H Vasudevan, S Itti, L Univ So Calif Dept Comp Sci Henry Salvatori Comp Sci Ctr Los Angeles CA 90089 USA

ISBN: (纸本)0819451959

We have developed a method for clustering features into objects by taking those features which include intensity, orientations and colors from the most salient points in an image as determined by our biologically motivated saliency program. We can train a program to cluster these features by only supplying as training input the number of objects that should appear in an image. We do this by clustering from a technique that involves linking nodes in a minimum spanning tree by not only distance, but by a density metric as well. We can then form classes over objects or object segmentation in a novel validation set by training over a set of seven soft and hard parameters. We discus as well the uses of such a flexible method in landmark based navigation since a robot using such a method may have a better ability to generalize over the features and objects.

关键词： Algorithm development Image segmentation Independent component analysis Visualization Feature extraction Cameras detection and tracking algorithms Expectation maximization algorithms Navigation systems Object recognition

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SMART MUNITION THERMAL SENSOR MODEL FOR EVALUATING EFFECTS OF TERRAIN AND ENVIRONMENT

SMART MUNITION THERMAL SENSOR MODEL FOR EVALUATING EFFECTS O...

引用

CONF ON CHARACTERIZATION, PROPAGATION, AND SIMULATION OF INFRARED SCENES

作者： SCOGGINS, RK MIXON, HD SABOL, BM U.S. Army Engineer Waterways Experiment Station (United States)

ISBN: (纸本)0819403628

Current engineering-level smart munition sensor models emphasize sensor/target interactions with detection and aim-point information being the principal outputs. Background is not treated with the same fidelity. False alarm rate is based on captive flight statistics and is not actually simulated. The lack of a means to evaluate effects of background in an end-to-end simulation mode motivated the development of the WES Smart Munition Thermal Sensor Model. The model consists of a generic set of algorithms used to simulate platform dynamics, scanning geometry, and infrared sensor optics and electronics. Thermal target models of a vehicle developed by Georgia Tech Research Institute are instantiated into a background scene consisting of calibrated thermal imagery. Parameters are set to reflect the flight dynamics, scanning, optics and electronics of a specified munition, and the output voltage is processed though an appended target acquisition algorithm. A hypothetical smart munition with a thermal sensor (simple flying spot detector) is configured and flown over high-resolution thermal imagery obtained from selected locations to demonstrate effects of varied terrain and environmental conditions.

关键词： Sensors detection and tracking algorithms Thermal modeling Infrared sensors Computer simulations Target acquisition Data modeling Thermography Target detection Infrared radiation

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Resolution limits and efficient signal processing for fiber optic Bragg grating sensors with direct spectroscopic detection

Resolution limits and efficient signal processing for fiber ...

引用

Conference on Optical Measurement Systems for Industrial Inspection III

作者： Miridonov, SV Shlyagin, AG Spirin, VV CICESE Res Ctr San Diego CA 92143 USA

ISBN: (纸本)0819450146

Theoretical resolution limits of evaluation of centroid shift in fiber optic Bragg grating sensors are analyzed. In the theoretical model, it is assumed that random additive Gaussian noise in a sensor signal acquisition equipment is the main origin of the system error. To provide noise resistant demodulation, a new optimal signal processing algorithm is suggested. It allows accurate detection of the Bragg wavelength shifts within large range and keeps resolution very close to theoretical limit imposed by a random noise. The new demodulation technique is also compared with well known centroid detection algorithm.

关键词： Demodulation Fiber Bragg gratings Sensors Signal to noise ratio Error analysis detection and tracking algorithms Fiber optics sensors Interference (communication) Refractive index Signal processing

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FEATURE COMPETITION AND DOMAIN OF ATTRACTION IN ARTIFICIAL-PERCEPTRON PATTERN-RECOGNIZER 2

FEATURE COMPETITION AND DOMAIN OF ATTRACTION IN ARTIFICIAL-P...

引用

CONF ON NEURAL AND STOCHASTIC METHODS IN IMAGE AND SIGNAL PROCESSING 2

作者： HU, CLJ Southern Illinois Univ./Carbondale (United States)

ISBN: (纸本)0819412813

As we reported previously, learning of a multi-layered hard-limited perceptron can be formulated into a set of simultaneous linear inequalities. Solving these inequalities under a given training set would then allow us to achieve the goal of learning in this system. If the dimension N of the input vector is much larger than the number M of different patterns to be learned, then there is considerable freedom for the system to select a proper solution of the connection matrix. In most cases, even a single layer perceptron will do the learning satisfactorily. This paper reports the results of some theoretical and experimental studies of this one-layered, hard-limited perceptron trained under the novel, one-step, noniterative learning scheme. Particularly, the analysis of some important properties of this novel learning system, such as automatic feature competition, domain of convergence, and robustness of recognition, are discussed in detail.

关键词： Analog electronics Binary data detection and tracking algorithms Boron Brain mapping Electrical engineering Machine learning Neural networks Neurons Thulium

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