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CONF ON SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1990

作者： ARNOLD, J PASTERNACK, H SRI International (United States)

ISBN: (纸本)0819403563

A dynamic programming (DP) technique has been developed for the detection of subpixel-sized, low-SNR targets observed by mosaic imaging sensors. The primary advantages of DP are its sensitivity to weak targets along with its robustness to target maneuvers and sensor instabilities. Such enhancements are achieved by performing target data association and detection in a single optimization procedure. To the extent that the target motion and noise behavior can be accurately modeled, the DP algorithm is shown to be optimal for this task. A prototype JR tracking system based on the DP approach was developed and tested for a step-staring IR camera application. Performance analysis indicates a sensitivity improvement of several dB over conventional sequential detection tracking approaches.

关键词： Target detection detection and tracking algorithms Sensors Signal processing Cameras Computer programming Data processing Optical filters 3D acquisition Image filtering

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detection AND tracking OF A MOVING POINT TARGET IN A LOW SIGNAL-TO-CLUTTER PLUS NOISE ENVIRONMENT

DETECTION AND TRACKING OF A MOVING POINT TARGET IN A LOW SIG...

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5TH CONF ON SIGNAL AND DATA PROCESSING OF SMALL TARGETS

作者： LI, ZY SHEN, ZK LI, ZY National Univ. of Defense Technology (China)

ISBN: (纸本)0819411906

detection and tracking of a moving point target in a low signal-to-clutter plus noise environment is studied in this paper. The clutter plus noise background in sequential images are assumed to be spatially correlated. Two-dimensional adaptive correlation canceling technique is employed to suppress the background noise. An effective searching method for target moving path in image sequence is proposed to find the target position by simple computation as possible.

关键词： Target detection Image processing Interference (communication) Signal detection detection and tracking algorithms Signal processing Environmental sensing Digital filtering Sensors Medical imaging

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Bayesian cluster detection and tracking using a generalized Cheeseman approach

Bayesian cluster detection and tracking using a generalized ...

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

作者： Mahler, R Lockheed Martin Tactical Syst Eagan MN USA

ISBN: (纸本)0819449563

Cluster tracking is the problem of detecting and tracking clustered formations of large numbers of targets, without necessarily being obligated to track each and every individual target. We address this problem by generalizing to the dynamic case a static Bayesian finite-mixture data-clustering approach due to P. Cheeseman. After summarizing Cheeseman's approach, we show that it implicitly draws on random set theory. Making this connection explicit allows us to incorporate it into a multitarget recursive Bayes filter, thereby leading to a rigorous Bayesian foundation for finite-mixture cluster tracking. A computational approach is proposed, based on an approximate, multitarget first-order moment filter ("cluster PHD" filter).

关键词： Digital filtering Target detection Electronic filtering Filtering (signal processing) Data modeling Statistical modeling detection and tracking algorithms Sensor fusion Signal processing Target recognition

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detection and tracking of drones using advanced acoustic cameras 11

Detection and tracking of drones using advanced acoustic cam...

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Conference on Unmanned/Unattended Sensors and Sensor Networks XI;and Advanced Free-Space Optical Communication Techniques and Applications

作者： Busset, Joel Perrodin, Florian Wellig, Peter Ott, Beat Heutschi, Kurt Ruehl, Torben Nussbaumer, Thomas Distran GmbH CH-8092 Zurich Switzerland Armasuisse Sci & Technol CH-3602 Thun Switzerland EMPA Swiss Fed Labs Mat Sci & Technol CH-8600 Dubendorf Switzerland RUAG CH-3602 Thun Switzerland

ISBN: (纸本)9781628418576

Recent events of drones flying over city centers, official buildings and nuclear installations stressed the growing threat of uncontrolled drone proliferation and the lack of real countermeasure. Indeed, detecting and tracking them can be difficult with traditional techniques. A system to acoustically detect and track small moving objects, such as drones or ground robots, using acoustic cameras is presented. The described sensor, is completely passive, and composed of a 120-element microphone array and a video camera. The acoustic imaging algorithm determines in real-time the sound power level coming from all directions, using the phase of the sound signals. A tracking algorithm is then able to follow the sound sources. Additionally, a beamforming algorithm selectively extracts the sound coming from each tracked sound source. This extracted sound signal can be used to identify sound signatures and determine the type of object. The described techniques can detect and track any object that produces noise (engines, propellers, tires, etc). It is a good complementary approach to more traditional techniques such as (i) optical and infrared cameras, for which the object may only represent few pixels and may be hidden by the blooming of a bright background, and (ii) radar or other echo-localization techniques, suffering from the weakness of the echo signal coming back to the sensor. The distance of detection depends on the type (frequency range) and volume of the noise emitted by the object, and on the background noise of the environment. detection range and resilience to background noise were tested in both, laboratory environments and outdoor conditions. It was determined that drones can be tracked up to 160 to 250 meters, depending on their type. Speech extraction was also experimentally investigated: the speech signal of a person being 80 to 100 meters away can be captured with acceptable speech intelligibility.

关键词： Cameras Acoustics Unmanned aerial vehicles Imaging systems detection and tracking algorithms Phased arrays Sensors Video Spherical lenses Clocks

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Fuzzy logic recursive change detection for tracking and denoising of video sequences

Fuzzy logic recursive change detection for tracking and deno...

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Conference on Image and Video Communications and Processing 2005

作者： Zlokolica, V De Geyter, M Schulte, S Pizurica, A Philips, W Kerre, E Univ Ghent Dept Telecommun & Informat Proc IPI B-9000 Ghent Belgium

ISBN: (纸本)0819456586

In this paper we propose a fuzzy logic recursive scheme for motion detection and temporal filtering that can deal with the Gaussian noise and unsteady illumination conditions both in temporal and spatial direction. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection in order to determine the degree of motion confidence. The proposed motion detector combines the membership degree appropriately using defined fuzzy rules, where the membership degree of motion for each pixel in a 2D-sliding-window is determined by the proposed membership function. Both fuzzy membership function and fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We perform simultaneously tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence with respect to the existence of motion. Finally, temporally filtered frames are further processed by the proposed spatial filter in order to obtain denoised image sequence. The main contribution of this paper is the robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: robustness to noise and changing illumination conditions and motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques.

关键词： Digital filtering Fuzzy logic Motion detection Video Denoising Sensors Video surveillance detection and tracking algorithms Spatial filters Electronic filtering

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SHADOW-tracking ALGORITHM FOR MOVING TARGET detection

SHADOW-TRACKING ALGORITHM FOR MOVING TARGET DETECTION

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CONF ON MILLIMETER WAVE AND SYNTHETIC APERTURE RADAR

作者： CARRARA, W ROUSSI, C WERNESS, S Environmental Research Institute of Michigan (United States)

ISBN: (纸本)0819401374

An algorithm to detect the presence of ground-moving targets in synthetic aperture radar (SAR) images has been investigated. Its performance is independent of the direction of the target velocity. Called the notch algorithm for its method of processing, it makes use of the shadow characteristics of a target moving over a clutter background in a unique way. While the image of a moving target may be displaced from its true position in a SAR image, the shadow cast by that target will not be shifted, although it will be de-focused and difficult to detect with conventional SAR processing. The notch algorithm attempts to find the target's ground-track and estimate its velocity vector using a single, coherent aperture of radar data. This information is critical to finding the moving target in the image of the scene and to indicating phase compensations required to begin to re-focus the target. Ground-track information can be obtained by applying the notch algorithm to the same data set several times while varying the algorithm parameters, followed by a comparison of the resulting images with the conventionally processed image. In this paper we describe the basis for the algorithm and present simulation results showing the performance over a range of target velocities. We also suggest several methods to improve detection.

关键词： detection and tracking algorithms Synthetic aperture radar Target detection Image processing Extremely high frequency Radar Computer simulations Fourier transforms Phase compensation Signal processing

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Order of battle change detection, fusion, and tracking

Order of battle change detection, fusion, and tracking

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

作者： Carlotto, MJ Nebrich, MA Crary, D Veridian Syst Arlington VA 22209 USA

ISBN: (纸本)0819444790

The automatic detection of significant changes in imagery is important in a number of intelligence, surveillance, and reconnaissance (ISR) tasks. An automated capability known as the Order of Battle Change Fusion (OBCF) system is described for detecting, fusing, and tracking changes over time in multisensor imagery. OBCF uses multiple change detection algorithms to exploit different aspects of change in multi-sensor images, normalcy models that provide a physical basis for detecting change and estimating the performance of change detection algorithms, algorithm fusion to combine the results from multiple change detection algorithms in order to enhance and maintain performance over changing operating conditions, and stationary tracking to provide a seamless history of image changes over time across different sensing modalities. Preliminary experimental results using electro-optical (EO) and synthetic aperture radar (SAR) imagery are presented.

关键词： Image fusion detection and tracking algorithms Synthetic aperture radar Sensors Fractal analysis Argon Error analysis Performance modeling Electro optical modeling Intelligence systems

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FAST ANALOG ARCHITECTURE FOR HIGH-ORDER CURVE RECOGNITION

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JOURNAL OF ELECTRONIC IMAGING 1995年第2期4卷 114-122页

作者： INIGO, RM ARRUE, BC UNIV SEVILLA ESCUELA SUPER INGNDEPT INGN SIST & AUTOMATE-41012 SEVILLESPAIN

An original method of higher order curve detection using the straight-line Hough transform (HT) is presented. This scheme is possible because curves in an image can be approximated by small straight-line segments. By combining the straight-line HT mapping scheme with an associative memory (AM), the method can discriminate and identify between different curves. Curves such as circles, ellipses, and parabolas have been studied. This method is computationally very efficient and may yield to hardware implementation, thus making it possible to use the HT in last real-time applications. The system consists of a parallel analog mapping from image plane to parameter plane and an AM pattern classifier.

关键词： Analog electronics Image segmentation Content addressable memory Neural networks Image processing Quantization detection and tracking algorithms Hough transforms Image resolution Pattern recognition

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Psychophysical experiments on the PicHunter image retrieval system

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JOURNAL OF ELECTRONIC IMAGING 2001年第1期10卷 170-180页

作者： Papathomas, TV Cox, IJ Yianilos, PN Miller, TL Minka, TP Conway, TE Ghosn, J NEC Res Inst Piscataway NJ 08854 USA Rutgers State Univ Lab Vis & Res Piscataway NJ 08854 USA Univ Montreal Dept Informat & Res Operat Montreal PQ H3C 3J7 Canada

Psychophysical experiments were conducted on PicHunter, a content-based image retrieval (CBIR) experimental prototype with the following properties: (1) Based on a model of how users respond, it uses Bayes's rule to predict what target users want, given their actions. (2) It possesses an extremely simple user interface. (3) It employs an entropy-based scheme to improve convergence. (4) It introduces a paradigm for assessing the performance of CBIR systems. Experiments 1-3 studied human judgment of image similarity to obtain data for the model. Experiment 4 studied the importance of using: (a) semantic information, (b) memory of earlier input and (c) relative and absolute judgments of similarity. Experiment 5 tested an approach that we propose for comparing performances of CBIR systems objectively. Finally, experiment 6 evaluated the most informative display-updating scheme that is based on entropy minimization, and confirmed earlier simulation results. These experiments represent one of the first attempts to quantify CBIR performance based on psychophysical studies, and they provide valuable data for improving CBIR algorithms. Even though they were designed with PicHunter in mind, their results can be applied to any CBIR system and, more generally, to any system that involves judgment of image similarity by humans. (C) 2001 SPIE and IS&T.

关键词： Image retrieval Databases Image enhancement Data modeling detection and tracking algorithms Performance modeling Digital imaging Visualization Human-machine interfaces Image processing

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Multiresolution, dynamic, and adaptive image quantization methodology: automation and analysis

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JOURNAL OF ELECTRONIC IMAGING 2003年第2期12卷 229-243页

作者： Soh, LK Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

We describe a multiresolution, dynamic, and adaptive image quantization methodology with automation being the goal of our research. To improve the robustness of the approach, we incorporate dynamic local thresholding and multiresolution peak detection. The first strategy extracts bisector values from local regions of the image and builds a histogram based on those values. The second strategy maps, the derived histogram into multiple levels of resolution, allowing peaks be scored for their significance and localized. We conduct several. experiments to analyze different versions of our quantization methodology and to compare it with the equal probability quantization. We also investigated the relationships between image attributes and the key parameters in our quantizers: Based on the findings, we developed a fully automated quantizer called QTR0.5. We have applied QTR0.5 to a variety of images-aerial, photographic, and satellite images-and have also used it as a preprocessor in an image segmentation software tool. (C) 2003 SPIE and IST.

关键词： Quantization Signal detection Tolerancing Image segmentation detection and tracking algorithms Image quality Buildings Calibration Satellite imaging Satellites

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