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

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PATTERN-RECOGNITION BY OPTICAL NEURAL-NETWORK-BASED ON THE OPTICAL CORRELATOR

PATTERN-RECOGNITION BY OPTICAL NEURAL-NETWORK-BASED ON THE O...

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International Conference on Holography, Correlation Optics, and Recording Materials

作者： PAVLOV, AV SHUBNIKOW, EI SI VAVILOV STATE OPT INST RES CTROPT PATTERN RECOGNIT & NEURAL NETWORKS LABST PETERSBURGRUSSIA

ISBN: (纸本)0819413992

The possibilities of the optical neural network formed by placing the optical correlator in a linear resonator to realize the vectors comparison and image preparation are discussed. The adaptive neural network model to pattern recognize on the complex background is proposed. The optical implementation of that NN model is discussed.

关键词： Neurons Optical correlators Neural networks Content addressable memory Image processing Optical pattern recognition Holography Pattern recognition detection and tracking algorithms Optical filters

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CLUSTERING AND COMPRESSION OF HIGH-DIMENSIONAL SENSOR DATA 2

CLUSTERING AND COMPRESSION OF HIGH-DIMENSIONAL SENSOR DATA

引用

CONF ON SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION 2

作者： HERMANN, DJ AHALT, SC MITCHELL, RA Battelle Memorial Institute (United States) The Ohio State Univ. (United States) Air Force Wright Lab. (United States)

ISBN: (纸本)0819411914

We investigate the compression of high-dimensional sensor data using vector quantization. Two metrics are presented for compression with the frequency sensitive competitive learning (FSCL) vector quantization (VQ) algorithm, and several indices of partitional validity are used to analyze the resulting VQ codebook clusters. Cluster analysis is used to determine the compressibility of the data. The results of this cluster analysis will help determine the effect of data compression on the performance of a target recognition system.

关键词： Mahalanobis distance Distortion Solids Databases Quantization detection and tracking algorithms Sensors Target recognition Data compression Data modeling

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Multilevel test and validation of algorithms implemented in a SOPC VisionNode

Multilevel test and validation of algorithms implemented in ...

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Conference on Intelligent Robots and Computer Vision XXI

作者： Sluiter, A Hartmann, K Hasenmaier, B Rost, M Stieler, D Weihs, W Univ Siegen Ctr Sensor Syst D-57076 Siegen Germany

ISBN: (纸本)081945155X

This paper describes results of a tool development process and still on-going research program in multilevel algorithm design and validation for multisensor systems. Our work covers both the algorithm design process including the simulation efforts and the implementation of the algorithms into the sensor node prepared for real-time processing within a vision system network. The sensor node hardware is based on the System on Programmable Chip (SOPC)-Technology. This gives us the flexibility to interface different kinds of sensor elements (matrix-, line-sensors) and the processing-power to provide real-time possibilities in height data-rate applications. At the point today our hardware module for Vision applications also uses a CPU module. This results in a high flexibility concerning the communication efforts. Our design can provide the use of CPU-modules within the SOPC design also. Mapping algorithms into a distributed sensor network will be done in either a centralized or decentralized way. That means the algorithm will be running on one sensor node or a part of the algorithm is implemented on some others nodes within the network. Beginning at the design and simulation level different kind of levels are opened for optimization, test and validate the developed algorithm. We present the architectural framework that supports the multilevel test and validation process.

关键词： Algorithm development Simulink Sensors Field programmable gate arrays detection and tracking algorithms MATLAB Computer architecture Computer simulations Data communications Image processing

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EFFECT OF CROSS-CORRELATION ON TRACK-TO-TRACK FUSION

EFFECT OF CROSS-CORRELATION ON TRACK-TO-TRACK FUSION

引用

Conference on Signal and Data Processing of Small Targets 1994

作者： SAHA, RK MITRE CORP BEDFORDMA 01730

ISBN: (纸本)0819415391

Since the advent of target tracking systems employing a diverse mixture of sensors, there has been increasing recognition by air defense system planners and other military system analysts of the need to integrate these tracks so that a clear air picture can be obtained in a command center. A popular methodology to achieve this goal is to perform track-to-track fusion, which performs track-to-track association as well as kinematic state vector fusion. This paper seeks to answer analytically the extent of improvement achievable by means of kinetic state vector fusion when the tracks are obtained from dissimilar sensors (e.g., Radar/ESM/IRST/IFF). It is well known that evaluation of the performance of state vector fusion algorithms at steady state must take into account the effects of cross-correlation between eligible tracks introduced by the input noise which, unfortunately, is often neglected because of added computational complexity. In this paper, an expression for the steady-state cross-covariance matrix for a 2D state vector track-to-track fusion is obtained. This matrix is shown to be a function of the parameters of the Kalman filters associated with the candidate tracks being fused. Conditions for positive definiteness of the cross-covariance matrix have been derived and the effect of positive definiteness on performance of track-to-track fusion is also discussed.

关键词： Sensors Filtering (signal processing) Matrices Kinematics detection and tracking algorithms Chemical elements Electronic filtering Algorithm development Data fusion Sensor fusion

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Performance of eight cluster validity indices on hyperspectral data

Performance of eight cluster validity indices on hyperspectr...

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Conference onalgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X

作者： Fontán, FM Jiménez, LO Lockheed Martin Missiles & Fire Control Orlando FL 32819 USA

ISBN: (纸本)081945348X

This paper evaluates the performance of 5 previously presented in the literature cluster validity indices for the Fuzzy C-Means (FCM) clustering algorithm. The first two indices, the Fuzzy Partition Coefficient (PC), Fuzzy Partition Entropy Coefficient (PEC) select the number of clusters for which the fuzzy partition is more "crisp-like" or less fuzzy. The other three indices are the Fuzzy Davies-Bouldin Index (FDB), Xie-Beni Index (XB), and the Index I (I) choose the number of clusters which maximizes the inter-cluster separation and minimizes the within cluster scatter. A modification to these three indices is proposed based on the Bhattacharyya distance between clusters. The results show that this modification improves upon the performance of Index I. On the data sets presented on this paper the modifications of indices FDB and XB performed adequately.

关键词： Fuzzy logic Distance measurement Matrices Silicon Hyperspectral imaging Sensors Data centers detection and tracking algorithms Image sensors Missiles

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Adaptive compression of still images: Automating the choice of algorithm and parameters

Adaptive compression of still images: Automating the choice ...

引用

EUROPTO Conference on Electronic Imaging - Processing, Printing, and Publishing in Color

作者： Gilbert, F Moutarde, F Alcatel Alsthom Rech Route Nozay F-91460 Marcoussis France

ISBN: (纸本)0819428612

This paper presents a new adaptive compression scheme for continuous-tone still images with two main benefits: 1 degrees/ it allows to take advantage of a set of algorithms (such as JPEG, wavelets, fractals,...) by switching between them according to the image content and target compression ratio;2 degrees/ it tunes automatically the algorithm-specific parameters (e.g. JPEG quantization factor) in order to meet the user compression objective (e.g. a target compression ratio). A performance evaluation test indicates the following: 1 degrees/ our adaptive coder can succeed with probability approximate to 72% in selecting automatically the best technique (among JPEG, a wavelet algorithm, and a fractal one);2 degrees/ the difference between the obtained compression ratio and the target is lower than 10%;3 degrees/the computing overhead due to algorithm selection and parameter choice is negligible. This adaptive compression technique has been proposed to the ISO standardisation committee currently working on the definition of the future "JPEG-2000" standard.

关键词： Image compression detection and tracking algorithms Wavelets Iterated function systems Calibration Fractal analysis Image quality Standards development Image quality standards Switching

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Multi-class open set recognition for SAR imagery 26

Multi-class open set recognition for SAR imagery

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Conference on Automatic Target Recognition XXVI

作者： Scherreik, Matthew Rigling, Brian Wright State Univ Dept Elect Engn Dayton OH 45435 USA

ISBN: (纸本)9781510600850

Supervised multi-class target recognition algorithms label an input pattern according to the most similar training class. Typically, the number of training classes is fixed and known a priori. In practice, however, a classifier may encounter novel targets that were not seen in training and label them incorrectly. Recent work in open set recognition (OSR) develops classifiers that can identify training targets as well as previously unknown targets. This results in a reduced number of forced misclassifications by ''rejecting" targets that were not present in training. Several OSR algorithms are based on support vector machines (SVMs), namely, the 1-vs-set machine, W-SVM, and POS-SVM. The 1-vs-set machine, a linear classifier, forms a ''slab" around each training class to discriminate it from the remaining training classes and limit the risk of labeling open space as target space. The W-SVM uses a novel dual-calibration technique to map the SVM outputs to posterior probabilities, which are then subjected to a pair of user-specified thresholds. The POS-SVM relies on a single calibration step, but features data-driven posterior probability thresholds that are chosen automatically. Both the W-SVM and POS-SVM have the capability to use nonlinear SVM kernel functions and perform particularly well with the popular Gaussian RBF kernel. Past works have shown that these algorithms can be effective for classifying ladar and IR images with a rejection option. In this paper, we apply these algorithms to the MSTAR SAR dataset and analyze their performance for classifying known targets and rejecting unknown targets in the presence of clutter.

关键词： detection and tracking algorithms Calibration Synthetic aperture radar Target recognition Automatic target recognition Binary data Data modeling Picosecond phenomena Infrared imaging LIDAR

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Steganalysis using color wavelet statistics and one-class support vector machines

Steganalysis using color wavelet statistics and one-class su...

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6th Conference on Watermarking of Multimedia Contents

作者： Lyu, S Farid, H Dartmouth Coll Dept Comp Sci Hanover NH 03755 USA

ISBN: (纸本)0819452092

Steganographic messages can be embedded into digital images in ways that are imperceptible to the human eve. These messages, however, alter the underlying statistics of an image. We previously built statistical models using first-and higher-order wavelet statistics, and employed a non-linear support vector machines (SVM) to detect steganographic messages. In this paper we extend these results to exploit color statistics, and show how a one-class SVM greatly simplifies the training stage of the classifier.

关键词： Statistical analysis Error analysis Steganalysis Wavelets Databases detection and tracking algorithms Image compression Image classification Steganography Eye

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Fast antibiotic susceptibility testing via holographic imaging

Fast antibiotic susceptibility testing via holographic imagi...

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European Conferences on Biomedical Optics - Translational Biophotonics - Diagnostics and Therapeutics

作者： Sedaghat, Zohreh Degout-Charmette, Elodie El Azami, Meriem Rol, Fabian Mahe, Pierre Chareire, Anne-Coline Nouvelli-Rousseau, Armelle Champion, Thibault Franceschi, Christine Mallard, Frederic Josso, Quentin Bioaster 40 Ave Tony Garnier F-69007 Lyon France bioMerieux 376 Chemin Orme F-69280 Marcy Letoile France CEA 17 Rue Martyrs F-38054 Grenoble 9 France bioMerieux 3 Route Port Michaud F-38390 La Balme Les Grottes France bioMerieux R&D Microbiol 5 Rue Berges F-38024 Grenoble France

We proposed a phenotypic image-based AST via digital inline holographic microscopy (DIHM) and dedicated machine learning algorithms. The analysis relies on monitoring single bacteria during the kinetics of changes ind... 详细信息

ISBN: (数字)9781510647053

ISBN: (纸本)9781510647053;9781510647046

关键词： Bacteria Holography Digital holography Image segmentation Machine learning Holograms Reconstruction algorithms detection and tracking algorithms Image processing algorithms and systems Statistical analysis

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Evaluation Schemes for Video and Image Anomaly detection algorithms 26

Evaluation Schemes for Video and Image Anomaly Detection Alg...

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Conference on Automatic Target Recognition XXVI

作者： Parameswaran, Shibin Harguess, Josh Barngrover, Christopher Shafer, Scott Reese, Michael Space & Naval Warfare Syst Ctr Pacific 53560 Hull St San Diego CA 92152 USA

ISBN: (纸本)9781510600850

Video anomaly detection is a critical research area in computer vision. It is a natural first step before applying object recognition algorithms. There are many algorithms that detect anomalies (outliers) in videos and images that have been introduced in recent years. However, these algorithms behave and perform differently based on differences in domains and tasks to which they are subjected. In order to better understand the strengths and weaknesses of outlier algorithms and their applicability in a particular domain/task of interest, it is important to measure and quantify their performance using appropriate evaluation metrics. There are many evaluation metrics that have been used in the literature such as precision curves, precision-recall curves, and receiver operating characteristic (ROC) curves. In order to construct these different metrics, it is also important to choose an appropriate evaluation scheme that decides when a proposed detection is considered a true or a false detection. Choosing the right evaluation metric and the right scheme is very critical since the choice can introduce positive or negative bias in the measuring criterion and may favor (or work against) a particular algorithm or task. In this paper, we review evaluation metrics and popular evaluation schemes that are used to measure the performance of anomaly detection algorithms on videos and imagery with one or more anomalies. We analyze the biases introduced by these by measuring the performance of an existing anomaly detection algorithm.

关键词： detection and tracking algorithms Video Sensors Computer vision technology Machine vision Binary data Video surveillance Receivers Analytical research Object recognition

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