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

共 1721 条记录，以下是1511-1520 订阅

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OPTICAL-FEATURE EXTRACTION USING THE RADON-TRANSFORM AND ANGULAR-CORRELATION 4

OPTICAL-FEATURE EXTRACTION USING THE RADON-TRANSFORM AND ANG...

引用

CONF ON OPTICAL INFORMATION PROCESSING SYSTEMS AND ARCHITECTURE 4

作者： SHUKLA, OB BOONE, BG Johns Hopkins Univ. (United States)

ISBN: (纸本)0819409456

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm, which extracts object length, width, area, aspect ratio, orientation and boundary from suitably thresholded images. In addition to being insensitive to scale and rotation, these simulations indicate that the features derived from angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. We will discussed these architectures, stressing the micro-optical approach. Test and evaluation using simple synthetic object data will be described. We will also describe the results of a study that uses object boundary (derivable from angular correlation) to classify objects using a neural network.

关键词： Video Hough transforms Optical correlators Radon transform Neural networks detection and tracking algorithms Sensors Matrices Detector arrays Radon

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EXTRACTION OF 2D GROUPINGS FOR 3D OBJECT RECOGNITION 3

EXTRACTION OF 2D GROUPINGS FOR 3D OBJECT RECOGNITION

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Conference on Visual Information Processing III

作者： ARSENEAULT, JL BERGEVIN, R LAURENDEAU, D UNIV LAVAL DEPT ELECT ENGNCOMP VIS & SYST LABQUEBEC CITY G1K 7P4QUEBECCANADA

ISBN: (纸本)081941543X

An approach for the recognition of 3D objects from single 2D views is presented. Using perceptual organization, hierarchies of features based on parallelism, co-linearity, and intersection are generated. Our local grouping algorithm is particularly inspired by the formalism defined by Etamadi et al., which is concerned with the formation of self-consistent groupings of straight lines from which all higher level groupings may be derived. Our approach extends this formalism to circular arcs. Surfaces in the scene are then extracted based on perceptual laws of symmetry and closure. The recognition process uses relational graphs of surfaces constructed by establishing the proximity, adjacency, and inclusion relations that exist between the surfaces. We identify closures which can be interpreted as the borders of the visible surfaces of objects and can also be used to describe the 2D shape of the surfaces. We show that a graph can be constructed from the relations between these closures and that similarities can be extracted from two different graphs obtained by analyzing two views of the same scene. Typical results obtained for complex indoor scenes are presented.

关键词： Image segmentation detection and tracking algorithms Object recognition Algorithm development Visual information processing Lutetium Image processing algorithms and systems Computer vision technology Feature extraction Machine vision

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MULTISTAGE, HIGHER-ORDER SDF FILTERS

MULTISTAGE, HIGHER-ORDER SDF FILTERS

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CONF ON HYBRID IMAGE AND SIGNAL PROCESSING 2

作者： BRASHER, JD HESTER, CF LAWSON, DW SIMS, R Teledyne Brown Engineering (United States) U.S. Army Aviation and Missile Command (United States)

ISBN: (纸本)0819403482

A goal-driven approach to the design of SDF correlation filters is described. It is shown that a variety of filters, each designed for a specific goal or result, are derived from a common generic form which is a multi-stage extension of the conventional SDF filter. The SDF construction affords many degrees of freedom which can be exploited to effect a specialized result.

关键词： Filtering (signal processing) detection and tracking algorithms Image filtering Electronic filtering Signal processing Interference (communication) Critical dimension metrology Linear filtering Target detection Missiles

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CLUSTERING WITH UNSUPERVISED LEARNING NEURAL NETWORKS - A COMPARATIVE-STUDY 4

CLUSTERING WITH UNSUPERVISED LEARNING NEURAL NETWORKS - A CO...

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4th International Conference on Applications of Artificial Neural Networks

作者： WANN, CD THOMOPOULOS, SCA PENN STATE UNIV DEPT ELECT & COMP ENGNDECIS & CONTROL SYST LABUNIV PKPA 16802

ISBN: (纸本)0819412015

A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ART2 and DIGNET on data clustering and pattern recognition problems with noise or interference is investigated by computer simulations. It is shown that DIGNET generally has faster learning and better clustering performance on the statistical pattern recognition problems. DIGNET has a simpler architecture, and the system parameters can be analytically determined from the self- organizing process. The threshold value used in DIGNET can be specifically determined from a given lower bound on the desirable signal-to-noise ratio (SNR). A modified model based on the features of ART2 and DIGNET is also derived and investigated. The simpler architecture combines the ART2 structure with the advantages of DIGNET model. The concepts of well depth and stage age originally introduced in DIGNET are applied in the modified model. The modified model preserves the features of noise suppression, contrast enhancement and self- organizing stable pattern recognition of ART2, yet provides a specific method to adjust parameters in the network. The network performs a variant of K-means learning, but without the knowledge of a priori information on the actual number of clusters. The networks discussed in this paper are applied and benchmarked against clustering and pattern recognition problems. Comparative simulation results of the networks are also presented.

关键词： Signal to noise ratio Performance modeling Systems modeling Artificial neural networks Pattern recognition Computer simulations Machine learning Image classification Neural networks detection and tracking algorithms

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Consequences of preprocessing feature data for support vector machines

Consequences of preprocessing feature data for support vecto...

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11th Conference on Automatic Target Recognition

作者： Gorsich, DJ Karlsen, RE Gerhart, GR USA Tank Automot & Armaments Command AMSTA TR R Warren MI 48397 USA

ISBN: (纸本)0819440744

Support vector machines are classification algorithms based on quadratic programming that have been found to give excellent classification results on problems such as discriminating targets from backgrounds. A key capability of these algorithms is that they do not require a preprocessing step to determine feature vectors, yet preprocessing is still an important step in the classification process. We discuss the effects of preprocessing feature data on the support vectors and the classification results of support vector machines. We first give a short introduction to support vector machines. Several methods to preprocess the data before being sent to the support vector machine are discussed. Then the algorithm is applied a set of second-order stochastic textures defined by their covariance structure. The effect on the classification rate is then determined.

关键词： Image classification Stochastic processes Matrices Computer programming detection and tracking algorithms Linear filtering Nonlinear filtering Computer simulations Feature extraction MATLAB

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New directions in the development of the ORASIS algorithm

New directions in the development of the ORASIS algorithm

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Conference on Imaging Spectrometry IX

作者： Bowles, J Chen, W Gillis, D USN Res Lab Remote Sensing Div Washington DC 20375 USA

ISBN: (纸本)0819450324

The Optical Real-time Adaptive Spectral Identification System (ORASIS) continues to be developed by the Naval Research Laboratory. Recently, new methods to compress hyperspectral data using ORASIS in combination with wavelets have been demonstrated. Variations on this approach continue to be pursued and will be discussed. In addition, a new method to perform target identification based on Spectral Angle Mapper (SAM) has been demonstrated. When projected into a reduced dimension space, angles between spectra are not conserved. Proper determination of the dimensions and coordinates of the subspace can result in maximizing the angles between a spectrum of interest and the general background. This approach can be used for target identification where the "target" can be a rare spectrum or something found in the general background. Recent improvements to these methods, as well as new applications of the software, are discussed.

关键词： Algorithm development Wavelets detection and tracking algorithms Target detection Error analysis RGB color model Sensors Signal to noise ratio Target recognition Analytical research

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Assuring Autonomy 4

Assuring Autonomy

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Conference on Disruptive Technologies in Information Sciences IV

作者： Bharadwaj, Ramesh Naval Res Lab Ctr High Assurance Comp Syst 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9781510636163

Autonomous systems, including self-driving cars and air vehicles, have caught the imagination of the press and the public. However, broader adoption of such systems in safety-critical applications has been the subject of intense debate and scrutiny. The stunning performance of deep learners compared to extant methods, including pattern matching, statistical methods, and legacy machine learning algorithms, has taken the research world by storm. This has naturally led the DoD community to ask the question: "How do we harness this technology being unleashed upon the world?" Before we answer this question, however, it is important to note that trust is integral to DoD applications, including autonomous systems, and ensuring reliable system operations is paramount. Therefore, we need strategies that harness deep learning algorithms to provide the DoD with autonomous systems that are robust, secure, timely and dependable.

关键词： Data modeling Neural networks Neurons Machine learning Computing systems Visualization detection and tracking algorithms

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POLARIMETRIC TECHNIQUES FOR ENHANCING SAR IMAGERY

POLARIMETRIC TECHNIQUES FOR ENHANCING SAR IMAGERY

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CONF ON SYNTHETIC APERTURE RADAR

作者： VERBOUT, SM NETISHEN, CM NOVAK, LM Lincoln Lab./MIT (United States)

ISBN: (纸本)0819407763

This paper describes several techniques for enhancing fully polarimetric, high-resolution synthetic aperture radar (SAR) imagery. Image enhancement is viewed as a pre-processing step to prepare the SAR data for sophisticated detection, discrimination, and classification algorithms. The paper considers enhancement techniques that use polarimetric information in the imagery to achieve one or both of the following goals: (1) reduction of image speckle, and (2) improvement in target-to-clutter contrast. Three enhancement techniques are presented: the polarimetric whitening filter (PWF), the polarimetric enhancement filter (PEF), and the polarimetric matched filter (PMF). These polarimetric processing techniques are applied to actual SAR data gathered by the Lincoln Laboratory MMW airborne sensor. The resulting target and clutter statistics are compared for typical data sets at both 1ft by 1ft resolution and lm by lm resolution.

关键词： Polarimetry Synthetic aperture radar Image enhancement detection and tracking algorithms Extremely high frequency Image classification Sensors Speckle

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Correlation filters for object tracking target re-acquisition and smart aimpoint selection

Correlation filters for object tracking target re-acquisitio...

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Conference on Optical Pattern Recognition VIII

作者： Mahalanobis, A Hughes Missile Systems Co. (United States)

ISBN: (纸本)0819424889

Correlation filters designed for automatic target recognition can be used for assisting trackers. We show that a tracker may use the filters for independent verification of the target as necessary. In particular, a form of the filters known as unconstrained correlation filters is shown to be optimal from a detection standpoint. An example is given to illustrate the concept of target reacquisition when the tracker's lock is broken.

关键词： Image filtering Distortion detection and tracking algorithms Electronic filtering Target detection Tolerancing Automatic target recognition Image processing Automatic tracking Composites

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Compression-based steganalysis of LSB embedded images

Compression-based steganalysis of LSB embedded images

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Conference on Security, Steganography and Watermarking of Multimedia Contents VIII

作者： Boncelet, C Marvel, L Raglin, A Univ Delaware Newark DE 19716 USA

ISBN: (纸本)0819461121

We present a new method of steganalysis, the detection of hidden messages, for least significant bits (LSB) replacement embedding. The method uses lossless image compression algorithms to model images bitplane by bitplane. The basic premise is that messages hidden by replacing LSBs of image pixels do not possess the same statistical properties and are therefore likely to be incompressible by compressors designed for images. In fact, the hidden data are usually compressed files themselves that may or may not be encrypted. In either case, the hidden messages are incompressible. In this work, we study three image compressors, one a standard and two we developed. The results are that many images can be eliminated as having possible steganographic content since the LSBs compress more than a hidden message typically would.

关键词： Image compression Steganalysis Steganography Bridges detection and tracking algorithms Algorithm development Analytical research Image filtering Image quality standards Standards development

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