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

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GENETIC SEARCH OF A GENERALIZED HOUGH TRANSFORM SPACE

GENETIC SEARCH OF A GENERALIZED HOUGH TRANSFORM SPACE

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CONF ON ADAPTIVE AND LEARNING SYSTEMS

作者： MONTANA, DJ Bolt Beranek & Newman Inc. (United States)

ISBN: (纸本)0819408719

We use a Generalized Hough transform (GHT) to detect and track instances of a class of sonar signals. This class consists of a four-dimensional set of curves and hence requires a four- dimensional transform space for the GHT. Many of the signals we need to detect are very weak. Such signals yield peaks in the transform space which are both very narrow and not too far above the random background variations. Finding such peaks is difficult. Exhaustive search over a predetermined discretization of the transform space will yield a nearly optimal point for a sufficiently fine discretization. However, even with an intelligently chosen discretization, exhaustive search requires searching over (and hence evaluating) many points in the transform space. We have therefore developed a genetic algorithm to more efficiently search the transform space. Designing the genetic algorithm to work properly has required experimentation with a number of its parameters. The most important of these are (1) the representation, (2) the population size, and (3) the number of runs.

关键词： Genetic algorithms Signal detection Hough transforms Genetics Algorithm development detection and tracking algorithms Computer programming Image segmentation Binary data Signal to noise ratio

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CHARACTERIZATION OF NATURAL BACKGROUND CLUTTER FOR DESIGN OF CAMOUFLAGE

CHARACTERIZATION OF NATURAL BACKGROUND CLUTTER FOR DESIGN OF...

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CONF ON CHARACTERIZATION PROPAGATION, SIMULATION OF SOURCES AND BACKGROUNDS 2

作者： WALKER, GW MCMANAMEY, JR U.S. Army Belvoir Research Development and Engineering Ctr. (United States)

ISBN: (纸本)0819408522

Camouflage design and evaluation requires that the developer assess the detectability of a camouflaged target in a natural, cluttered environment. Traditional techniques have involved panels of observers, but modern camoufleurs are seeking a satisfactory means of making reasonable estimates by the use of computer modeling. However, no adequate target detection model exists at this time. Camouflage experts at Belvoir Research, Development, and Engineering Center (BRDEC) are seeking means of improving the best of the current target detection models to make it capable of serving their needs. This is an interest which they share with many others within the Army who find that the best available model is unable to cope with low-observable targets. Unmodeled effects are responsible for the shortcomings. Current work at BRDEC and other organizations within the Army is examining such diverse methods as the use of fractals, edge evaluation, gray level cooccurrence matrices, power spectral analysis, and correlation lengths.

关键词： Target detection Camouflage Computer simulations Systems modeling Fractal analysis Matrices detection and tracking algorithms Environmental sensing Image processing Visual process modeling

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MARVEL - A SYSTEM THAT RECOGNIZES WORLD LOCATIONS 6

MARVEL - A SYSTEM THAT RECOGNIZES WORLD LOCATIONS

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CONF ON MOBILE ROBOTS 6

作者： BRAUNEGG, DJ Artificial Intelligence Lab./MIT (United States)

ISBN: (纸本)081940750X

To use a world model, a mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, I present MARVEL, a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. MARVEL is designed to be robust with respect to input errors and to respond to a gradually changing world by updating its world location models. In over 1000 recognition tests using real-world data, MARVEL yielded a false negative rate under 10% with zero false positives. MARVEL also fits into a world modeling system under development.

关键词： Data modeling Visual process modeling Mobile robots Navigation systems Cameras Coastal modeling detection and tracking algorithms Stereo vision systems Databases Systems modeling

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STATISTICAL TECHNIQUES FOR NOISE REMOVAL FROM VISUAL IMAGES

STATISTICAL TECHNIQUES FOR NOISE REMOVAL FROM VISUAL IMAGES

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3RD CONF ON HYBRID IMAGE AND SIGNAL PROCESSING

作者： ALLRED, LG KELLY, GE Air Force Ogden Air Logistics Ctr. (United States) Air Force Ogden Air Logistics Ctr (United States)

ISBN: (纸本)0819408670

The median operator has been demonstrated to be a very effective method for restoring recognizable images from very noisy image data. The power of the median operator stems from its non-algebraic formulation, which prevents erroneous data corrupting the final color computation. A principal drawback is that the median operator replaces all data, erroneous or not, the result being a net loss of information. This paper presents alternative statistical outlier techniques by which erroneous data is readily recognized, but valid data usually remains unchanged. The result is an effective noise removal algorithm with reduced loss of information.

关键词： Cameras Signal processing Visualization Image processing Video detection and tracking algorithms Digital filtering Image enhancement Modulation transfer functions Optical filters

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RECENT ADVANCES ON TECHNIQUES AND THEORIES OF FEEDFORWARD NETWORKS WITH SUPERVISED LEARNING

RECENT ADVANCES ON TECHNIQUES AND THEORIES OF FEEDFORWARD NE...

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1ST CONF ON SCIENCE OF ARTIFICIAL NEURAL NETWORKS

作者： XU, L KLASA, S Concordia Univ. (Canada) Harvard Univ. (United States)

ISBN: (纸本)0819408751

The rediscovery and popularization of the back propagation training technique for multilayer perceptrons as well as the invention of the Boltzmann Machine learning algorithm has given a new boost to the study of supervised learning networks. In recent years, besides the widely spread applications and the various further improvements of the classical back propagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematical review on the recent advances on supervised learning techniques and theories for static feedforward networks. We summarize a great number of developments into four aspects: (1) Various improvements and variants made on the classical back propagation techniques for multilayer (static) perceptron nets, for speeding up training, avoiding local minima, increasing the generalization ability, as well as for many other interesting purposes. (2) A number of other learning methods for training multilayer (static) perceptron, such as derivative estimation by perturbation, direct weight update by perturbation, genetic algorithms, recursive least square estimate and extended Kalman filter, linear programming, the policy of fixing one layer while updating another, constructing networks by converting decision tree classifiers, and others. (3) Various other feedforward models which are also able to implement function approximation, probability density estimation and classification, including various models of basis function expansion (e.g., radial basis functions, restricted coulomb energy, multivariate adaptive regression splines, trigonometric and polynomial bases, projection pursuit, basis function tree, and may others), and several other supervised learning models. (4) Models with complex structures, e.g., modular architecture, hierarchy architecture, and others. (5) A number of theoretical issues involving the universal approximatio

关键词： Neural networks Machine learning Evolutionary algorithms Genetic algorithms Artificial neural networks detection and tracking algorithms algorithms Neurons Control systems Pattern recognition

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EARLY PERCEPTION AND STRUCTURAL IDENTITY - NEURAL IMPLEMENTATION 10

EARLY PERCEPTION AND STRUCTURAL IDENTITY - NEURAL IMPLEMENTA...

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CONF ON INTELLIGENT ROBOTS AND COMPUTER VISION 10 : NEURAL, BIOLOGICAL, AND 3-D METHODS

作者： LIGOMENIDES, PA Univ. of Maryland and CAELUM Research Corp. (United States)

ISBN: (纸本)0819407453

It is suggested that there exists a minimal set of rules for the perceptual composition of the unending variety of spatio-temporal patterns in our perceptual world. Driven by perceptual discernment of "sudden change" and "unexpectedness", these rules specify conditions (such as co-linearity and virtual continuation) for perceptual grouping and for recursive compositions of perceptual "modalities" and "signatures". Beginning with a smallset of primitive perceptual elements, selected contextually at some relevant level of abstraction, perceptual compositions can graduate to an unlimited variety of spatiotemporal signatures, scenes and activities. Local discernible elements, often perceptually ambiguous by themselves, may be integrated into spatiotemporal compositions, which generate unambiguous perceptual separations between "figure" and "ground". The definition of computational algorithms for the effective instantiation of the rules of perceptual grouping remains a principal problem. In this paper we present our approach for solving the problem of perceptual recognition within the confines of one-D variational profiles. More specifically, concerning "early" (pre-attentive) recognition, we define the "structural identity of a k-norm, k ∈ K,"--SkID--as a tool for discerning and locating the instantiation of spatiotemporal objects or events. The SkID profile also serves a s a reference coordinate framework for the "perceptual focusing of attention" and the eventual assessment of resemblance. Neural network implementations of pre-attentive and attentive recognition are also discussed briefly. Our principles are exemplified by application to one-D perceptual profiles, which allows simplicity of definitions and of the rules of perceptual composition.

关键词： Image segmentation Sensors Robots Computer vision technology Machine vision Robot vision Visual process modeling Pattern recognition detection and tracking algorithms Neural networks

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RECOGNITION OF MOVING VEHICLES USING DIRECTIONAL-TEMPORAL PLANE TRANSFORM

RECOGNITION OF MOVING VEHICLES USING DIRECTIONAL-TEMPORAL PL...

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CONF ON INTELLIGENT ROBOTS AND COMPUTER VISION 10 : algorithms AND TECHNIQUES

作者： TANIGUCHI, H FURUSAWA, H SEKI, A IKEBATA, S Mitsubishi Electric Corp. (Japan)

ISBN: (纸本)0819407445

Traffic flow measurement systems, which recognize vehicles and measure traffic parameters by image processing, are researched. We propose a new recognition method for moving vehicles using directional-temporal plane transform (DTT) for the system. DTT is a transformation from spatiotemporal data to 2-D data onto a directional-temporal plane. The transformation is done by projecting feature data of vehicles to a directional axis which is roughly parallel to the moving loci, and by placing the projected data stream side by side in temporal order. We also show an effective extraction algorithm of vehicles from the 2-D data obtained by DTT. Experimental results using real images demonstrate the effectiveness of this method.

关键词： Image processing detection and tracking algorithms Robot vision Robots Machine vision Computer vision technology Cameras Edge detection Magnetic sensors Roads

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ADAPTIVE MULTISPECTRAL IMAGE-PROCESSING FOR THE detection OF TARGETS IN TERRAIN CLUTTER

ADAPTIVE MULTISPECTRAL IMAGE-PROCESSING FOR THE DETECTION OF...

引用

4TH CONF ON THE SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1992

作者： HOFF, LE ZEIDLER, JR YERKES, CR Naval Command Control and Ocean Surveillance Ctr. (United States)

ISBN: (纸本)0819408638

In passive detection of small infrared targets in image data, we are faced with the difficult task of enhancing some characteristic of the target or signal while suppressing the clutter or background image noise. We reported that an effective means by which targets may be identified is to exploit characteristics which exist between scenes measured in different bands in the long wave infrared region of the electromagnetic spectrum. These methods are broadly termed multispectral techniques. In this paper we present a method by which a two- dimensional least-mean square adaptive filter is used to distinguish between target and clutter using multispectral techniques.

关键词： Target detection detection and tracking algorithms Image processing Signal processing Data processing Digital filtering Infrared imaging Infrared radiation Error analysis Linear filtering

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REAL-TIME ANALYSIS OF SMALL OR LOW-CONTRAST OBJECTS

REAL-TIME ANALYSIS OF SMALL OR LOW-CONTRAST OBJECTS

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4TH CONF ON THE SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1992

作者： YOOL, SR SMEDLEY, KG VANVACTOR, D Lockheed Palo Alto Research Lab. (United States) Lockheed Missiles & Space Co. Inc. (United States)

ISBN: (纸本)0819408638

Multi-frame, multi-spectral image sequences can be exploited in real-time, giving analysts access to information on evolving strategic events or threats. Video-rate dynamic range adjustment subdues fluctuations in image intensity, thereby assisting discrimination of small or low-contrast objects. Contrast-regulated unsharp masking enhances differentially shadowed or otherwise low-contrast image regions. Real-time removal of localized hotspots may enhance resolution of adjacent objects. In image sequences contaminated by zero-mean noise, real-time frame averaging can assist resolution and location of small or low-contrast objects. Image sequences can be screened automatically for low-frequency, high-magnitude events.

关键词： Algorithm development Video Image enhancement Video surveillance Artillery Data processing Signal processing Sun Image processing detection and tracking algorithms

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ANALYSIS OF IMAGE REGISTRATION algorithms FOR INFRARED IMAGES

ANALYSIS OF IMAGE REGISTRATION ALGORITHMS FOR INFRARED IMAGE...

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CONF ON SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION

作者： VENKATESWARLU, R CHATTERJEE, BN Defence Research and Development Organisation (India) Indian Institute of Technology/Kharagpur (India)

ISBN: (纸本)0819408646

Image registration techniques have gained importance for many applications such as area correlation tracking, handing over recognized target scenes from one sensor to another sensor, tracking an area of interest before detection, tracking point targets, and the latest being multi- target handling capability for defense needs, scene stabilization, etc. No single image registration algorithm (IRA) can work satisfactorily for all applications and in all environments. This paper analyzes the suitability of image registration algorithms for infrared images. Infrared images are characterized by low contrast and sensor nonuniformities such as offset-errors, gain variations resulting in fixed pattern noise (FPN). Particularly, in focal plane arrays (FPA), the output of each detector is characterized by `ax + b' where `a' and `b' are gain and dc offset terms respectively, and `x' is photon flux level falling on the detectors. These parameters and especially their variations from detector element to detector element effect the performance of image registrations algorithms. In this paper, the basic IRAs are analyzed in this context of infrared images with low contrast and FPN. Simulated results using real world images are presented. Novel and inexpensive confidence and redundancy measures have been proposed to improve the performance by detecting misregistrations.

关键词： Image registration Sensors Signal to noise ratio Simulation of CCA and DLA aggregates Infrared imaging Infrared radiation Staring arrays detection and tracking algorithms Curium Image sensors

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