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

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Hopfield neural network for qualitative recognition of object motion based on optical flow

Hopfield neural network for qualitative recognition of objec...

引用

Signal Processing, Sensor Fusion, and Target Recognition III

作者： Convertino, Gabriella Brattoli, Maddalena Distante, Arcangelo Istituto Elaborazione Segnali ed Immagini/CNR (Italy)

ISBN: (纸本)0819415367

A method for the recognition of moving objects from a sequence of time-varying images is presented. The method consists of two phases: an estimation phase of optical flow field and an interpretation phase where a qualitative analysis of optical flow patterns is performed. The two phases interact each other in order to provide a final map in which areas of the image interested by the same motion are isolated and classified. For the estimation phase a gradient-based approach has been selected, that provides a linear optical flow map. In the interpretation phase the optical flow field is regarded as a 2D linear system of differential equations and then the geometric theory of differential equations is used. The whole algorithm is implemented by means of an Hopfield neural network. Experimental results on synthetic images are given.

关键词： Optical flow Neural networks Object recognition Differential equations detection and tracking algorithms Evolutionary algorithms

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Simulated annealing algorithm for radar cross-section estimation and segmentation

Simulated annealing algorithm for radar cross-section estima...

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Applications of Artificial Neural Networks V

作者： White, Richard G. Defence Research Agency Malvern (United Kingdom)

ISBN: (纸本)0819415472

We present here an algorithm which performs radar cross-section estimation by using techniques based on simulated annealing. Standard simulated annealing approaches to image restoration attempt to categorize each image element as belonging to one of a small number of predefined image states or values. This is restrictive for tasks such as radar cross-section estimation and we present here an algorithm which is capable of producing a real-valued output. This is achieved by introducing an edge detection stage into the simulated annealing process. The action of the annealing algorithm may be viewed as a filter which adapts to local image structure. We present results which demonstrate this behavior and in so doing allow us to estimate the residual noise levels we might expect.

关键词： Radar Image processing algorithms and systems algorithms Image segmentation Edge detection detection and tracking algorithms Image filtering Statistical analysis Speckle Annealing

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Chromato-structural approach toward surface defect detection in random textured images

Chromato-structural approach toward surface defect detection...

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Machine Vision Applications in Industrial Inspection II

作者： Song, K.Y. Kittler, J. Petrou, Maria Ng, I. Univ. of Surrey (United Kingdom)

ISBN: (纸本)0819414786

Machine vision and automatic surface inspection has been an active field of research during the last few years. However, very little research has been contributed to the area of defect detection in textured images, especially for the case of random textures. In this paper, we propose a novel algorithm that uses color and texture information to solve the problem. A new color clustering scheme based on human color perception is developed. No a priori knowledge regarding the actual number of colors associated with the color image is required. With this algorithm, very promising results are obtained on defect detection in random textured images and in particular, granite images.

关键词： Colorimetry RGB color model Defect detection Binary data Image segmentation detection and tracking algorithms Inspection Feature extraction Electroluminescence Image processing

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New approach for locating multiple targets

New approach for locating multiple targets

引用

Signal Processing, Sensor Fusion, and Target Recognition III

作者： Bao, Zhonghao Flachs, Gerald M. New Mexico State Univ. (United States)

ISBN: (纸本)0819415367

The research presented in this paper is focused on the development of mathematical foundations and algorithm for locating multiple targets on film images. A locator algorithm with firm mathematical foundations has been developed to locate multiple targets. The algorithm is based upon the assumption that an image is composed of a mixture of component distributions, stemming from the different background and target regions in the image. Using both a maximum likelihood estimator and iterative clustering algorithm, a locator algorithm was developed to separate the mixture into its major components. Having the components of the mixture, objects are located by classifying the components of mixture and recognizing the targets based upon their size and shape. The algorithm has been tested on a set of images that were selected based upon anticipated difficult situations. The algorithm has demonstrated the ability to locate multiple objects in noisy and cluttered background scenes.

关键词： Algorithm development detection and tracking algorithms Target recognition

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Disaster detector: an automatic disk head/slider inspection system

Disaster detector: an automatic disk head/slider inspection ...

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Machine Vision Applications in Industrial Inspection II

作者： Dom, Byron E. Steele, David A. Krebs, Richard Kiehl, David R. Saldanha, Patrick Wong, Eric Moffitt, John W. Petkovic, Dragutin Herber, John Kuhlmann, Lionel Dunbar, Scott IBM Research Div. (United States) IBM Corp. (United States)

ISBN: (纸本)0819414786

This paper describes a system known as 'The Disaster Detector', for automatic inspection of the air-bearing surface of disk sliders (disk read/write heads). It inspects for certain types of defects that are global or systematic in the sense that, when they occur, they occur on every slider in a row or, in some cases, on every slider in the entire carrier. The inspection system is described and the associated image-analysis algorithms are described in detail. The system uses standard microscope optics, a color CCD camera, computer-controlled state, laser autofocus, a video digitizer and a PC/AT (486-based).

关键词： Inspection Sensors detection and tracking algorithms Video Head Image analysis Microscopes Edge detection Manufacturing Defect detection

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Segmentation engine: a real-time image segmentation subsystem

Segmentation engine: a real-time image segmentation subsyste...

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Machine Vision Applications in Industrial Inspection II

作者： Dom, Byron E. Blanz, Wolf-Ekkehard Cox, Charles Steele, David A. Dorundo, Alan D. IBM Almaden Research Ctr. (United States)

ISBN: (纸本)0819414786

This paper describes a system developed for segmenting multiband grayscale images into n-class labeled images at high-throughput rates. This system, which we refer to as the segmentation engine, performs supervised image segmentation using algorithms based on the statistical pattern recognition paradigm. So-called 'features' are computed for each pixel and the feature vector thus formed is presented to a statistical classifier, which uses feature information to determine the most probable class of the pixel. algorithms are described for the following: features, automatic feature selection, classification and classifier training. While this paper describes the entire system, the algorithmic approach will be emphasized.

关键词： Image segmentation Image processing algorithms and systems Feature selection Image processing Algorithm development Error analysis Machine vision Aluminum Radon detection and tracking algorithms

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Algorithm to choose which pixels to zero out in a zero and phase filter

Algorithm to choose which pixels to zero out in a zero and p...

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Photonics for Processors, Neural Networks, and Memories

作者： Kallman, Robert R. Univ. of North Texas (United States)

ISBN: (纸本)0819412759

An algorithm to choose which pixels to zero out in an otherwise phase-only filter is presented. This algorithm involves a simple thresholding technique applied to the general spatial filter described in previous work. Having fixed those pixels which are to be set equal to 0 in the filter, one can then view the remaining phases as free parameters and design a good 0 and continuous phase filter by optimizing the filter's signal-to-clutter ratio using the author's techniques. One can the discretize this zero and phase filter into one whose entries are 0 or one of the nth roots of unity. For example, ternary (0, +1, and -1) filter are of this form as are filters whose entries are 0 and the sixteenth roots of unity. Examples of such are given. This paper together with its ancestor provides a completely automatic way to start with a training set of true and false targets of any size and automatically and without human intervention produce optimized filters for use in extant spatial light modulators.

关键词： Electronic filtering Spatial filters detection and tracking algorithms Spatial frequencies algorithms Image filtering Phase only filters Sensors Mathematics Target recognition

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Capaciflector sensor imaging and ranging applications for robot control

Capaciflector sensor imaging and ranging applications for ro...

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Telemanipulator Technology and Space Telerobotics

作者： Kizhner, Semion NASA Goddard Space Flight Ctr. (United States)

ISBN: (纸本)0819413224

One basic robotics task is to position a robot tool point over the geometric center of a symmetric object. This paper presents a fully autonomous control command development technique of linear complexity that solves the robot tool point centering and alignment problem. It is based on non-visual sensor imaging and starts with only a partial image of the target in its field of view. A new capacitive proximity and imaging sensor, called a capaciflector, is being used to obtain 2D discrete images of objects with moderate surface complexity. Considered are spacecraft parts whose imaged surfaces are flat with a simple geometrical shape such as a solid rectangle or a circle.

关键词： Sensors Calibration Image sensors Optical sensors Robotics Image processing Data centers detection and tracking algorithms Ranging Solids

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Maximum-likelihood adaptive neural network (MLANS) application to classification while tracking

Maximum-likelihood adaptive neural network (MLANS) applicati...

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Signal Processing, Sensor Fusion, and Target Recognition III

作者： Perlovsky, Leonid I. Kwauk, Reimin Tye, David Nichols Research Corp. (United States)

ISBN: (纸本)0819415367

Currently, detection, tracking, and classification functions are performed sequentially, for example, tracks are initiated after objects have been detected. This leads to utilization of only partial information for each of the surveillance functions: the information about the object being on a specific track is not utilized for the object detection. This, in turn, leads to unnecessary limitations on system performance or to stringent and expensive sensor requirements. We have developed a novel approach to enhancing surveillance functions by combining several functions and by utilizing all the available information for each function, based on the maximum likelihood adaptive neural network (MLANS). The MLANS capability for a general model-based processing permits combining such functions as data correlation, detection, track estimation, and classification. In this application, a generic MLANS architecture implements a model that combines classification model based on statistical distributions of object features with the dynamical model of object motion. The MLANS learning mechanism results in a maximum likelihood estimation of the model parameters, yielding concurrent estimates of data association probabilities, track parameters, and object classification. This novel approach to tracking results in a dramatic improvement of performance: the MLANS tracking exceeds performance of existing tracking algorithms due to optimal utilization of all the available data.

关键词： Motion models Data modeling Neural networks Statistical modeling Surveillance Data analysis detection and tracking algorithms Model-based design Sensors

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Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

Evaluation of the maximum-likelihood adaptive neural system ...

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Signal Processing, Sensor Fusion, and Target Recognition III

作者： Chernick, Julian A. Perlovsky, Leonid I. Tye, David U.S. Army Materiel Systems Analysis Activity (United States) Nichols Research Corp. (United States)

ISBN: (纸本)0819415367

This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution;on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

关键词： Infrared imaging detection and tracking algorithms Extremely high frequency Image enhancement Model-based design Neural networks Statistical modeling Target detection

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