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

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COMPARING SUBSET-CONVERGENT AND VARIABLE-DEPTH LOCAL SEARCH ON PERSPECTIVE-SENSITIVE LANDMARK RECOGNITION PROBLEMS

COMPARING SUBSET-CONVERGENT AND VARIABLE-DEPTH LOCAL SEARCH ...

引用

11TH CONF ON INTELLIGENT ROBOTS AND COMPUTER VISION : algorithms, TECHNIQUES, AND ACTIVE VISION

作者： BEVERIDGE, JR Univ. of Massachusetts/Amherst (United States)

ISBN: (纸本)0819410268

An intelligent robot with a camera and a partial model of its environment should be able to determine where it is from what it sees. This goal, landmark based navigation, can be realized using geometric object recognition algorithms. An important problem that arises in the development of such algorithms concerns the role of full 3-D perspective projection. Much of the work on object recognition has focused upon simplified problems which are essentially 2- D. One such simplification uses weak-perspective: to test the alignment of matched features object models are rotated, translated, and scaled in the image plane. At increased computational cost, full-perspective can be incorporated into recognition using a family of probabilistic optimization procedures based upon local search. This paper considers two specific algorithms from this family: subset-convergent and variable-depth local search. Both approaches reliably recognize landmarks even when landmark appearance is sensitive to perspective. Results presented here suggest the relatively simpler variable-depth algorithm is preferable when errors in the robot pose estimate are smaller, but that at some point as uncertainty in the initial pose estimate increases the more sophisticated subset-convergent algorithm becomes preferable.

关键词： Image segmentation 3D modeling Robot vision Algorithm development detection and tracking algorithms Data modeling Visual process modeling Computer vision technology Machine vision Cameras

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CORNER detection USING SPLINE-WAVELETS 2

CORNER DETECTION USING SPLINE-WAVELETS

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CONF ON CURVES AND SURFACES IN COMPUTER VISION AND GRAPHICS 2

作者： CHAN, AK CHUI, CK ZHA, J LIU, Q Texas A&M Univ. (United States)

ISBN: (纸本)081940747X

detection of corners in an image is very useful in computer vision and pattern recognition. The existing algorithms for corner detection seem to be insufficient in many situations. The corner detection algorithm proposed in this paper is based on spline-wavelet decompositions. Corner and edge detectors are constructed from the 2-D wavelet transform coefficients. A somewhat sophisticated thresholding technique is applied to remove noise and minor irregularities in the images. Noise can be further reduced if additional processing is applied to the component images at all resolutions. Information on the edges and corners is contained in the component images in all the octaves to facilitate precise localization. A real-time wavelet decomposition algorithm is developed for the corner and edge detectors. It is very efficient and requires very little memory, since most of the computations involve only simple moving average operations and sub-sampling.

关键词： Corner detection Wavelets detection and tracking algorithms Sensors Computer vision technology Machine vision Wavelet transforms Algorithm development Computer graphics Visualization

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detection AND CLASSIFICATION OF CYCLOSTATIONARY SIGNALS VIA CYCLIC-HOS - A UNIFIED APPROACH

DETECTION AND CLASSIFICATION OF CYCLOSTATIONARY SIGNALS VIA ...

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CONF ON ADVANCED SIGNAL PROCESSING algorithms, ARCHITECTURES, AND IMPLEMENTATIONS 3

作者： DANDAWATE, AV GIANNAKIS, GB Univ. of Virginia (United States)

ISBN: (纸本)081940943X

detection and classification of cyclostationary signals in noise of unknown distribution is addressed and novel tests for cyclostationarity are proposed. Both cases of known and unknown signal statistics are considered. The proposed approaches exploit the asymptotic normality of sample cyclic- cumulant and polyspectrum estimators for deriving asymptotically optimal X² tests. Simpler, but generally suboptimal versions are also presented. Simulations are performed to test the proposed algorithms and illustrate their insensitivity to any stationary noise as well as the ability of higher-than second-order schemes to suppress cyclostationary Gaussian interferences of unknown covariance.

关键词： Signal detection Interference (communication) Error analysis Signal processing Receivers Signal to noise ratio Statistical analysis detection and tracking algorithms Monte Carlo methods Sensors

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CONSTRAINED NEURAL NETWORK ARCHITECTURES FOR TARGET RECOGNITION 3

CONSTRAINED NEURAL NETWORK ARCHITECTURES FOR TARGET RECOGNIT...

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3RD ANNUAL INTERNATIONAL CONF ON APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

作者： HUSH, DR MOYA, MM CLARK, SY Univ. of New Mexico (United States) Sandia National Labs. (United States)

ISBN: (纸本)0819408743

This paper describes several different types of constraints that can be placed on multilayered feedforward neural networks which are used for automatic target recognition (ATR). We show how unconstrained networks are likely to give poor generalization on the ATR problem. We also show how the ATR problem requires a special type of classifier called a one-class classifier. The network constraints come in two forms: architectural constraints and learning constraints. Some of the constraints are used to improve generalization, while others are incorporated so that the network will be forced to perform one-class classification.

关键词： Automatic target recognition Neural networks Artificial neural networks Pattern recognition Multilayers Network architectures Sensors Target recognition detection and tracking algorithms Image segmentation

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HOUGH-BASED RECOGNITION OF COMPLEX 3D ROAD SCENES 6

HOUGH-BASED RECOGNITION OF COMPLEX 3D ROAD SCENES

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CONF ON MOBILE ROBOTS 6

作者： FORESTI, G REGAZZONI, CS Univ. of Genoa (Italy)

ISBN: (纸本)081940750X

In this paper, we address the problem of the object recognition in a complex 3-D scene by detecting the 2-D object projection on the image-plane for an autonomous vehicle driving; in particular, the problems of road detection and obstacle avoidance in natural road scenes are investigated. A new implementation of the Hough Transform (HT), called Labeled Hough Transform (LHT), to extract and group symbolic features is here presented; the novelty of this method, in respect to the traditional approach, consists in the capability of splitting a maximum in the parameter space into noncontiguous segments, while performing voting. Results are presented on a road image containing obstacles which show the efficiency, good quality, and time performances of the algorithm.

关键词： Roads Sensors Hough transforms Mobile robots 3D image processing detection and tracking algorithms Image processing Image quality Image segmentation Image sensors

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DATA INTEGRATION (FUSION) TREE PARADIGM

DATA INTEGRATION (FUSION) TREE PARADIGM

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

作者： BOWMAN, C Ball Aerospace (United States)

ISBN: (纸本)0819408638

Data integration (fusion) trees provide a top-down functional partitioning of the level 1 data fusion process. Since this process has exponential growth in complexity the fusion tree is selected to balance system performance with cost. The fusion tree defines the order in which the data is to be integrated. The data integration is specified at each fusion node to include common referencing, data association, and state estimation. The breadth of application of this paradigm is described herein.

关键词： Data fusion Sensors Signal processing Data integration Data processing detection and tracking algorithms Kinematics Performance modeling Electronic filtering Filtering (signal processing)

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NEURAL NETWORKS APPLIED TO RADAR SIGNAL-PROCESSING - FROM SIMULATION TO HARDWARE 3

NEURAL NETWORKS APPLIED TO RADAR SIGNAL-PROCESSING - FROM SI...

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3RD ANNUAL INTERNATIONAL CONF ON APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

作者： SCHLAG, KL KOPP, MT NUNEMAKER, AN SPILLANE, JT PETTY, KC THOMPSON, RA Georgia Tech Research Institute (United States)

ISBN: (纸本)0819408743

Signal processing techniques are currently being developed by engineers and analysts to exploit subtle radar phenomena. Research efforts at the Georgia Tech Research Institute, sponsored by the U.S. Air Force, have been directed toward the collection and analysis of data on some of the more subtle characteristics of signals from complex domestic radar systems. Analysis efforts have included both classical and emerging techniques. Applying classical statistical methods of analysis to Georgia Tech's data base on such characteristics, analysts have obtained encouraging results in many areas of radar signal processing. There remain, however, areas involving slowly changing characteristics where an adaptive capability could be critical, particularly in the dense electromagnetic environments that can be anticipated. Recent rapid advances in neural network techniques, when accompanied by advances in parallel processing hardware that will be needed to implement the networks for future field use, have resulted in the potential to provide this adaptability to changing parametric conditions. To assess that potential, the U.S. Air Force initiated two separate programs at the Georgia Tech Research Institute. Results from one program include an evaluation of a number of neural network paradigms for their appropriateness for processing radar waveforms to extract subtle information contained there. Test results using selected neural networks with various forms of collected data are presented. Results from another ongoing effort include a discussion of an effort to implement in hardware one of the neural networks tested.

关键词： Neural networks Radar Radar signal processing Artificial neural networks Signal processing Computer simulations Statistical analysis Analytical research Data processing detection and tracking algorithms

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TRAINING NEURAL NETWORKS WITH GENETIC algorithms FOR TARGET detection

TRAINING NEURAL NETWORKS WITH GENETIC ALGORITHMS FOR TARGET ...

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1ST CONF ON SCIENCE OF ARTIFICIAL NEURAL NETWORKS

作者： SCHERF, AV VOELZ, LD Loral Aeronutronic (United States)

ISBN: (纸本)0819408751

algorithms for training artificial neural networks, such as backpropagation, often employ some form of gradient descent in their search for an optimal weight set. The problem with such algorithms is their tendency to converge to local minima, or not to converge at all. Genetic algorithms simulate evolutionary operators in their search for optimality. The techniques of genetic search are applied to training a neural network for target detection in infrared imagery. The algorithm design, parameters, and experimental results are detailed. Testing verifies that genetic algorithms are a useful and effective approach for neural network training.

关键词： Target detection Genetic algorithms Genetics Neural networks Artificial neural networks detection and tracking algorithms Infrared imaging Infrared radiation Image filtering Infrared detectors

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ROBUST OBJECT detection UNDER VARIOUS ENVIRONMENTAL-CONDITIONS

ROBUST OBJECT DETECTION UNDER VARIOUS ENVIRONMENTAL-CONDITIO...

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

作者： MAEDA, E ISHII, K NTT Human Interface Labs. (Japan)

ISBN: (纸本)0819407445

We present a method which is robust to environmental conditions, that detects the existence of space filling objects such as cars, people, etc. This method is unaffected by uniform or local variations in image brightness induced by lighting conditions, weather, shadow of other objects such as buildings, trees, clouds, etc. Moreover, it does not depend on the position, size, or background pattern of the regions, or the shapes and color of the objects to be detected. The method consists of the following processes. Normalize brightness of the target and the reference image using mean and variance of the brightness in respective regions. The reference image is the image which represents the background scene without objects and is taken from the same camera position as the target images. Calculate normalized principal component features from both sets of normalized image brightness. Use the features to construct a classifier by statistical learning. The proposed features are determined by the variance and covariance of brightness of both images. They are a better measure of the correspondence of two images than conventional features such as the correlation coefficient of image brightness or statistics calculated from a difference image. The method is applied to car detection in a parking lot. The experimental images were collected over a one year period under various conditions. At least 98% of the cars were always correctly detected. Application to moving-car detection and person detection in a hall are presented. Since the proposed algorithm for object detection is robust under various environmental conditions and is object independent, it is well suited to a wide range of facilities offering automatic surveillance, automatic counting, automatic recognition of scene situation, etc.

关键词： detection and tracking algorithms Machine vision Environmental sensing Robots Robot vision Computer vision technology Mahalanobis distance Cameras Error analysis Infrared imaging

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VERSATILE ARCHITECTURE FOR IMAGE RECOGNITION APPLICATIONS

VERSATILE ARCHITECTURE FOR IMAGE RECOGNITION APPLICATIONS

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CONF ON MACHINE VISION ARCHITECTURES, INTEGRATION, AND APPLICATIONS

作者： SACRAMONE, A SCOLA, J SHAZEER, D Charles Stark Draper Lab. Inc. (United States)

ISBN: (纸本)0819407526

Architectures for the development of image recognition algorithms must support the implementation of systematic procedures for solving image recognition problems. All too often, designers develop image recognition architectures in an ad hoc fashion which lacks the structure to meet long term needs. Vendors typically supply customers with standard image processing libraries and display tools. Combining these tools and formulating development strategies have remained stumbling blocks in the design of complete image recognition algorithm development environments. In this paper, an architecture is presented which provides a well defined framework, and at the same time is sufficiently flexible to accommodate images of multiple sensor and data types. The primary components of the architecture are: ground-truthing, preprocessing (which includes image processing and segmentation), feature extraction, classification, and performance analysis. Powerful and well defined data structures are exploited for each of the primary components. Groups of programs called tasks manipulate one or more of these data structures, each task belonging to one of the primary components. Multiple tasks can be executed in an unsupervised mode over an entire database of images. Results are then subjected to performance analysis and feedback. A description of the primary components and how they are integrated to facilitate the rapid prototyping and development of image recognition algorithms is presented.

关键词： Algorithm development detection and tracking algorithms Image processing Image sensors Databases Feature extraction Image classification Image segmentation Rapid manufacturing Sensors

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