A novel approach to robot guidance in an unfamiliar environment is presented. In previous guiding methods, a preinstalled map or predefined path is required for a robot navigating in its working space. The present met...
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A novel approach to robot guidance in an unfamiliar environment is presented. In previous guiding methods, a preinstalled map or predefined path is required for a robot navigating in its working space. The present method uses an indication post (IP) to provide the information on the destination, the direction, and the distance from the source point to the destination point, assuming that the mobile robot has no information concerning its workplace. To realize the concept, many IPs depicting simple information were designed. The content of IPs can be described by using a finite-state grammar. image-processing, statistical, and syntactic pattern recognition approaches are integrated to solve the problems of IP finding, robot location determination, IP identification, and understanding. Experiments simulating practical environments were performed. The results verify that the IP candidates can be automatically found and the depicted information can be extracted and understood correctly, so that the mobile robot is able to plan a global optimal path from an arbitrary start point to an arbitrary end point.< >
The performance is compared of a linear space-invariant (LSI) maximum a posteriori filter, an LSI reduced update Kalman filter (RUKF), an edge-adaptive RUKF, and an adaptive convex-type constraint-based restoration im...
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The performance is compared of a linear space-invariant (LSI) maximum a posteriori filter, an LSI reduced update Kalman filter (RUKF), an edge-adaptive RUKF, and an adaptive convex-type constraint-based restoration implemented via the method of projection onto convex sets. The finite impulse response Wiener filter is taken as a benchmark in this comparison. In image restoration, the LSI techniques are found to have some important drawbacks, such as producing ringing artifacts. As expected, the space-variant restoration methods which are adaptive to local image properties provide the best results.
Various types of statistical signal processing require the estimation of time-variant correlation functions and the fitting of time-variant models for nonstationary processes. In this paper, the situations for which n...
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Various types of statistical signal processing require the estimation of time-variant correlation functions and the fitting of time-variant models for nonstationary processes. In this paper, the situations for which nonstationary probabilistic correlation functions can be accurately estimated from a single sample path of a stochastic process are delineated. These include only (i) known form of nonstationarity, (ii) periodic or almost periodic nonstationarity, and (iii) slowly fluctuating nonstationarity. Known methods of estimation for each of these situations are reviewed within the unifying framework of orthogonal series expansions. The fact that estimators based on orthogonal series expansions for nonstationary correlation functions other than (i)–(iii) cannot be guaranteed to be accurate is established. Ramifications for time-variant autoregressive model fitting are discussed. Verschiedene Aufgabenbereiche der Signalverarbeitung erfordern die Schätzung zeitvarianter Korrelationsfunktionen und die zeitvariante Modellierung nichtstationärer Prozesse. Im folgenden werden die Situationen dargelegt, für die nicht-stationäre Korrelationsfunktionen aus einem einzigen Repräsentanten eines Prozesses geschätzt werden können. Erfaßt werden können nur Fälle mit (i) bekannter Form der Instationarität, (ii) periodischer oder fast-periodischer Instationarität und (iii) langsam veränderlicher Instationarität. Die bekannten Schätzverfahren für jede dieser Gegebenheiten werden in einem einheitlichen Rahmen, dem der Orthogonalreihen-Entwicklung, zusammengestellt. Die Grenzen der Nutzung von Reihenentwicklungen bei der Schätzung nicht-stationärer Korrelationsfunktionen werden aufgezeigt. Variationsmöglichkeiten für die zeitvariante AR-Modellierung werden diskutiert. De nombreuses operations de traitement du signal requièrent l'estimation de fonctions de corrélation dependent du temps et la construction de modèles variant avec le temps pour les processus nonstationnaires. Dans cet a
A model-based approach is proposed for the problem of texture segmentation using a maximum a posteriori (MAP) estimation technique. A Gauss-Markov random field (GMRF) is used for the conditional density of the intensi...
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A model-based approach is proposed for the problem of texture segmentation using a maximum a posteriori (MAP) estimation technique. A Gauss-Markov random field (GMRF) is used for the conditional density of the intensity array, given the unobserved texture class and a second-order Ising distribution for the prior distribution over the texture classes. The GMRF model for the conditional density allows a closed-form expression for the density to be written, so that the dependence of the density on the label parameters can be expressed. This expression is used here to derive the joint distribution of intensity and label arrays. The joint distribution is maximized using the stochastic relaxation method and the deterministic iterated conditional mode (ICM) technique. The ICM algorithm can be implemented efficiently on a neural net with local connectivity and regular structure. Comparisons of these two methods are given using real textured images.
The accuracy of optical flow computation is investigated using gradient methods and global optimization. It considers the influence of systematic and statistical errors on the constraint equation, and of systematic an...
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ISBN:
(纸本)0818608781
The accuracy of optical flow computation is investigated using gradient methods and global optimization. It considers the influence of systematic and statistical errors on the constraint equation, and of systematic and statistical errors on the iterative solution of the optimization of the continuity constraint. Approaches to error reduction are suggested. Experimental results are given using synthetic images with exactly known properties demonstrating the efficiency of error reduction techniques.
It is shown that the statistical properties of the mean absolute difference mismatch measure surface is dependent on the autocorrelation function of image and signal-to-noise ratio. A method for finding the match poin...
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It is shown that the statistical properties of the mean absolute difference mismatch measure surface is dependent on the autocorrelation function of image and signal-to-noise ratio. A method for finding the match point is presented, based on the idea of testing a set of hypotheses. Using the threshold estimation formulas and the optimized search scheme developed by the authors, a fast algorithm which may be adaptable to various expected operating conditions can be implemented.< >
The proceedings contain 66 papers. The special focus in this conference is on Pattern Recognition. The topics include: Change detection in digital imagery using the adaptive learning networks;image segmentation using ...
ISBN:
(纸本)9783540190363
The proceedings contain 66 papers. The special focus in this conference is on Pattern Recognition. The topics include: Change detection in digital imagery using the adaptive learning networks;image segmentation using causal Markov random field models;preditas — Software package for solving pattern recognition and diagnostic problems;processing poor quality line drawings by local estimation of noise;a color classification algorithm for color images;fuzzy set methods in pattern recognition;a fuzzy hybrid model for pattern classification;on the role of pattern in recognizer design;a statistical study in word recognition;an integrated image segmentation/image analysis system;feature extraction from line drawing images;syntax analysis in automated digitizing of maps;a fast binary template matching algorithm for document image data compression;recognition system for three-view mechanical drawings;a heuristic algorithm for stroke extraction;an analysis of methods for improving long-range connectivity in meshes;Implementation and use of software scanning on a small CLIP4 processor array;Performing global transforms on an SIMD machine;a parallel architecture for model-based object recognition;lapwing - A trainable image recognition system for the linear array processor;a fast algorithm for the automatic recognition of heat sources in satellite images;linguistic definition of generic models in computer vision;a multiple hypothesis rule-based automatic target recognizer;generic cueing in image understanding;knowledge-based approach for adaptive recognition of drawings;extended symbolic projections as a knowledge structure for spatial reasoning;Knowledge-based road network extraction on SPOT satellite images;median-based methods of corner detection;an efflcient Radon transform.
A model-based approach is proposed for the problem of texture segmentation using a maximum a posteriori (MAP) estimation technique. A Gauss-Markov random field (GMRF) is used for the conditional density of the intensi...
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A model-based approach is proposed for the problem of texture segmentation using a maximum a posteriori (MAP) estimation technique. A Gauss-Markov random field (GMRF) is used for the conditional density of the intensity array, given the unobserved texture class and a second-order Ising distribution for the prior distribution over the texture classes. The GMRF model for the conditional density allows a closed-form expression for the density to be written, so that the dependence of the density on the label parameters can be expressed. This expression is used here to derive the joint distribution of intensity and label arrays. The joint distribution is maximized using the stochastic relaxation method and the deterministic iterated conditional mode (ICM) technique. The ICM algorithm can be implemented efficiently on a neural net with local connectivity and regular structure. Comparisons of these two methods are given using real textured images.< >
Presents a probabilistic formulation for motion estimation in images and a stochastic algorithm for minimization of the associated objective function. It is shown that motion estimation, an ill-posed problem, can be r...
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Presents a probabilistic formulation for motion estimation in images and a stochastic algorithm for minimization of the associated objective function. It is shown that motion estimation, an ill-posed problem, can be regularized by means of a Bayesian estimation approach. The unknown motion field is modeled as a two-dimensional vector Markov random field with a certain neighbourhood system. The posterior distribution of the motion field given image observations is then a Gibbs distribution. Maximization of this a posteriori probability to obtain the MAP estimate of the motion field is achieved by simulated annealing. Results of the estimation procedure applied to television sequences with natural motion are presented.< >
A probabilistic model called the stochastic segment model is introduced that describes the statistical dependence of all the frames of a speech segment. The model uses a time-warping transformation to map the sequence...
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A probabilistic model called the stochastic segment model is introduced that describes the statistical dependence of all the frames of a speech segment. The model uses a time-warping transformation to map the sequence of observed frames to the appropriate frames of the segment model. The joint density of the observed frames is then given by the joint density of the selected model frames. The automatic training and recognition algorithms are discussed and a few preliminary recognition results are presented.< >
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