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Real-time visual processing in support of autonomous driving

Real-time visual processing in support of autonomous driving

引用

25th AIPR workshop on Emerging Applications of Computer Vision

作者： Nashman, M Schneiderman, H National Institute of Standards and Technology (United States) Carnegie Mellon Univ. (United States)

ISBN: (纸本)0819423661

Autonomous driving provides an effective way to address traffic concerns such as safety and congestion. There has been increasing interest in the development of autonomous driving in recent years. Interest has included high-speed driving on highways, urban driving, and navigation through less structured off-road environments. The primary challenge in autonomous driving is developing perception techniques that are reliable under the extreme variability of outdoor conditions in any of these environments. Roads vary in appearance. Some are smooth and well marked, while others have cracks and potholes or are unmarked. Shadows, glare, varying illumination, dirt or foreign matter, other vehicles, rain, and snow also affect road appearance. This paper describes a visual processing algorithm that supports autonomous driving. The algorithm requires that lane markings be present and attempts to track the lane markings on each of two lane boundaries in the lane of travel. There are three stages of visual processing computation: extracting edges, determining which edges correspond to lane markers, and updating geometric models of the lane markers. A fourth stage computes a steering command for the vehicle based on the updated road model. All processing is confined to the 2-D image plane. No information about the motion of the vehicle is used. This algorithm has been used as part of a complete system to drive an autonomous vehicle, a High Mobility Multipurpose Wheeled Vehicle (HMMWV.) Autonomous driving has been demonstrated on both local roads and highways at speeds up to 100 kilometers per hour (km/h.) The algorithm has performed well in the presence of non-ideal road conditions including gaps in the lane markers, sharp curves, shadows, cracks in the pavement, wet roads, rain, dusk, and nighttime driving. The algorithm runs at a sampling rate of 15 Hz and has a worst case processing delay time of 150 milliseconds. processing is implemented under the NASA/NBS Standard Reference

关键词： autonomous driving feature tracking hierarchical control system architecture image processing real-time processing recursive least squares world model

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Autonomous mobile robot motion control in non-structured environments based on real-time video processing

Autonomous mobile robot motion control in non-structured env...

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IEEE international workshop on Intelligent Robots and Systems (IROS)

作者： J. Ferruz A. Ollero Departmento de Ingeniería de Sistemas y Automática Escuela Superior de Ingenieros Universidad de Sevilla Seville Spain

This paper describes a method for mobile robot position estimation based on feature tracking through the sequence of images provided by a conventional video camera. The block-based correspondence algorithms selects candidates through grey level matching and local coherence analysis; a DSP-based multiprocessing image processing system is used to achieve real time performance. Point-to-point correspondences are used to estimate the motion parameters of a mobile robot. Motion-based procedures to eliminate false matching have been implemented. The method has been applied to a mobile robot in non-structured and outdoor environments.

关键词： Mobile robots Motion control Robot vision systems Cameras image analysis Performance analysis Algorithm design and analysis image processing Real time systems Motion estimation

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Circuital Markov random fields for analog edge detection

Circuital Markov random fields for analog edge detection

引用

Computer Architectures for Machine Perception (CAMP)

作者： M. Storace M. Parodi C. Regazzoni Biophysical and Electronic Engineering Department University of Genoa Genoa Italy

A nonlinear analog circuit for coupled image restoration and edge extraction based on Markov Random Fields (MRFs) is presented and analyzed. The circuit is made up of a set of identical linear capacitors and two sets of proper nonlinear resistors. The electric behaviour of the resistive part of the circuit is governed by a generalized potential function whose minima represent the solutions of the MRF problem. The advantages of analogic computations based on MRFs over classical digital approaches are discussed. In particular the real-time MRF-like processing obtained through local interactions between the circuit elements turns out to be the most interesting characteristic of the proposed circuit. Some simulations are described in order to show the performances of the MRF non-linear analog circuit.

关键词： Markov random fields image edge detection Analog circuits Coupling circuits image restoration image analysis Capacitors Resistors Computational modeling Circuit simulation

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Edge regularization of noised image method

Edge regularization of noised image method

引用

Proceedings of the 1996 4th IEEE international workshop on Cellular Neural Networks, and Their Applications, CNNA-96

作者： Stankiewicz, Arthur Merta, Idzi Jaroszewicz, Leszek R. Military Univ of Technology Warsaw Poland

In this work a model of extraction and regularization of edge of noised image is presented. Edge is defined as a local maximum of image gradient. The model is based on a formulation of local dependences between pixels of image, so called pattern and step dynamic approximation of value of brightness of individual pixels. The dynamics of the model is defined by an adequately constructed function of cost (further on called energy, due to an analogy with potential energy). Energy is specified from the use of the sum of adversities of scalar products of the neighboring vectors of gradient. This process can be easily implemented in a cellular neuron network of a suitable design. Regularization of image is an important phase of image processing, among others image recognition.

关键词： Cellular neural networks

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Convex approximation of regularization models for motion estimation with Markov random fields

Convex approximation of regularization models for motion est...

引用

Proceedings 1996 international Conference on image processing, ICIP'96

作者： Kardouchi, Mustapha Dipanda, Albert Marzani, Franck Legrand, Louis Lab d'Informatique Medicale Dijon France

In most situations, the mere gray value variations do not provide sufficient information in order to estimate the displacement vectors field between two successive frames in a sequence. The regularization constraints are necessary to solve this problem. Regularization models usually employed, taking into account the discontinuities of the displacement vectors field, involve non convex functions. Determinist algorithms do not give the optimal solution for such models. In this paper, we propose a method that takes into account the discontinuities while allowing a convex approximation of a non convex regularization model. This method is based on a Markov random field model in which we integrate a local motion amplitude obtained by Fourier analysis. The model that we thus obtained enables adaptive smoothness of the displacement field. The motion estimation is clearly improved, especially at the edges. Some results of this method on real and synthetic images are given.

关键词： image analysis

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The edge regularization of noised image method

The edge regularization of noised image method

引用

IEEE international workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： A. Stankiewicz I. Merta L.R. Jaroszewicz Institute of Air Force Technology Warsaw Poland Military University of Technology Warsaw Poland

A model of extraction and regularization of edges of noisy images is presented. Edge is defined as a local maximum of the image gradient. The model is based on a formation of local dependences between pixels of the image, so called pattern and step dynamic approximation of the value of the brightness of individual pixels. The dynamics of the model are defined by an adequately constructed function of cost (further on called energy, due to an analogy with potential energy). Energy is specified from the use of the sum of adversities of scalar products of the neighboring vectors of the gradient. This process can be easily implemented in a cellular neuron network of a suitable design. Regularization of the image is an important phase of image processing among others image recognition.

关键词： image recognition Convolution Brightness Neurons Pixel Cost function Cellular networks image processing Noise reduction Gaussian noise

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Coordinate systems and transformations for 3D modeling: the unifying concept in the RADIUS common development environment

Coordinate systems and transformations for 3D modeling: the ...

引用

24th AIPR workshop on Tools and Techniques for Modeling and Simulation

作者： Quam, Lynn H. Heller, Aaron J. SRI Int. Menlo Park CA USA

ISBN: (纸本)0819420182

The RADIUS Common Development Environment pulls together many diverse functions into an integrated whole. The main goal of the environment is to provide a system to do interactive modeling of 3-dimensional scenes from multiple images, as well as, providing an infrastructure to support the research in and implementation of image understanding-based algorithms for this and other tasks. The RCDE contains facilities for: CAD-system-like 3D modeling;image processing;electronic-light-table image viewing and exploitation;frame and non-frame camera photogrammetry;and photo realistic rendering. The major achievement of the system is the high level of integration and interoperability between and among these facilities. The key realization that enables this is that every entity represented in the RCDE has an associated local coordinate system. This includes cartographic and cultural features, images and sub-images, text annotations, graphical user interface elements, photogrammetric conjugate points and even the earth itself. These entities are tied together through a flexible and efficient network of coordinate transformations. This allows each type of data to be represented, manipulated, and displayed in the most convenient and precise form, without sacrificing functionality or generality, in addition to enabling the fusion of different types of geometric data. In this paper, we explain the coordinate system representations and transformation facilities in the RCDE and outline some of the rationale and strategies behind the current design and implementation. Also included are examples drawn from its use in the government sponsored RADIUS program.

关键词： image processing

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Improvement of 3-D data by neural networks

Improvement of 3-D data by neural networks

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international workshop on Neural Networks for Identification, Control, Robotics, and Signal/image processing

作者： T. Lilienblum P. Albrecht B. Michaelis Institute for Measurement and Electronics Otto-von-Guericke University of Magdeburg Magdeburg Germany

The 3-D measurement by photogrammetry is easy only in special cases. The measurement principle consists in an approximation of surface pieces by planes or paraboloids. If steps in surfaces or strongly curved surfaces are measured systematic errors appear. Also random errors appear. The aim is to reduce these errors. After measuring the 3-D data will be processed by neural networks. A modified associative memory smoothes and corrects the calculated 3-D coordinates using a-priori knowledge about the measurement object. The result is the reconstructed 3-D shape. For the measurement and processing of data local coordinates are used. The calculated geometrical shape is more precise than the results obtained with other methods or needs fewer measurement values. It is also possible to measure special dimensions of parts of measured objects.

关键词： Neural networks Associative memory Shape measurement Coordinate measuring machines Cameras Surface reconstruction Position measurement image reconstruction Artificial neural networks Mathematical model

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A convex approximation of regularization models for motion estimation with Markov random fields

A convex approximation of regularization models for motion e...

引用

IEEE international Conference on image processing

作者： M. Kardouchi A. Dipanda F. Marzani L. Legrand Laboratoire dInformatique médicale 7 bd jeanne darc Faculté de médecine Dijon France Liesib-Département dInformatique Faculté des Sciences Mirande Dijon France

In most situations, the mere gray value variations do not provide sufficient information in order to estimate the displacement vectors field between two successive frames in a sequence. The regularization constraints are necessary to solve this problem. Regularization models usually employed, taking into account the discontinuities of the displacement vectors field, involve non-convex functions. Determinist algorithms do not give the optimal solution for such models. We propose a method that takes into account the discontinuities while allowing a convex approximation of a non-convex regularization model. This method is based on a Markov random field model in which we integrate a local motion amplitude obtained by Fourier analysis. The model that we thus obtained enables adaptive smoothness of the displacement field. The motion estimation is clearly improved, especially at the edges. Some results of this method on real and synthetic images are given.

关键词： Motion estimation Markov random fields Motion analysis image motion analysis Biomedical imaging image processing Joining processes Brightness Pixel Statistical analysis

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MULTIRESOLUTION GMRF MODELS FOR TEXTURE SEGMENTATION

MULTIRESOLUTION GMRF MODELS FOR TEXTURE SEGMENTATION

引用

1995 international Conference on Acoustics, Speech, and Signal processing

作者： KRISHNAMACHARI, S CHELLAPPA, R UNIV MARYLAND INST ADV COMP STUDIESDEPT ELECT ENGNCOLLEGE PKMD 20742

ISBN: (纸本)0780324323

A multiresolution model for Gauss Markov random fields (GMRF) is presented. Coarser resolution sample fields are obtained by either subsampling or local averaging the sample field at the fine resolution. Although Markovianity is lost under such resolution transformation, coarser resolution non-Markov random fields can be effectively approximated by Markov fields. We use a local conditional distribution invariance approximation to estimate the parameters of the coarser resolution processes from the fine resolution parameters. This multiresolution model is used to perform texture segmentation.

关键词： image segmentation

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