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检索条件"任意字段=Conference on Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques"

共 375 条记录，以下是341-350 订阅

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A General Model and Calibration Method for Spherical Stereoscopic vision

A General Model and Calibration Method for Spherical Stereos...

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conference on intelligent robots and computer vision XXIX - algorithms and techniques

作者： Feng, Weijia Roning, Juha Kannala, Juho Zong, Xiaoning Zhang, Baofeng Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Tianjin 300072 Peoples R China Oulu Univ Infotech Oulu Oulu Finland Oulu Univ Elect & Informat Engn Dept Oulu Finland

ISBN: (纸本)9780819489487

In geometric calibration of stereoscopic cameras the object is to determine a set of parameters which describe the mapping from 3D reference coordinates to 2D image coordinates, and indicate the geometric relationships between the cameras. While various methods for stereo cameras with ordinary lenses can be found from the literature, stereoscopic vision with extremely wide angle lenses has been much less discussed. Spherical stereoscopic vision is more and more convenient in computer vision applications. However, its use for 3D measurement purposes is limited by the lack of an accurate, general, and easy-to-use calibration procedure. Hence, we present a geometric model for spherical stereoscopic vision equipped by extremely wide angle lenses. Then, a corresponding generic mathematical model is built. Method for calibration the parameters of the mathematical model is proposed. This paper shows practical results from the calibration of two high quality panomorph lenses mounted on cameras with 2048x1536 resolutions. Here, the stereoscopic vision system is flexible, the position and orientation of the cameras can be adjusted randomly. The calibration results include interior orientation, exterior orientation and the geometric relationships between the two cameras. The achieved level of calibration accuracy is very satisfying.

关键词： spherical stereoscopic vision panomorph lens general geometric and mathematical model parameter calibration

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On the complexity of computing equilibria for nonsymmetric analog networks

On the complexity of computing equilibria for nonsymmetric a...

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International Joint conference on Neural Networks (IJCNN)

作者： D.A. Miller S.W. Sucker Computer Vision and Robotics Laboratory Research Center of Intelligent Machines McGill University Montreal Canada Comput. Vision & Robotics Lab. McGill Univ. Montreal Que. Canada

The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if the amplifiers are piecewise-linear, then such networks are instances of a game-theoretic model known as polymatrix games. Equilibria for the latter may be computed by vertex pivoting algorithms similar to the simplex method for linear programming, which are in practice of low order polynomial complexity. These algorithms appear to be the only ones both guaranteed to work and which are polynomial in practice. These results suggest that networks with few nonstable equilibria would be computationally attractive.< >

关键词： Analog computers computer networks Polynomials Biology computing NP-hard problem computer vision Physics computing Piecewise linear techniques intelligent robots Robot vision systems

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Heterogeneous Multi-Robot Localization in Unknown 3D Space

Heterogeneous Multi-Robot Localization in Unknown 3D Space

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2006 IEEE/RSJ International conference on intelligent robots and Systems

作者： Yi Feng Zhigang Zhu Jizhong Xiao Department of Computer Science CUNY NY USA Department of Computer Science City College of New York NY USA

This paper presents a self-localization strategy for a team of heterogeneous mobile robots including ground mobile robots of various sizes and wall-climbing robots. These robots are equipped with various visual sensors, such as miniature webcams, omnidirectional cameras, and PTZ cameras. As the core of this work, a formation of four-robot team is constructed to operate in a 3D space, e.g., moving on ground, climbing on walls and clinging to ceilings. The four robots could dynamically localize themselves asynchronously by employing cooperative vision techniques. Three of them on the ground mutually view each other and determine their relative poses with 6 degrees of freedom (DOFs). A wall-climbing robot, which significantly extends the work space of the robot team to 3D, is at a vantage point (e.g., on the ceiling) such that it can see all the three teammates, thus determining its own location and orientation. The four-robot formation theory and algorithms are presented, and experimental results with both simulated and real image data are provided to demonstrate the feasibility of this formation. Two 3D localization and control strategies are designed for applications such as search and rescue and surveillance in 3D urban environments where robots must be deployed in a full 3D space

关键词： Orbital robotics Robot kinematics Mobile robots Robot sensing systems Robot vision systems Cameras Service robots intelligent robots computer science Cities and towns

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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IEEE International conference on Power, intelligent Computing and Systems (ICPICS)

作者： Herun Zhong School of Arts and Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. This article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. The discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks.

关键词： Deep learning computer vision Visualization Image recognition Benchmark testing Visual systems Classification algorithms Object recognition Convolutional neural networks Videos

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Reducing Computation Requirements for Autonomous Mobile robots on Low Powered Embedded Systems

Reducing Computation Requirements for Autonomous Mobile Robo...

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Modeling, Simulation & intelligent Computing (MoSICom), International conference on

作者： Abhinav Pathak Aditya Singhal Adit Tewari Aadish Jain V. Kalaichelvi Department of Computer Science Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.

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Joint probabilistic techniques for tracking objects using multiple visual cues

Joint probabilistic techniques for tracking objects using mu...

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IEEE International Workshop on intelligent robots and Systems (IROS)

作者： C. Rasmussen G.D. Hager Center for Computational Vision & Control Yale University New Haven CT USA

robots relying on vision as a primary sensor frequently need to track common objects such as people, cars, and tools in order to successfully perform autonomous navigation or grasping tasks. These objects may comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying characteristics. In this paper, we present a framework for sharing information among disparate state estimation processes operating on the same underlying visual object. Well-known techniques for joint probabilistic data association are adapted to yield increased robustness when multiple trackers attuned to different visual cues are deployed simultaneously. We also formulate a measure of tracker confidence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We will discuss experiments using color-region- and snake-based tracking in tandem that demonstrate the efficacy of this approach.

关键词： Target tracking Head State estimation Filters computer vision Robustness Switches Technological innovation Current measurement Computed tomography

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Autonomous flight and vision-based target tracking for a flapping-wing MAV

Autonomous flight and vision-based target tracking for a fla...

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IEEE/RSJ International conference on intelligent robots and Systems

作者： Seungwan Ryu Ukjin Kwon H. Jin Kim Department of Mechanical and Aerospace Engineering Seoul National University Korea Department of Electrical and Computer Engineering Seoul National University Korea

ISBN: (纸本)9781509037636

In this paper, we design an autonomous flight controller for height regulation and bang-bang controller for directional control of a light-weight flapping-wing micro air vehicle (FWMAV) with limited payload. We also present autonomous vision-based target tracking for a FWMAV equipped with a low-cost and light-weight first person view (FPV) camera. We construct a ground station, integrated with control and vision algorithms, which performs the image processing and the computation of control inputs based on the acquired state variables from motion capture system. In addition, we employ a vision algorithm for a low-quality camera to detect a static target with the discussions on the techniques to improve the reliability of visual detection. Experimental results show satisfactory flight performance, achieving the height regulation and directional control, and autonomous vision-based target tracking.

关键词： Cameras Target tracking Vehicles Streaming media Actuators Data models Batteries

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Automatic restoration of underwater monocular sequences of images

Automatic restoration of underwater monocular sequences of i...

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IEEE/RSJ International conference on intelligent robots and Systems

作者： P. Drews E. R. Nascimento M. F. M. Campos A. Elfes Department of Computer Science of the Universidade Federal de Minas Gerais - UFMG Belo Horizonte Brazil Centro de Ciências Computacionais - C3 Univ. Federal do Rio Grande - FURG Rio Grande Brazil Autonomous System Program - ASP CSIRO Pullenvale Australia

Underwater environments present a considerable challenge for computer vision, since water is a scattering medium with substantial light absorption characteristics which is made even more severe by turbidity. This poses significant problems for visual underwater navigation, object detection, tracking and recognition. Previous works tackle the problem by using unreliable priors or expensive and complex devices. This paper adopts a physical underwater light attenuation model which is used to enhance the quality of images and enable the applicability of traditional computer vision techniques images acquired from underwater scenes. The proposed method simultaneously estimates the attenuation parameter of the medium and the depth map of the scene to compute the image irradiance thus reducing the effect of the medium in the images. Our approach is based on a novel optical flow method, which is capable of dealing with scattering media, and a new technique that robustly estimates the medium parameters. Combined with structure-from-motion techniques, the depth map is estimated and a model-based restoration is performed. The method was tested both with simulated and real sequences of images. The experimental images were acquired with a camera mounted on a Remotely Operated Vehicle (ROV) navigating in a naturally lit, shallow seawater. The results show that the proposed technique allows for substantial restoration of the images, thereby improving the ability to identify and match features, which in turn is an essential step for other computer vision algorithms such as object detection and tracking, and autonomous navigation.

关键词： Image restoration Optical imaging Attenuation Cameras Lighting computer vision Estimation

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Autonomous altitude estimation of a UAV using a single onboard camera

Autonomous altitude estimation of a UAV using a single onboa...

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2009 IEEE/RSJ International conference on intelligent robots and Systems

作者： Anoop Cherian Jon Andersh Vassilios Morellas Nikolaos Papanikolopoulos Bernard Mettler Department of Computer Science University of Minnesota Minneapolis MN USA Department of AeroSpace Engineering University of Minnesota Minneapolis MN USA

Autonomous estimation of the altitude of an Unmanned Aerial Vehicle (UAV) is extremely important when dealing with flight maneuvers like landing, steady flight, etc. vision based techniques for solving this problem have been underutilized. In this paper, we propose a new algorithm to estimate the altitude of a UAV from top-down aerial images taken from a single on-board camera. We use a semi-supervised machine learning approach to solve the problem. The basic idea of our technique is to learn the mapping between the texture information contained in an image to a possible altitude value. We learn an over complete sparse basis set from a corpus of unlabeled images capturing the texture variations. This is followed by regression of this basis set against a training set of altitudes. Finally, a spatio-temporal Markov Random Field is modeled over the altitudes in test images, which is maximized over the posterior distribution using the MAP estimate by solving a quadratic optimization problem with L1 regularity constraints. The method is evaluated in a laboratory setting with a real helicopter and is found to provide promising results with sufficiently fast turnaround time.

关键词： Unmanned aerial vehicles Machine learning Helicopters intelligent robots USA Councils Robot vision systems Smart cameras computer science Machine learning algorithms Markov random fields

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Scenario-based simulation of intelligent driving functions using neural networks

Scenario-based simulation of intelligent driving functions u...

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International conference on Computing, Communication and Networking Technologies

作者： Parampreet Kaur Rajeev Sobti School of Computer Science Engineering Lovely Professional University Jalandhar India

The modern technological era is witnessing a significant advancement in AI related fields such as machine learning, mobile robots and autonomous vehicles. The success of such systems is immensely dependent on computer vision algorithms. The entirely software enabled intelligent vehicles will soon be hitting the roads in the coming decade. Such self-controlled mobile robots may still be vulnerable to accidents or crashes. Therefore, the industry requires some state of the art techniques to substantiate the safety and protection of its passengers as well as other road users. Simulation based testing methods have been in use from a long time, but the newer smart vehicle innovations require better versions of simulation techniques. We thus provide an improved mechanism for simulation testing to validate safe navigation of cars in variety of traffic scenarios. Neural network approach integrated with agent based modelling is described in this paper.

关键词： Agent based modelling intelligent driving SAE autonomy levels Simulation ADAS

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