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CIRCULAR-MELLIN FEATURES FOR TEXTURE SEGMENTATION

IEEE TRANSACTIONS ON IMAGE PROCESSING 1995年第12期4卷 1629-1640页

作者： RAVICHANDRAN, G TRIVEDI, MM Computer Vision and Robotics Research Laboratory Electrical and Computer Engineering Dept. University of Tennessee Knoxville TN 37996-2100 USA

Texture is an important cue in region-based segmentation of images. In this paper, we provide insight into the development of a new set of distortion-invariant texture operators. These ''circular-Mellin'' operators are invariant to both scale and orientation of the target and represent the spectral decomposition of the image scene in the polar-log coordinate system. Coupled with the unique shift invariance property of the correlator architecture, we show that these circular-Mellin operators can be used for rotation-and scale-invariant feature extraction. We note that while these feature extractors have a functional form that is similar to the Gabor operators, they have distortion-invariant characteristics unlike the Gabor functions that make them more suitable for texture segmentation. A detailed analytical description of these operators and segmentation results to highlight their salient properties are presented.

关键词： Feature extraction Correlation Image segmentation Gabor filters Maximum likelihood detection Correlators Nonlinear filters

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial Intelligence Autonomous Vehicles Deep leaning Edge Computing Internet of Vehicles

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RESIDUAL UNCERTAINTY IN 3-DIMENSIONAL RECONSTRUCTION USING 2-PLANES CALIBRATION AND STEREO METHODS

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PATTERN RECOGNITION 1995年第7期28卷 1073-1082页

作者： THAYER, S TRIVEDI, M Electrical and Computer Engineering Department Computer Vision and Robotics Research Laboratory The University of Tennessee Knoxville TN 37996-2100 U.S.A.

A necessary goal in the calibration of any instrument is an indication of how well the particular device performs. In the specific case where a camera system is used in the process of three-dimensional reconstruction or stereopsis, geometric measures of the accuracy, precision, and repeatability of 3-D point measurements give a quantitative evaluation of the success of that algorithm. This paper presents an experimental performance analysis of a traditional calibration algorithm as applied to the problem of three-dimensional vision. The study begins with an analysis of the algorithm in a single camera or monocular system. Monocular analysis concludes with a derivation of error boundaries under different. geometric constraints. Showing how this residual error propagates from the component monocular systems into a three-dimensional reconstruction, a binocular analysis derives the desired performance monitors. The paper concludes with a geometric interpretation of the results.

关键词： STEREOPSIS ACCURACY PRECISION REPEATABILITY MONOCULAR BINOCULAR 2-PLANES CALIBRATION

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence computer architecture Data models Accuracy Servers Safety

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LINE DETECTION IN DIGITAL PICTURES - A HYPOTHESIS PREDICTION VERIFICATION PARADIGM

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computer vision GRAPHICS AND IMAGE PROCESSING 1987年第1期40卷 95-114页

作者： MANSOURI, AR MALOWANY, AS LEVINE, MD Computer Vision and Robotics Laboratory McGill Research Centre for Intelligent Machines McGill University Montréal Québec Canada

We present an efficient method for detecting straight line segments in digital pictures using a hypothesis prediction/verification paradigm. In this paradigm, a straight line segment of predefined length is predicted to exist at some particular pixel location. The orientation of this predicted line segment is based on the edge orientation at the pixel location. This prediction is then verified against statistical tests performed on the line. As a result, the predicted line is either validated as being a line segment, or it is rejected. An extension of this algorithm for the detection of lines at different lengths is also presented, and a criterion is defined in order to evaluate the significance of the detected line segments.

关键词： Image processing

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A DATABASE DESIGN FOR THE RUNTIME ENVIRONMENT OF A ROBOTIC WORKCELL

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robotics AND computer-INTEGRATED MANUFACTURING 1989年第1期5卷 21-31页

作者： FREEDMAN, P MICHAUD, C CARAYANNIS, G MALOWANY, A Computer Vision and Robotics Laboratory McGill Research Centre for Intelligent Machines McGill University Montréal Québec Canada

Intelligent robotic workcell activities have come to require a database framework for arranging, storing and accessing information in real-time about the workcell environment in a standard way. After a brief introduction to the theory of databases (DB), we examine the general topic of robotic workcells, identify characteristics typical of robotic applications, and then present a survey of DB-related work in the robotics domain. We then construct a set of design constraints based on our analysis of robotic applications, and describe a suitable software architecture. The paper concludes with a discussion of experience gained with two generations of DB implementations.

关键词： Database Systems

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FROM ORIENTATION SELECTION TO OPTICAL-FLOW

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computer vision GRAPHICS AND IMAGE PROCESSING 1987年第2期37卷 196-220页

作者： ZUCKER, SW IVERSON, L Computer Vision and Robotics Laboratory McGill Research Centre for Intelligent Machines McGill University Montréal Québec Canada

Optical flow can be thought of as a generalization of orientation selection to include time, and this is a first attempt to develop this generalization. This paper is informal, and begins with an overview of a computational theory of orientation selection that has been developed in our laboratory during the past few years. A theory of optical flow computations is then sketched on the basis of this background, and a number of consequences important for both computational modeling and psychophysics are discussed. We concentrate in particular on two notions—dimensionality and structure—and describe a series of experiments that demonstrate that sensitivity to discontinuities in optical flow patterns has significant similarities to those in orientation patterns. Such similarities lend strong support to the analogy.

关键词： vision

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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作者： Kim, Min-Jung Oh, Tae-Hyun Kweon, In So Robotics and Computer Vision Laboratory KAIST Korea Republic of

ISBN: (纸本)9781479957514

Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera. © 2014 IEEE.

关键词： Cameras

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Robust road marking detection & recognition using density-based grouping & machine learning techniques 17

Robust road marking detection & recognition using density-ba...

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17th IEEE Winter Conference on Applications of computer vision, WACV 2017

作者： Bailo, Oleksandr Lee, Seokju Rameau, Francois Yoon, Jae Shin Kweon, In So KAIST Robotics and Computer Vision Laboratory United States

ISBN: (纸本)9781509048229

This paper presents a robust approach for road marking detection and recognition from images captured by an embedded camera mounted on a car. Our method is designed to cope with illumination changes, shadows, and harsh meteorological conditions. Furthermore, the algorithm can effectively group complex multi-symbol shapes into an individual road marking. For this purpose, the proposed technique relies on MSER features to obtain candidate regions which are further merged using density-based clustering. Finally, these regions of interest are recognized using machine learning approaches. Worth noting, the algorithm is versatile since it does not utilize any prior information about lane position or road space. The proposed method compares favorably to other existing works through a large number of experiments on an extensive road marking dataset. © 2017 IEEE.

关键词： Road and street markings

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AN INTEGRATED PROGRAMMING ENVIRONMENT FOR A GENERIC ROBOTIC WORKCELL

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JOURNAL OF ROBOTIC SYSTEMS 1989年第2期6卷 149-173页

作者： CARAYANNIS, G FREEDMAN, P MALOWANY, A Computer Vision and Robotics Laboratory McGill Research Centre for Intelligent Machines McGill University Montreal Quebec Canada

AbstractThe complexity of modern robotic workcells, consisting of many cooperating elements (e.g., robots, sensing systems) makes programming applications enormously difficult. Even when the application can be easily decomposed into workcelll operations, the tight coupling among these operations due to constraints on precedence and resource sharing makes it difficult to identify and take advantage of possible concurrency. Since robotic workcells are typically configured to repetitively perform an application, we seek to minimize the ‘cycle’ time required, by optimizing the order in which operations take place. Eventually, a sequential program must be written for each workcell element, and they must then execute concurrently. Commercial robot languages and systems provide neither the necessary tools for programming such a distributed collection of elements nor a mechanism for inter‐element communication. In this article the authors propose a two‐part solution to this workcell programming problem: SAGE/WRAP. A graphics interface is provided to the user in order to define the application. Then, from the user's input, SAGE builds a Petri Net description of the workcell application; this is used to obtain a time‐optimal sequence of operations for each workcell element. The time‐optimal sequence then becomes the basis of the runtime program that performs the application. This program is interpreted and executed by the hierarchical workcell runtime environment called WRAP. An example is provided to illustrate t

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