The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform

Lepeng Song - One of the best experts on this subject based on the ideXlab platform.

  • ICCI*CC - Study of variable spray control system based on machine vision
    2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, 2014
    Co-Authors: Rui Zhang, Lepeng Song
    Abstract:

    This system captures and analyses the growth of crops based on machine vision technology, controlled by the PLC variables and achieve the objective of saving fertilizer, improving economic efficiency and protecting the environment. It uses shape or texture of the crops and background information contained in the image to classify, builds database algorithms, processes the collecting real-time signal by the Internal Computer and provides a spray flow rate required by the target area of operation, thus achieves variable spray to the target automatically. As a result, the real-time database of the precision agriculture variable spray is created; the system then receives the target spray flow signals on the intelligent platform and variable sprays with an intelligent spray operation platform speed.

  • Study of variable spray control system based on machine vision
    2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, 2014
    Co-Authors: Rui Zhang, Lepeng Song
    Abstract:

    This system captures and analyses the growth of crops based on machine vision technology, controlled by the PLC variables and achieve the objective of saving fertilizer, improving economic efficiency and protecting the environment. It uses shape or texture of the crops and background information contained in the image to classify, builds database algorithms, processes the collecting real-time signal by the Internal Computer and provides a spray flow rate required by the target area of operation, thus achieves variable spray to the target automatically. As a result, the real-time database of the precision agriculture variable spray is created; the system then receives the target spray flow signals on the intelligent platform and variable sprays with an intelligent spray operation platform speed.

Alexandre Bernardino - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Towards markerless visual servoing of grasping tasks for humanoid robots
    2017 IEEE International Conference on Robotics and Automation (ICRA), 2017
    Co-Authors: Pedro Vicente, Lorenzo Jamone, Alexandre Bernardino
    Abstract:

    Vision-based grasping for humanoid robots is a challenging problem due to a multitude of factors. First, humanoid robots use an “eye-to-hand” kinematics configuration that, on the contrary to the more common “eye-in-hand” configuration, demands a precise estimate of the position of the robot's hand. Second, humanoid robots have a long kinematic chain from the eyes to the hands, prone to accumulate the calibration errors of the kinematics model, which offsets the measured hand-to-object relative pose from the real one. In this paper, we propose a method able to solve these two issues jointly. A robust pose estimation of the robot's hand is achieved via a 3D model-based stereo-vision algorithm, using an edge-based distance transform metric and synthetically generated images of a robot's arm-hand Internal Computer-graphics model (kinematics and appearance). Then, a particle-based optimization method adapts on-line the robot's Internal model to match the real and the synthetically generated images, effectively compensating the kinematics calibration errors. We evaluate the proposed approach using a position-based visual-servoing method on the iCub robot, showing the importance of the continuous visual feedback in humanoid grasping tasks.

  • Towards markerless visual servoing of grasping tasks for humanoid robots
    2017 IEEE International Conference on Robotics and Automation (ICRA), 2017
    Co-Authors: Pedro Vicente, Lorenzo Jamone, Alexandre Bernardino
    Abstract:

    Vision-based grasping for humanoid robots is a challenging problem due to a multitude of factors. First, humanoid robots use an “eye-to-hand” kinematics configuration that, on the contrary to the more common “eye-in-hand” configuration, demands a precise estimate of the position of the robot's hand. Second, humanoid robots have a long kinematic chain from the eyes to the hands, prone to accumulate the calibration errors of the kinematics model, which offsets the measured hand-to-object relative pose from the real one. In this paper, we propose a method able to solve these two issues jointly. A robust pose estimation of the robot's hand is achieved via a 3D model-based stereo-vision algorithm, using an edge-based distance transform metric and synthetically generated images of a robot's arm-hand Internal Computer-graphics model (kinematics and appearance). Then, a particle-based optimization method adapts on-line the robot's Internal model to match the real and the synthetically generated images, effectively compensating the kinematics calibration errors. We evaluate the proposed approach using a position-based visual-servoing method on the iCub robot, showing the importance of the continuous visual feedback in humanoid grasping tasks.

Rui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • ICCI*CC - Study of variable spray control system based on machine vision
    2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, 2014
    Co-Authors: Rui Zhang, Lepeng Song
    Abstract:

    This system captures and analyses the growth of crops based on machine vision technology, controlled by the PLC variables and achieve the objective of saving fertilizer, improving economic efficiency and protecting the environment. It uses shape or texture of the crops and background information contained in the image to classify, builds database algorithms, processes the collecting real-time signal by the Internal Computer and provides a spray flow rate required by the target area of operation, thus achieves variable spray to the target automatically. As a result, the real-time database of the precision agriculture variable spray is created; the system then receives the target spray flow signals on the intelligent platform and variable sprays with an intelligent spray operation platform speed.

  • Study of variable spray control system based on machine vision
    2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, 2014
    Co-Authors: Rui Zhang, Lepeng Song
    Abstract:

    This system captures and analyses the growth of crops based on machine vision technology, controlled by the PLC variables and achieve the objective of saving fertilizer, improving economic efficiency and protecting the environment. It uses shape or texture of the crops and background information contained in the image to classify, builds database algorithms, processes the collecting real-time signal by the Internal Computer and provides a spray flow rate required by the target area of operation, thus achieves variable spray to the target automatically. As a result, the real-time database of the precision agriculture variable spray is created; the system then receives the target spray flow signals on the intelligent platform and variable sprays with an intelligent spray operation platform speed.

Pedro Vicente - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Towards markerless visual servoing of grasping tasks for humanoid robots
    2017 IEEE International Conference on Robotics and Automation (ICRA), 2017
    Co-Authors: Pedro Vicente, Lorenzo Jamone, Alexandre Bernardino
    Abstract:

    Vision-based grasping for humanoid robots is a challenging problem due to a multitude of factors. First, humanoid robots use an “eye-to-hand” kinematics configuration that, on the contrary to the more common “eye-in-hand” configuration, demands a precise estimate of the position of the robot's hand. Second, humanoid robots have a long kinematic chain from the eyes to the hands, prone to accumulate the calibration errors of the kinematics model, which offsets the measured hand-to-object relative pose from the real one. In this paper, we propose a method able to solve these two issues jointly. A robust pose estimation of the robot's hand is achieved via a 3D model-based stereo-vision algorithm, using an edge-based distance transform metric and synthetically generated images of a robot's arm-hand Internal Computer-graphics model (kinematics and appearance). Then, a particle-based optimization method adapts on-line the robot's Internal model to match the real and the synthetically generated images, effectively compensating the kinematics calibration errors. We evaluate the proposed approach using a position-based visual-servoing method on the iCub robot, showing the importance of the continuous visual feedback in humanoid grasping tasks.

  • Towards markerless visual servoing of grasping tasks for humanoid robots
    2017 IEEE International Conference on Robotics and Automation (ICRA), 2017
    Co-Authors: Pedro Vicente, Lorenzo Jamone, Alexandre Bernardino
    Abstract:

    Vision-based grasping for humanoid robots is a challenging problem due to a multitude of factors. First, humanoid robots use an “eye-to-hand” kinematics configuration that, on the contrary to the more common “eye-in-hand” configuration, demands a precise estimate of the position of the robot's hand. Second, humanoid robots have a long kinematic chain from the eyes to the hands, prone to accumulate the calibration errors of the kinematics model, which offsets the measured hand-to-object relative pose from the real one. In this paper, we propose a method able to solve these two issues jointly. A robust pose estimation of the robot's hand is achieved via a 3D model-based stereo-vision algorithm, using an edge-based distance transform metric and synthetically generated images of a robot's arm-hand Internal Computer-graphics model (kinematics and appearance). Then, a particle-based optimization method adapts on-line the robot's Internal model to match the real and the synthetically generated images, effectively compensating the kinematics calibration errors. We evaluate the proposed approach using a position-based visual-servoing method on the iCub robot, showing the importance of the continuous visual feedback in humanoid grasping tasks.

Tomas Olovsson - One of the best experts on this subject based on the ideXlab platform.

  • VTC-Fall - In-Vehicle CAN Message Authentication: An Evaluation Based on Industrial Criteria
    2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017
    Co-Authors: Nasser Nowdehi, Aljoscha Lautenbach, Tomas Olovsson
    Abstract:

    Vehicles have evolved from mostly mechanical machines into devices controlled by an Internal Computer network consisting of more than 100 interconnected Electronic Control Units (ECUs). Moreover, modern vehicles communicate with external devices to enable new features, but these new communication facilities also expose safety-critical functions to security threats. As the most prevalent automotive bus, the Controller Area Network (CAN) bus is a prime target for attacks. Even though the Computer security community has proposed several message authentication solutions to alleviate those threats, such solutions have not yet been widely adopted by the automotive industry. We have identified the most promising CAN message authentication solutions and provide a comprehensive overview of them. In order to investigate the lack of adoption of such solutions, we, together with industry experts, have identified five general requirements they must fulfill in order to be considered viable in industry. Based on those requirements, we analyze and evaluate the identified authentication solutions. We find that none of them meet all the requirements, and that backward compatibility and acceptable overhead are the biggest obstacles.

  • In-Vehicle CAN Message Authentication: An Evaluation Based on Industrial Criteria
    2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017
    Co-Authors: Nasser Nowdehi, Aljoscha Lautenbach, Tomas Olovsson
    Abstract:

    Vehicles have evolved from mostly mechanical machines into devices controlled by an Internal Computer network consisting of more than 100 interconnected Electronic Control Units (ECUs). Moreover, modern vehicles communicate with external devices to enable new features, but these new communication facilities also expose safety-critical functions to security threats. As the most prevalent automotive bus, the Controller Area Network (CAN) bus is a prime target for attacks. Even though the Computer security community has proposed several message authentication solutions to alleviate those threats, such solutions have not yet been widely adopted by the automotive industry. We have identified the most promising CAN message authentication solutions and provide a comprehensive overview of them. In order to investigate the lack of adoption of such solutions, we, together with industry experts, have identified five general requirements they must fulfill in order to be considered viable in industry. Based on those requirements, we analyze and evaluate the identified authentication solutions. We find that none of them meet all the requirements, and that backward compatibility and acceptable overhead are the biggest obstacles.