The Experts below are selected from a list of 62067 Experts worldwide ranked by ideXlab platform

Naokazu Yokoya - One of the best experts on this subject based on the ideXlab platform.

  • real time Camera Position and posture estimation using a feature landmark database with priorities
    International Conference on Pattern Recognition, 2008
    Co-Authors: Takafumi Taketomi, Tomokazu Sato, Naokazu Yokoya
    Abstract:

    In the field of computer vision, many kinds of Camera parameter estimation methods have been proposed. As one of these methods, an extrinsic Camera parameter estimation method that uses pre-constructed feature landmark database has been studied. In this method, extrinsic Camera parameters of video images are estimated from correspondences between landmarks and image features. Although this method can work in a large outdoor environment, its computational cost in matching process is expensive and it cannot work in real-time. In this paper, to achieve real-time Camera parameter estimation, the number of matching candidates are reduced by using priorities of landmarks that are determined from previously captured video sequences.

  • estimating Camera Position and posture by using feature landmark database
    Scandinavian Conference on Image Analysis, 2005
    Co-Authors: Tomokazu Sato, Naokazu Yokoya
    Abstract:

    Estimating Camera Position and posture can be applied to the fields of augmented reality and robot navigation. In these fields, to obtain absolute Position and posture of the Camera, sensor-based methods using GPS and magnetic sensors and vision-based methods using input images from the Camera have been investigated. However, sensor-based methods are difficult to synchronize the Camera and sensors accurately, and usable environments are limited according to selection of sensors. On the other hand, vision-based methods need to allocate many artificial markers otherwise an estimation error will accumulate. Thus, it is difficult to use such methods in large and natural environments. This paper proposes a vision-based Camera Position and posture estimation method for large environments, which does not require sensors and artificial markers by detecting natural feature points from image sequences taken beforehand and using them as landmarks.

  • Camera Position and posture estimation based on feature landmark database
    2005
    Co-Authors: O E Motoko, Tomokazu Sato, Naokazu Yokoya
    Abstract:

    Estimating Camera Position and posture can be applied to the fields of augmented reality and robot navigation. In these fields, to obtain absolute Position and posture of the Camera, sensor-based methods using GPS and magentic sensors and vision-based methods using input images from the Camera have been investigated. How- ever, sensor-based methods are difficult to synchronize the Camera and sensors accurately, and usable environments are limited according to selection of sensors. On the other hand, vision-based methods needs to allocate many artificial markers otherwise an estimation error will accumulate. Thus, it is difficult to use such methods in large and natural environments. This paper proposes a vision-based Camera Position and posture estimation method for large environments, which does not require sensors and artificial markers by detecting natural feature points from

Heiko Hecht - One of the best experts on this subject based on the ideXlab platform.

Wen -hsiang Tsai - One of the best experts on this subject based on the ideXlab platform.

  • Camera calibration by vanishing lines for 3 d computer vision
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991
    Co-Authors: Ling -ling Wang, Wen -hsiang Tsai
    Abstract:

    A novel approach to Camera calibration by vanishing lines is proposed. Calibrated parameters include the orientation, Position, and focal length of a Camera. A hexagon is used as the calibration target to generate a vanishing line of the ground plane from its projected image. It is shown that the vanishing line includes useful geometric hints about the Camera orientation parameters and the focal length, from which the orientation parameters can be solved easily and analytically. And the Camera Position parameters can be calibrated by the use of related geometric projective relationships. The simplicity of the target eliminates the complexity of the environment setup and simplifies the feature extraction in relevant image processing. The calibration formulas are also simple to compute. Experimental results show the feasibility of the proposed approach. >

Tomi Westerlund - One of the best experts on this subject based on the ideXlab platform.

  • detecting water reflection symmetries in point clouds for Camera Position calibration in unmanned surface vehicles
    International Symposium on Communications and Information Technologies, 2019
    Co-Authors: Li Qingqing, Pena J Queralta, Nguyen T Gia, Zhuo Zou, Hannu Tenhunen, Tomi Westerlund
    Abstract:

    The development of autonomous vehicles has seen considerable advances over the past decade. However, specific challenges remain in the area of autonomous waterborne navigation. Two key aspects in autonomous surface vehicles are sensor calibration and segmentation of water surface. Cameras and other sensors in a car or drone can be installed accurately in a specific Position and orientation. In a large vessel, this is not always possible, as sensors might be installed around the vessel or on masts. Taking advantage of the medium in which these vehicles operate, the water plane can be used as a reference for different sensors to calibrate their orientation. This allows more accurate localization of obstacles of objects. State-of-the-art deep learning techniques have been successfully applied for water surface segmentation in open sea. However, in other environments such as small rivers or lakes with still waters, a different approach might enable more accurate water surface estimation. We propose a method to estimate the water plane based on the detection of local symmetry planes that naturally occur when objects are reflected in the water. By using a point cloud generated with a stereo Camera, we are able to accurately estimate the water level and, at the same time, calibrate the Camera Position and improve the localization of obstacles. We assume that an approximate Position and orientation of the Camera is known with respect to the sea level. We demonstrate the efficiency of our method with data obtained in the Aura river, Finland, with our prototype vessel facing both the riverside and the center of the river.

Christoph Bernhard - One of the best experts on this subject based on the ideXlab platform.