The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Yeung Sam Hung - One of the best experts on this subject based on the ideXlab platform.

  • Stratified Self-Calibration and Metric Reconstruction for Zooming/Refocusing Circular Motion Sequences
    Journal of Mathematical Imaging and Vision, 2008
    Co-Authors: W. K. Tang, Yeung Sam Hung
    Abstract:

    Self-calibration for imaging sensors is essential to many computer vision applications. In this paper, a new stratified self-calibration and metric reconstruction method is proposed for zooming/refocusing cameras under Circular Motion. With the assumption of known rotation angles, the Circular Motion constraints are first formulated. By enforcing the constraints gradually, metric reconstruction is retrieved up to a two-parameter ambiguity. The closed form expression of the absolute conic w.r.t. the two parameters is deduced. The ambiguity is then resolved with the square pixel assumption of the camera. The advantages of this method are mainly as follows: It gives precise results by defining and enforcing the Circular Motion constraints; It is flexible that it allows both the focal lengths and the principal point to vary; It requires no scene constraint. Experimental results with both synthetic data and real images are presented, demonstrating the accuracy and robustness of the new method.

  • Multi-stage 3D reconstruction under Circular Motion
    Image and Vision Computing, 2007
    Co-Authors: Hong Zhong, Yeung Sam Hung
    Abstract:

    In this paper, we propose a new method for 3D reconstruction from an image sequence captured by a camera with constant intrinsic parameters undergoing Circular Motion. We introduce a method, called Circular projective reconstruction, for enforcing the Circular constraint in a factorization-based projective reconstruction. To deal with the missing data problem, our method uses a multi-stage approach to reconstructing the objects and cameras, which first computes a Circular projective reconstruction of a sub-sequence and then extends the reconstruction to the complete sequence. Camera matrix, rotation angles, and 3D structure are computed iteratively in a way that the 2D reprojection error is minimized. The algorithm is evaluated using real image sequences.

  • Self-calibration from one Circular Motion sequence and two images
    Pattern Recognition, 2006
    Co-Authors: Hong Zhong, Yeung Sam Hung
    Abstract:

    This paper describes a new method for self-calibration of camera with constant internal parameters under Circular Motion, using one sequence and two images captured with different camera orientations. Unlike the previous method, in which three Circular Motion sequences are needed with known Motion, the new method computes the rotation angles and the projective reconstructions of the sequence and the images with Circular constraint enforced, which is called a Circular projective reconstruction, using a factorization-based method. It is then shown that the images of the Circular points of each Circular projective reconstruction can be readily obtained. Subsequently, the image of the absolute conic and the calibration matrix of the camera can be determined. Experiments on both synthetic and real image sequence are given, showing the accuracy and robustness of the new algorithm.

  • BMVC - Factorization-based Hierarchical Reconstruction for Circular Motion
    Procedings of the British Machine Vision Conference 2004, 2004
    Co-Authors: Huang Zhong, Yeung Sam Hung
    Abstract:

    A new practical method is developed for 3D reconstruction from an image sequence captured by a camera with constant intrinsic parameters undergoing Circular Motion. We introduce a method for enforcing the Circular constraint in a factorization-based projective reconstruction. This is called a Circular projective reconstruction. Given a turntable sequence, our method uses a hierarchical approach to reconstructing the objects and cameras, which first computes a Circular projective reconstruction of a sub-sequence and then extends the reconstruction to the complete sequence. Camera matrix and the Motion parameters, i.e. the rotation angles, are computed iteratively in a way that minimizes the 2D reprojection error. Thus, an optimal reconstruction is obtained upon convergence. The algorithm is evaluated using real image sequence.

  • ICIP - A factorization-based projective reconstruction algorithm with Circular Motion constraint
    2004 International Conference on Image Processing 2004. ICIP '04., 1
    Co-Authors: W. K. Tang, Yeung Sam Hung
    Abstract:

    In this paper, we propose a projective reconstruction algorithm for a Circular Motion image sequence. We first formulate the Circular Motion constraint in the Euclidean frame, and then deduce its expression in a projective frame. The Circular Motion constraint is gradually enforced during the iterations of a projective reconstruction. This approach can be used to deal with both constant and varying intrinsic parameters. Experimental results for synthetic and real data are presented to illustrate the performance and improvements of our approach over methods based on general Motion.

Hong Zhong - One of the best experts on this subject based on the ideXlab platform.

  • Multi-stage 3D reconstruction under Circular Motion
    Image and Vision Computing, 2007
    Co-Authors: Hong Zhong, Yeung Sam Hung
    Abstract:

    In this paper, we propose a new method for 3D reconstruction from an image sequence captured by a camera with constant intrinsic parameters undergoing Circular Motion. We introduce a method, called Circular projective reconstruction, for enforcing the Circular constraint in a factorization-based projective reconstruction. To deal with the missing data problem, our method uses a multi-stage approach to reconstructing the objects and cameras, which first computes a Circular projective reconstruction of a sub-sequence and then extends the reconstruction to the complete sequence. Camera matrix, rotation angles, and 3D structure are computed iteratively in a way that the 2D reprojection error is minimized. The algorithm is evaluated using real image sequences.

  • Self-calibration from one Circular Motion sequence and two images
    Pattern Recognition, 2006
    Co-Authors: Hong Zhong, Yeung Sam Hung
    Abstract:

    This paper describes a new method for self-calibration of camera with constant internal parameters under Circular Motion, using one sequence and two images captured with different camera orientations. Unlike the previous method, in which three Circular Motion sequences are needed with known Motion, the new method computes the rotation angles and the projective reconstructions of the sequence and the images with Circular constraint enforced, which is called a Circular projective reconstruction, using a factorization-based method. It is then shown that the images of the Circular points of each Circular projective reconstruction can be readily obtained. Subsequently, the image of the absolute conic and the calibration matrix of the camera can be determined. Experiments on both synthetic and real image sequence are given, showing the accuracy and robustness of the new algorithm.

Kwanyee K Wong - One of the best experts on this subject based on the ideXlab platform.

  • 1d camera geometry and its application to the self calibration of Circular Motion sequences
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
    Co-Authors: Kwanyee K Wong, C. Liang, Guoqiang Zhang, Hui Zhang
    Abstract:

    This paper proposes a novel method for robustly recovering the camera geometry of an uncalibrated image sequence taken under Circular Motion. Under Circular Motion, all the camera centers lie on a circle and the mapping from the plane containing this circle to the horizon line observed in the image can be modelled as a 1D projection. A 2times2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged Circular points of the Motion plane and the rotation angle between the two views can be derived directly from such a homography. This way of recovering the imaged Circular points and rotation angles is intrinsically a multiple view approach, as all the sequence geometry embedded in the epipoles is exploited in the estimation of the homography for each view pair. This results in a more robust method compared to those computing the rotation angles using adjacent views only. The proposed method has been applied to self-calibrate turntable sequences using either point features or silhouettes, and highly accurate results have been achieved.

  • 1d camera geometry and its application to Circular Motion estimation
    British Machine Vision Conference, 2006
    Co-Authors: Guoqiang Zhang, Hui Zhang, Kwanyee K Wong
    Abstract:

    This paper describes a new and robust method for estimating Circular Motion geometry from an uncalibrated image sequence. Under Circular Motion, all the camera centers lie on a circle, and the mapping of the plane containing this circle to the horizon line in the image can be modelled as a 1D projection. A 2◊ 2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged Circular points and the rotation angle between the two views can be derived directly from the eigenvectors and eigenvalues of such a homography respectively. The proposed 1D geometry can be nicely applied to Circular Motion estimation using either point correspondences or silhouettes. The method introduced here is intrinsically a multiple view approach as all the sequence geometry embedded in the epipoles is exploited in the computation of the homography for a view pair. This results in a robust method which gives accurate estimated rotation angles and imaged Circular points. Experimental results are presented to demonstrate the simplicity and applicability of the new method.

  • BMVC - 1D Camera Geometry and Its Application to Circular Motion Estimation
    Procedings of the British Machine Vision Conference 2006, 2006
    Co-Authors: Guoqiang Zhang, Hui Zhang, Kwanyee K Wong
    Abstract:

    This paper describes a new and robust method for estimating Circular Motion geometry from an uncalibrated image sequence. Under Circular Motion, all the camera centers lie on a circle, and the mapping of the plane containing this circle to the horizon line in the image can be modelled as a 1D projection. A 2◊ 2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged Circular points and the rotation angle between the two views can be derived directly from the eigenvectors and eigenvalues of such a homography respectively. The proposed 1D geometry can be nicely applied to Circular Motion estimation using either point correspondences or silhouettes. The method introduced here is intrinsically a multiple view approach as all the sequence geometry embedded in the epipoles is exploited in the computation of the homography for a view pair. This results in a robust method which gives accurate estimated rotation angles and imaged Circular points. Experimental results are presented to demonstrate the simplicity and applicability of the new method.

  • Structure and Motion estimation from apparent contours under Circular Motion
    Image and Vision Computing, 2002
    Co-Authors: Kwanyee K Wong, Paulo R. S. Mendonça, Roberto Cipolla
    Abstract:

    In this paper we address the problem of recovering structure and Motion from the apparent contours of a smooth surface. Fixed image features under Circular Motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the Motion parameters, which all bear physical meanings. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only 2 epipolar tangents. In contrast to previous methods, the Motion estimation algorithm introduced here can cope with incomplete Circular Motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated Motion is presented.

Hideki Miyazaki - One of the best experts on this subject based on the ideXlab platform.

  • Circular Motion tests and uncertainty analysis for ship maneuverability
    Journal of Marine Science and Technology, 2009
    Co-Authors: Michio Ueno, Yasuo Yoshimura, Yoshiaki Tsukada, Hideki Miyazaki
    Abstract:

    Circular Motion test data and uncertainty analysis results of investigations of the hydrodynamic characteristics of ship maneuvering are presented. The model ships used were a container ship and two tankers, and the measured items were the surge and sway forces, yaw moment, propeller thrust, rudder normal and tangential forces, pitch and roll angles, and heave. The test parameters were the oblique angle and yaw rate for the conditions of a hull with a rudder and propeller in which the rudder angle was set to zero and the propeller speed was set to the model self-propulsion conditions. Carriage data showing the accuracy of the towing conditions in the Circular Motion test are also presented. It was confirmed that the uncertainties in the hydrodynamic forces such as the surge and sway forces, yaw moment, rudder tangential and normal forces, and propeller thrust were fairly small. The reported uncertainty analysis results of the Circular Motion test data may be beneficial in validating data quality and in discussing reliability for simulation of ship maneuvering performance.

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

  • 1d camera geometry and its application to the self calibration of Circular Motion sequences
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
    Co-Authors: Kwanyee K Wong, C. Liang, Guoqiang Zhang, Hui Zhang
    Abstract:

    This paper proposes a novel method for robustly recovering the camera geometry of an uncalibrated image sequence taken under Circular Motion. Under Circular Motion, all the camera centers lie on a circle and the mapping from the plane containing this circle to the horizon line observed in the image can be modelled as a 1D projection. A 2times2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged Circular points of the Motion plane and the rotation angle between the two views can be derived directly from such a homography. This way of recovering the imaged Circular points and rotation angles is intrinsically a multiple view approach, as all the sequence geometry embedded in the epipoles is exploited in the estimation of the homography for each view pair. This results in a more robust method compared to those computing the rotation angles using adjacent views only. The proposed method has been applied to self-calibrate turntable sequences using either point features or silhouettes, and highly accurate results have been achieved.

  • 1d camera geometry and its application to Circular Motion estimation
    British Machine Vision Conference, 2006
    Co-Authors: Guoqiang Zhang, Hui Zhang, Kwanyee K Wong
    Abstract:

    This paper describes a new and robust method for estimating Circular Motion geometry from an uncalibrated image sequence. Under Circular Motion, all the camera centers lie on a circle, and the mapping of the plane containing this circle to the horizon line in the image can be modelled as a 1D projection. A 2◊ 2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged Circular points and the rotation angle between the two views can be derived directly from the eigenvectors and eigenvalues of such a homography respectively. The proposed 1D geometry can be nicely applied to Circular Motion estimation using either point correspondences or silhouettes. The method introduced here is intrinsically a multiple view approach as all the sequence geometry embedded in the epipoles is exploited in the computation of the homography for a view pair. This results in a robust method which gives accurate estimated rotation angles and imaged Circular points. Experimental results are presented to demonstrate the simplicity and applicability of the new method.

  • BMVC - 1D Camera Geometry and Its Application to Circular Motion Estimation
    Procedings of the British Machine Vision Conference 2006, 2006
    Co-Authors: Guoqiang Zhang, Hui Zhang, Kwanyee K Wong
    Abstract:

    This paper describes a new and robust method for estimating Circular Motion geometry from an uncalibrated image sequence. Under Circular Motion, all the camera centers lie on a circle, and the mapping of the plane containing this circle to the horizon line in the image can be modelled as a 1D projection. A 2◊ 2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged Circular points and the rotation angle between the two views can be derived directly from the eigenvectors and eigenvalues of such a homography respectively. The proposed 1D geometry can be nicely applied to Circular Motion estimation using either point correspondences or silhouettes. The method introduced here is intrinsically a multiple view approach as all the sequence geometry embedded in the epipoles is exploited in the computation of the homography for a view pair. This results in a robust method which gives accurate estimated rotation angles and imaged Circular points. Experimental results are presented to demonstrate the simplicity and applicability of the new method.