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The appearance of the inside of a running pressure flow pipe, when viewed through a photo-lens, is often blurred and deformed by the influence of fluid pressure, temperature and type. This study proposes an image pre-processing method based on self-scoring restoration and self-calibration to solve the problems and make it adaptable to the complicated environments inside the pipe. The method consists of two stages, in the first stage, a restoration method based on Wiener filter is used to work with the defined merit functions to deal with the degenerated images, in the second stage, two images taken from different depths inside the pipe are used to calculate the distortion parameters according to the matching points obtained from those two pictures. The experiment results show the proposed method performs well in clarity and contrast and removes the distortion effectively.

The main objective of this study was to develop a 3D reconstruction technique of the spine and rib cage of idiopathic scoliotic patients using the self-calibration of the imaging system. The proposed approach computes the intrinsic and extrinsic parameters of the radiographic setup with respect to the global coordinate system used at Ste-Justine Hospital. Our approach determines an optimal estimate of the geometrical parameters of the imaging system from a nonlinear minimization of the mean square distance between the observed and analytical projections of a set of matched points identified on a pair of radiographic views. The accuracy of the optimal estimate for the intrinsic parameters was significantly improved when geometric knowledge such as the known length of detectable straight bars is incorporated as a set of equality constraints in the optimization process. Furthermore, in order to retrieve the 3D structure of interest in the global coordinate system, a reference plane including the origin of the global coordinate system is specified. Computer simulations were performed to evaluate the self-calibration procedure and to determine the minimum knowledge required to obtain an accurate 3D reconstruction for clinical applications. An in vitro validation on real images of a dry cadaveric human spine showed that the method is feasible and reaches the expected accuracy.

Phase error of the demodulation clock in the Coriolis vibratory gyroscope system allows the quadrature errors to leak into the sense channel and causes significant bias and temperature drift at the rate output. A phase self-correction method to suppress the temperature drift of the bias in gyroscopes is proposed. Through sweeping the demodulation clock phase and simultaneously monitoring the mechanical quadrature error output in gyroscopes, the optimal demodulation clock phase with minimum relatively phase shift is determined. Thus the bias influenced by the temperature and surroundings can be calibrated on-chip at start-up or when the environment changes drastically without the requirement of the complicated instruments. The proposed approach is validated by a silicon MEMS gyroscope with the natural frequency of 2.8kHz, which shows nearly 22 times improvement in the temperature sensitivity of the system bias, from 550mdeg/s/^∘C down to 24.7mdeg/s/^∘C.

In this paper, a novel parallel-coupled quadrature LC oscillator (PC-QO) is presented that can automatically eliminate imbalances of output phase and amplitude resulting from mismatches in the LC tanks. The method of using unequal coupling factors in a parallel-coupled quadrature oscillator is the base of this design; canceling the phase and amplitude errors takes place by tuning the imbalanced coupling factor using tuneable tail currents. First, the proposed circuit senses the amplitude error and then adjusts the coupling factors according to the situation. We show that using the inversely proportional coupling factors can eliminate the phase and amplitude error simultaneously. In other words, this design uses the amplitude control loop to cancel the phase and amplitude error. The circuit has been simulated using TSMC 0.18 CMOS practical model to confirm the high accuracy of the analysis and capability of the canceling $I$/$Q$ mismatch technique. The simulation results show that the phase noise of the proposed quadrature voltage controlled oscillator (QVCO) is −123.9 dBc/Hz at 1MHz offset from 4.4GHz operation frequency. The total power consumption of the QVCO is 3.4mW.

This paper presents a new approach for reconstructing realistic 3D models of buildings from uncalibrated image sequences taken by a hand-held camera. Firstly, correspondences between image pairs are established by using various computer vision tools, and then the fundamental matrix is estimated to high accuracy. Meanwhile, homography constraints are exploited to find more correspondences, to avoid degenerate cases and to obtain more accurate results. Secondly, rectified image pairs are resampled by using epipolar geometry constraints, where epipolar lines coincide with image scan-lines and disparities between the images are in the x-direction only. This allows subsequent stereoscopic analysis algorithms to easily take advantage of the epipolar constraint and reduce the search space to one dimension, namely along the horizontal row of the rectified images. Furthermore, dense stereo matching of the original image pairs is simple and low computational cost. Finally, the 3D model can be built through self-calibration, matching and Delaunay triangulation. The self-calibration method uses prior knowledge of orthogonal planes (lines) and parallel planes (lines) to act as constraints on the absolute quadric. A large number of experimental results show that this method improves the speed and accuracy of reconstructed 3D models and the 3D models obtained are more realistic.

The accuracy of feature-based vision algorithms, including the self-calibration of binocular camera extrinsic parameters used in autonomous driving environment perception techniques relies heavily on the quality of the features extracted from the images. This study investigates the influence of the depth distance between objects and the camera, the feature points in different object regions, and the feature points in dynamic object regions on the self-calibration of binocular camera extrinsic parameters. To achieve this, the study first filters out different types of objects in the image through semantic segmentation. Then, it identifies the areas of dynamic objects and extracts the feature points in the static object region for the self-calibration of binocular camera extrinsic parameters. By calculating the baseline error of the binocular camera and the row alignment error of the matching feature points, this study evaluates the influence of feature points in dynamic object regions, feature points in different object regions, and feature points at different distances on the self-calibration algorithm. The experimental results demonstrate that feature points at static objects close to the camera are beneficial for the self-calibration of extrinsic parameters of binocular camera.

For 3D Euclidean reconstruction, the challenging problem is that only the hypothesis of intrinsic parameters can be used to retrieve the camera parameters without additional information. In this paper, we propose a method to find out the intrinsic parameters of a camera using the rank constraint of the relation matrix of absolute conic Ω. The degenerative problems and the problem of variable internal parameters are also studied. The experimental results presented show the good performance compared with the results of other methods for the self-calibration of a camera.

For 3D Eucledean reconstruction, the challenging problem is that only the hypothesis of intrinsic parameters can be used to retrieve the camera parameters without additional information. In this paper, we propose a method to find out the camera's variable intrinsic parameters using the scene invariable conics (SIC). The experiment results are presented and analysed, which show good performance of this proposed method.

One of the challenging problems of 3D Euclidean reconstruction is that only the hypothesis of intrinsic parameters can be used to retrieve the camera parameters. In this paper, we proposed a method to find out the intrinsic parameters of a camera using the rank constraint of the relation matrix of absolute conic Ω. Simultaneously, analyzed the degeneration of self-calibration. The experimental results showed that the self-calibration method of camera is better than the other methods.

Stereo rig with wide baseline is necessary when accurate depth estimation for distant object is desired. However, in order to make calibration pattern to be viewed from both left and right cameras, the wider the baseline the bigger the calibration pattern is required.

In contrast to the traditional stereo calibration method using calibration pattern, we propose a self-calibration approach that can estimate cameras' rotation matrices for stereo rig with wide baseline (3 m). Given images taken from left and right cameras, the relative roll and pitch angles between two cameras are recovered by aligning sea horizon in left and right images. The pitch angle is estimated by making the projections of one point at infinite distance appear at the same location in both images. A photometric minimization is applied to refine the rotation parameters. Compared with conventional checkerboard-based calibration techniques which require extra equipments or personnel, our approach only needs a pair of sea images. Moreover, unlike most self-calibration approaches, feature detection and matching are not required which makes it possible to apply our approach on featureless images. As a result, it is flexible and easy to implement our approach on sea surface images. Real world experiments demonstrate the feasibility of our approach.

The main objective of this study was to develop a 3D reconstruction technique of the spine and rib cage of idiopathic scoliotic patients using the self-calibration of the imaging system. The proposed approach computes the intrinsic and extrinsic parameters of the radiographic setup with respect to the global coordinate system used at Ste-Justine Hospital. Our approach determines an optimal estimate of the geometrical parameters of the imaging system from a nonlinear minimization of the mean square distance between the observed and analytical projections of a set of matched points identified on a pair of radiographic views. The accuracy of the optimal estimate for the intrinsic parameters was significantly improved when geometric knowledge such as the known length of detectable straight bars is incorporated as a set of equality constraints in the optimization process. Furthermore, in order to retrieve the 3D structure of interest in the global coordinate system, a reference plane including the origin of the global coordinate system is specified. Computer simulations were performed to evaluate the self-calibration procedure and to determine the minimum knowledge required to obtain an accurate 3D reconstruction for clinical applications. An in vitro validation on real images of a dry cadaveric human spine showed that the method is feasible and reaches the expected accuracy.