Traditional matching algorithms cannot be directly applied to the fisheye image matching for large distortion existing in fisheye image. Therefore, a matching algorithm based on uncorrected fisheye images is proposed. This algorithm adopts a local feature description method which combines MSER detector with CSLBP descriptor to obtain the image feature. First, the two uncorrected fisheye images captured by binocular vision system are described by the principle of epipolar constraint. Then the region detection is done with MSER and the ellipse fitting is used to the obtained regions. The MSER regions are described by CSLBP subsequently. Finally, in order to exclude the mismatching points of initial match, random sample consensus (RANSAC) algorithm has been adopted to achieve exact match. Experiments show that the method has a good effect on the uncorrected fisheye image matching.
Accurate measurement of sound speed is important to calibrate a sound velocity profiler which provides real-time sound velocity to the sonar equipment in oceanographic survey. The sound velocity profiler calculates the sound speed by measuring the time-of-flight of a 1 MHz single acoustic pulse to travel over about 300 mm path. A standard sound velocimeter instrument was invited to calibrate the sound velocity profiler in pure water at temperatures of 278,283, 288, 293, 298, 303 and 308K in a thermostatic vessel at one atmosphere. The sound velocity profiler was deployed in the thermostatic vessel alongside the standard sound velocimeter instrument and two platinum resistance thermometers (PRT) which were calibrated to 0.002k by comparison with a standard PRT. Time of flight circuit board was used to measure the time-of-flight to 22 picosecond precision. The sound speed which was measured by the sound velocity profiler was compared to the standard sound speed calculated by UNESCO to give the laboratory calibration coefficients and was demonstrated agreement with CTD-derived sound speed using Del Grosso's seawater equation after removing a bias.
Spherical stereo vision is a kind of stereo vision system built by fish-eye lenses, which discussing the stereo algorithms
conform to the spherical model. Epipolar geometry is the theory which describes the relationship of the two imaging
plane in cameras for the stereo vision system based on perspective projection model. However, the epipolar in
uncorrected fish-eye image will not be a line but an arc which intersects at the poles. It is polar curve. In this paper, the
theory of nonlinear epipolar geometry will be explored and the method of nonlinear epipolar rectification will be
proposed to eliminate the vertical parallax between two fish-eye images. Maximally Stable Extremal Region (MSER)
utilizes grayscale as independent variables, and uses the local extremum of the area variation as the testing results. It is
demonstrated in literatures that MSER is only depending on the gray variations of images, and not relating with local
structural characteristics and resolution of image. Here, MSER will be combined with the nonlinear epipolar rectification
method proposed in this paper. The intersection of the rectified epipolar and the corresponding MSER region is
determined as the feature set of spherical stereo vision. Experiments show that this study achieved the expected results.