# Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision
###### Abstract  
The wave parameter is an important environmental input condition. Traditional contact wave measurement methods are unable to meet the requirements of high precision, non-contact, and ship wave field assessment. Alternatively, stereo vision technology can realize a non-contact and mobile form of measurement. However, this technology suffers from poor timeliness and adaptability. This paper proposes a comprehensive wave measurement method that is based on stereo vision, wherein the gridding of siftGPU is used to achieve the fast matching of large images. The whole algorithm can be run within 6 s and it guarantees more than 20,000 feature-matching logarithms. Furthermore, by utilizing the least squares method and sea surface wave surface theory, the sea surface base level can be calculated without control points, along with the inversion of the sea wave parameters (wave height, period, and wave direction) and error point fitting. The rationality and superiority of the algorithm were verified through multiple comparison experiments. Compared with the Richard Brancker Research (RBR) wave height meter, the measurement error of the wave height is less than 10%, the period error is less than 0.5 s, and the wave direction error is less than 10\({}^{\circ}\) with the proposed method. stereo vision; gridding siftGPU; wave height; period; wave direction +
Footnote †: journal: Medicalfluctuations through the principle of image matching and lens imaging and it obtains the direction spectrum [8]. This technology can effectively make up for the deficiencies of buoys and the remote sensing observation method. Therefore, the development of stereo vision wave measurement technology has important scientific significance and a practical application value for the theoretical development of ocean waves and their application in ocean remote sensing and ocean engineering [9]. The direct linear transformation (DLT) theory was proposed by [PERSON] in 1971; it takes several or even ten days for each pair of images to be completely analyzed, thereby affecting the post-processing time [10]. In 2009, [PERSON] et al. developed a stereo-photographic ocean wave measurement system and performed an experiment on the coast of Schevenin-gen [11]. They successfully measured the evolution of waves and they found that the measured sea surface area reached 1800 m\({}^{2}\)[12]. Furthermore, in 2011, [PERSON] developed a fully automated trinocular photography ocean wave observation system (ATSIS) [13]. Different from the other photography systems, ATSIS uses three synchronized cameras to form an image acquisition system in the shape of a \"product\" and it simulates the measurement scene through the previously recorded camera frame's orientation in the laboratory in order to calibrate the external orientation elements. The ATSIS calibration process is cumbersome and the measurement area is limited by the camera's frame, indeed the ATSIS measurement area is only about 10 m\({}^{2}\)[14]. In 2010, [PERSON] used three synchronous cameras to form an image acquisition system in the shape of a \"pin\" [15] and then obtained the coordinates of the control point through the means of assessing the underwater makeup. It was necessary to set the calibration points with the current pool and the overall operation was complex [16]. In 2011, [PERSON] of Henan University captured a target image of the Yellow River model water flow vertically through a binocular system. By combining the improved Canny edge detection algorithm with the morphological connected domain segmentation method, he finally extracted and identified the river's potential width and the flow velocity of the Yellow River model [17]. In 2012, [PERSON], through his digital photogrammetry wave measurement system, reported a good study on the feature matching of left and right images [18]. [PERSON]'s corner detector is a corner detection operator that is based on gray variance that extracts the points with the maximum and minimum gray variance in four main directions as the feature points [19].