Li Jiakuan, Sun Chunsheng, Hu Yiming, et al. An underwater stereo matching method based on ORB features[J]. Opto-Electronic Engineering, 2019, 46(4): 180456. doi: 10.12086/oee.2019.180456
Citation: Li Jiakuan, Sun Chunsheng, Hu Yiming, et al. An underwater stereo matching method based on ORB features[J]. Opto-Electronic Engineering, 2019, 46(4): 180456. doi: 10.12086/oee.2019.180456

An underwater stereo matching method based on ORB features

    Fund Project: Supported by the 13th Five-Year Plan Pre-research Project Fund
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  • Since the traditional algorithm may cause problems such as slow running speed and more mismatching points when perform stereo matching on underwater environment, the ORB characteristics detection and curve restriction has been applied in this paper. Firstly the image should be detected so as to find out the characteristics, generate the descriptor, and match the feature points. Then the underwater curve restriction can be deduced according to the law of refraction combining internal and external parameters of camera. Finally the mismatching points can be decreased by means of underwater curve restriction. The experimental results have shown that in the case of effectively controlling mismatches, the speed of this algorithm are faster than traditional SIFT algorithm combined with curve restriction. As a result, it is of practical significance to improve the speed of underwater binocular vision system.
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  • Overview: With the exploration of Marine resources, the underwater vision technology has a very broad application prospect. As a main branch of computer vision, binocular stereo vision has become a common realization form of underwater vision technology due to its relatively simple structure and high efficiency. Among the realization steps of image acquisition, camera calibration, image preprocessing, stereo matching and 3D reconstruction, stereo matching is a key technology and a hot spot in binocular vision field. How to get a matching algorithm with high precision and high speed is more difficult to study. At present, the underwater stereo matching method adopts adaptive search, determines the optimal search domain, and is based on color segmentation, but only takes into account the calculation accuracy. For example, in previous literature, SIFT feature matching and curve constraint were adopted to further improve accuracy, but the calculation speed was not discussed. In this paper, a matching method is proposed to improve the operation speed under the premise of ensuring accuracy.

    According to different matching primitives, stereo matching is mainly divided into regional matching and feature matching. The region-based matching algorithm takes parallax for each pixel, and finally gets the dense parallax map. While the feature-based matching algorithm extracts feature points for matching, and finally gets the sparse parallax map. Due to problems such as low illumination and high noise in underwater images, which will cause large errors in solving dense parallax plan. Thus, feature matching algorithm that is insensitive to noise and environment is selected in this paper. Commonly used feature matching algorithms include SURF, KAZA, SIFT, ORB, etc., among which SIFT algorithm is the most classic one. This algorithm has many advantages, such as good stability, high precision and strong robustness, which are widely used in many scenes. But at the same time, there are problems such as large calculation and long time. After SIFT, an ORB algorithm is also proposed. ORB is an algorithm for rapid feature extraction and matching. Although there are some problems such as low accuracy and many mismatches in complex scenes with large changes such as scale and illumination, the computing speed is greatly improved compared with the previous algorithm, and some scenes can be two orders of magnitude higher than the SIFT algorithm. Although the velocity of ORB algorithm is improved, there are a series of factors affecting the image quality, such as refraction, lens distortion, etc., in the underwater environment. If it is used directly, there will be many mismatches. For the accuracy problem, the curvilinear polar constraint commonly used in underwater stereo matching is derived under the circumstance that underwater environment does not meet the polar constraint in air, which is applicable to underwater curve polar constraint, so as to eliminate the mismatched points and ensure the matching accuracy.

    This paper adopts underwater stereo matching method combining ORB feature detection and curve polar line constraint. Firstly, it used ORB feature detection algorithm to match feature points of underwater image, and then reduced mismatched points according to curve polar line constraint, so as to achieve the goal of improving speed under the premise of ensuring accuracy.

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    沈阳化工大学材料科学与工程学院 沈阳 110142

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