Research on stereo matching methods for long distance sea surface image

2019 ◽  
Author(s):  
Ying Yang ◽  
Cunwei Lu ◽  
Chenhao Chen
Keyword(s):  
Stereo Matching ◽  
Sea Surface ◽  
Long Distance ◽  
Matching Methods ◽  
Surface Image

scholarly journals A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

2021 ◽  
Vol 9 (11) ◽  
pp. 1281
Author(s):  
Ying Yang ◽  
Cunwei Lu
Keyword(s):  
Stereo Matching ◽  
Sea Surface ◽  
Prediction Errors ◽  
Sea Waves ◽  
Measurement Instruments ◽  
Matching Method ◽  
Search Range ◽  
Tsunami Forecast ◽  
Long Distance ◽  
Binocular Stereo Vision

Tsunamis are some of the most destructive natural disasters. Some proposed tsunami measurement and arrival prediction systems use a limited number of instruments, then judge the occurrence of the tsunami, forecast its arrival time, location and scale. Since there are a limited number of measurement instruments, there is a possibility that large prediction errors will occur. In order to solve this problem, a long-distance tsunami measurement system based on the binocular stereo vision principle is proposed in this paper. The measuring range is 4–20 km away from the system deployment site. In this paper, we will focus on describing the stereo matching method for the proposed system. This paper proposes a two-step matching method. It first performs fast sparse matching, and then complete high precision dense matching based on the results of the sparse matching. A matching descriptor based on the physical features of sea waves is proposed to solve the matching difficulty caused by the similarity of sea surface image textures. The relationship between disparity and the y coordinate is built to reduce the matching search range. Experiments were conducted on sea surface images with different shooting times and distances; the results verify the effectiveness of the presented method.

An evaluation of stereo matching methods for view interpolation

2013 ◽  
Author(s):  
G. Fuhr ◽  
G. P. Fickel ◽  
L. P. Dal'Aqua ◽  
C. R. Jung ◽  
T. Malzbender ◽  
...  
Keyword(s):  
Stereo Matching ◽  
View Interpolation ◽  
Matching Methods

scholarly journals BOUNDARY DEPTH INFORMATION USING HOPFIELD NEURAL NETWORK

2016 ◽  
Vol XLI-B5 ◽  
pp. 139-146
Author(s):  
Sheng Xu ◽  
Ruisheng Wang
Keyword(s):  
Neural Network ◽  
Energy Function ◽  
Stereo Matching ◽  
Boundary Energy ◽  
Region Of Interest ◽  
Hopfield Neural Network ◽  
Depth Information ◽  
Matching Methods ◽  
The Difference ◽  
Smoothness Term

Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.

Influence of Reflections from Sea Surface on Long-Distance Tropospheric Scattering

2003 ◽  
Vol 59 (7-9) ◽  
pp. 5
Author(s):  
I. M. Mytsenko ◽  
A. V. Zatserlyanaya ◽  
D. D. Khalameyda
Keyword(s):  
Sea Surface ◽  
Long Distance

Measurement Error Correction of y Coordinate in Three Dimensional Localization of Tomatoes Using Binocular Stereo Vision

2014 ◽  
Vol 610 ◽  
pp. 209-215 ◽  
Author(s):  
Rong Xiang ◽  
Huan Yu Jiang ◽  
Yi Bin Ying
Keyword(s):  
Linear Regression ◽  
Measurement Errors ◽  
Regression Models ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Linear Regression Models ◽  
Matching Methods ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Binary Linear Regression

Accuracy three dimensional coordinates of fruits and vegetables are very important to harvesting robots to harvest fruits and vegetables correctly. To decrease the measurement errors of the y coordinates of tomatoes, we analyzed the measurement errors of y coordinate acquired using binocular stereo vision based on three stereo matching methods. These three stereo matching methods were centroid-based, area-based, and combination stereo matching methods. After stereo matching, the three dimensional coordinates of tomatoes could be acquired based on the triangle ranging principle. Tests of 225 pairs of stereo images of three plastic balls used as normal balls acquired at the distances from 300 to 1000 mm showed that the ranges of the measurement errors of y coordinate acquired based on three stereo matching methods changed with the image acquisition distances obviously. Moreover, the measurement errors of y coordinate appeared linear decreasing trends approximately. Therefore, binary linear regression models were set up to reduce the ranges of the measurement errors of y coordinate of three balls. These models were used as correction models of the measurement values of y coordinate and were helpful to reduce the measurement errors of y coordinate. However, there were owe correction and overcorrection conditions when the image acquisition distances were smaller and larger than 750 mm separately. Then, the correction models based on piecewise binary linear regression were used to solve this problem. The ranges of the measurement errors of y coordinate were reduced further. Tests of 225 pairs of stereo images of three tomatoes acquired at the distances from 300 to 1000 mm showed that the ranges of the measurement errors of y coordinate acquired based on three stereo matching methods were separately from [-20.9, -6.6], [-19.9, -3.44], [-19.9, -3.48] mm to [-6.84, -0.06], [-5.84, -0.82], [-5.85, -0.83] mm after the correction using the piecewise binary linear regression models. It proved that the piecewise binary linear regression models were helpful to reduce the measurement errors of y coordinate in three dimensional localization of tomatoes using binocular stereo vision.

scholarly journals BOUNDARY DEPTH INFORMATION USING HOPFIELD NEURAL NETWORK

2016 ◽  
Vol XLI-B5 ◽  
pp. 139-146
Author(s):  
Sheng Xu ◽  
Ruisheng Wang
Keyword(s):  
Neural Network ◽  
Energy Function ◽  
Stereo Matching ◽  
Boundary Energy ◽  
Region Of Interest ◽  
Hopfield Neural Network ◽  
Depth Information ◽  
Matching Methods ◽  
The Difference ◽  
Smoothness Term

Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.

scholarly journals Analysis of Signal Reception Mechanism of Sky Wave Long-distance Maritime Communication

2018 ◽  
Vol 232 ◽  
pp. 04065
Author(s):  
Huantao Ren ◽  
Qiangjun Xie ◽  
Jiajing Chai ◽  
Yi Xue
Keyword(s):  
Energy Loss ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Propagation Loss ◽  
Sea Surface ◽  
Long Distance ◽  
Loss Model ◽  
Point Field ◽  
Loss Models ◽  
Maritime Communication

Based on the principle of high frequency radio propagation, the signal reception mechanism of sky wave communication over oceans is investigated. Due to long distance signal transmission, the energy loss is inevitable, especially in the space and on the sea surface. Firstly, we establish the space propagation loss model by the ionospheric absorption, the free space propagation characteristics and other extra loss. Referring to the reflection principle of smooth ground and the Kirchhoff approximation, the energy loss models of the calm sea surface and the turbulent sea surface are obtained respectively. Then, through combining the space propagation loss model and the sea surface propagation loss models, we give out a formula of receiving point field strength. According to the signal to noise ratio, we summarize a complete and concise sky wave maritime communication calculation process, through which multi hops number of the receivable sky wave signal can be calculated accurately. The experimental results show the effectiveness.

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