Adaptive stereo matching for SPOT multi-spectral image pairs

1994 ◽  
Author(s):  
A. J. Chen ◽  
C. H. Liu ◽  
J. Y. Rau ◽  
Lin-Chi Chen
Keyword(s):  
Stereo Matching ◽  
Spectral Image ◽  
Image Pairs

scholarly journals Unsupervised Cross-Spectral Stereo Matching by Learning to Synthesize

2019 ◽  
Vol 33 ◽  
pp. 8706-8713 ◽  
Author(s):  
Mingyang Liang ◽  
Xiaoyang Guo ◽  
Hongsheng Li ◽  
Xiaogang Wang ◽  
You Song
Keyword(s):  
Semantic Information ◽  
Stereo Matching ◽  
Spectral Image ◽  
Adversarial Learning ◽  
Additional Information ◽  
Spectral Bands ◽  
Image Translation ◽  
View Reconstruction ◽  
Image Pairs ◽  
Spectral Translation

Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any depth or disparity supervision. The estimated depth provides additional information complementary to original images, which can be helpful for other vision tasks such as tracking, recognition and detection. However, there are large appearance variations between images from different spectral bands, which is a challenge for cross-spectral stereo matching. Existing deep unsupervised stereo matching methods are sensitive to the appearance variations and do not perform well on cross-spectral data. We propose a novel unsupervised crossspectral stereo matching framework based on image-to-image translation. First, a style adaptation network transforms images across different spectral bands by cycle consistency and adversarial learning, during which appearance variations are minimized. Then, a stereo matching network is trained with image pairs from the same spectra using view reconstruction loss. At last, the estimated disparity is utilized to supervise the spectral translation network in an end-to-end way. Moreover, a novel style adaptation network F-cycleGAN is proposed to improve the robustness of spectral translation. Our method can tackle appearance variations and enhance the robustness of unsupervised cross-spectral stereo matching. Experimental results show that our method achieves good performance without using depth supervision or explicit semantic information.

scholarly journals Efficient Stereo Matching with Decoupled Dissimilarity Measure Using Successive Weighted Summation

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Cheng-Tao Zhu ◽  
Yau-Zen Chang ◽  
Huai-Ming Wang ◽  
Kai He ◽  
Shih-Tseng Lee ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Dissimilarity Measure ◽  
Stereo Image ◽  
Fitting Procedure ◽  
Comparable Performance ◽  
Novel Method ◽  
Disparity Maps ◽  
Image Pairs ◽  
Benchmark Evaluation ◽  
Complexity Algorithm

Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps.

scholarly journals Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3747 ◽  
Author(s):  
Ma ◽  
Bai ◽  
Wang ◽  
Fang
Keyword(s):  
Nonlinear Optimization ◽  
Stereo Matching ◽  
Measurement Model ◽  
Stereo Image ◽  
Measurement Unit ◽  
Experimental Comparison ◽  
The Public ◽  
Stereo Cameras ◽  
Image Pairs ◽  
The Cost

The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.

scholarly journals Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1869
Author(s):  
Hua Liu ◽  
Rui Wang ◽  
Yuanping Xia ◽  
Xiaoming Zhang
Keyword(s):  
Cost Function ◽  
Stereo Matching ◽  
Guided Filter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Dense Stereo Matching ◽  
Gradient Based ◽  
Matching Cost ◽  
Cost Measurement ◽  
Image Pairs

Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.

scholarly journals A Dynamic Multi-Projection-Contour Approximating Framework for the 3D Reconstruction of Buildings by Super-Generalized Optical Stereo-Pairs

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2153 ◽  
Author(s):  
Yiming Yan ◽  
Nan Su ◽  
Chunhui Zhao ◽  
Liguo Wang
Keyword(s):  
3D Reconstruction ◽  
3D Model ◽  
Stereo Matching ◽  
Shape Reconstruction ◽  
Basic Unit ◽  
Projection System ◽  
3D Shape ◽  
Bee Colony ◽  
3D Shape Reconstruction ◽  
Image Pairs

In this paper, a novel framework of the 3D reconstruction of buildings is proposed, focusing on remote sensing super-generalized stereo-pairs (SGSPs). As we all know, 3D reconstruction cannot be well performed using nonstandard stereo pairs, since reliable stereo matching could not be achieved when the image-pairs are collected at a great difference of views, and we always failed to obtain dense 3D points for regions of buildings, and cannot do further 3D shape reconstruction. We defined SGSPs as two or more optical images collected in less constrained views but covering the same buildings. It is even more difficult to reconstruct the 3D shape of a building by SGSPs using traditional frameworks. As a result, a dynamic multi-projection-contour approximating (DMPCA) framework was introduced for SGSP-based 3D reconstruction. The key idea is that we do an optimization to find a group of parameters of a simulated 3D model and use a binary feature-image that minimizes the total differences between projection-contours of the building in the SGSPs and that in the simulated 3D model. Then, the simulated 3D model, defined by the group of parameters, could approximate the actual 3D shape of the building. Certain parameterized 3D basic-unit-models of typical buildings were designed, and a simulated projection system was established to obtain a simulated projection-contour in different views. Moreover, the artificial bee colony algorithm was employed to solve the optimization. With SGSPs collected by the satellite and our unmanned aerial vehicle, the DMPCA framework was verified by a group of experiments, which demonstrated the reliability and advantages of this work.

scholarly journals Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Sheng Liu ◽  
Haiqiang Jin ◽  
Xiaojun Mao ◽  
Binbin Zhai ◽  
Ye Zhan ◽  
...  
Keyword(s):  
Global Optimization ◽  
Stereo Matching ◽  
Cost Optimization ◽  
Depth Estimation ◽  
Optimization Method ◽  
Optimization Approach ◽  
Real Scene ◽  
Complex Scenes ◽  
Matching Cost ◽  
Image Pairs

This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches.

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