A fast stereo matching algorithm for 3D reconstruction of internal organs in laparoscopic surgery

2008 ◽  
Author(s):  
Yoshimichi Okada ◽  
Takeshi Koishi ◽  
Suguru Ushiki ◽  
Toshiya Nakaguchi ◽  
Norimichi Tsumura ◽  
...  
Keyword(s):  
Laparoscopic Surgery ◽  
3D Reconstruction ◽  
Stereo Matching ◽  
Internal Organs ◽  
Matching Algorithm

A New Method on Super Pixel Reducing Stereo Matching Time of Integrated Imaging

2020 ◽  
pp. 2154014
Author(s):  
Xue-Guang Wang ◽  
Ming Li ◽  
Lei Zhang ◽  
Hui Zhao ◽  
Thelma D. Palaoag
Keyword(s):  
3D Reconstruction ◽  
Conventional Method ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Technical Difficulty ◽  
New Method ◽  
Matching Algorithm ◽  
The Core ◽  
Pixel Matching ◽  
Novel Method

Stereo vision and 3D reconstruction technologies are increasingly concerned in many fields. Stereo matching algorithm is the core of stereo vision and also a technical difficulty. A novel method based on super pixels is mentioned in this paper to reduce the calculating amount and the time. Stereo images from University of Tsukuba are used to test our method. The proposed method spends only 1% of the time spent by the conventional method. Through a two-step super-pixel matching optimization, it takes 6.72 s to match a picture, which is 12.96% of the pre-optimization.

scholarly journals A Miniature Binocular Endoscope with Local Feature Matching and Stereo Matching for 3D Measurement and 3D Reconstruction

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2243 ◽  
Author(s):  
Di Wang ◽  
Hua Liu ◽  
Xiang Cheng
Keyword(s):  
3D Reconstruction ◽  
Fiber Optics ◽  
Relative Error ◽  
Stereo Matching ◽  
Stereoscopic Vision ◽  
Light Change ◽  
External Diameter ◽  
3D Measurement ◽  
Matching Algorithm ◽  
Mean Relative Error

As the traditional single camera endoscope can only provide clear images without 3D measurement and 3D reconstruction, a miniature binocular endoscope based on the principle of binocular stereoscopic vision to implement 3D measurement and 3D reconstruction in tight and restricted spaces is presented. In order to realize the exact matching of points of interest in the left and right images, a novel construction method of the weighted orthogonal-symmetric local binary pattern (WOS-LBP) descriptor is presented. Then a stereo matching algorithm based on Gaussian-weighted AD-Census transform and improved cross-based adaptive regions is studied to realize 3D reconstruction for real scenes. In the algorithm, we adjust determination criterions of adaptive regions for edge and discontinuous areas in particular and as well extract mismatched pixels caused by occlusion through image entropy and region-growing algorithm. This paper develops a binocular endoscope with an external diameter of 3.17 mm and the above algorithms are applied in it. The endoscope contains two CMOS cameras and four fiber optics for illumination. Three conclusions are drawn from experiments: (1) the proposed descriptor has good rotation invariance, distinctiveness and robustness to light change as well as noises; (2) the proposed stereo matching algorithm has a mean relative error of 8.48% for Middlebury standard pairs of images and compared with several classical stereo matching algorithms, our algorithm performs better in edge and discontinuous areas; (3) the mean relative error of length measurement is 3.22%, and the endoscope can be utilized to measure and reconstruct real scenes effectively.

scholarly journals 3D Reconstruction Algorithm of Weld Pool Surface Based on Computer Vision Technology

2015 ◽  
Vol 9 (1) ◽  
pp. 820-825
Author(s):  
Zhen-Hai Mu
Keyword(s):  
3D Reconstruction ◽  
Weld Pool ◽  
Stereo Matching ◽  
Vision System ◽  
Reconstruction Algorithm ◽  
Welding Parameters ◽  
Matching Algorithm ◽  
Pool Surface ◽  
Matching Cost ◽  
Weld Pool Surface

As is well known that sensing and measuring the weld pool surface is very important to design intelligent welding machines which is able to imitate a skilled human welder who can choose suitable welding parameters. Therefore, in this paper, we focused on the problem of weld pool surface 3D reconstruction, which is a key issue in intelligent welding machines development. Firstly, the framework of the weld pool surface 3D reconstruction system is described. The weld pool surface 3D reconstruction system uses a single camera stereo vision system to extract original data from weld pool, and then the left and right images are collected. Afterward, we utilize Pixel difference square and matching algorithm and Stereo matching algorithm to process images. Next, the 3D reconstruction of weld pool surface is constructed using the point cloud data. Secondly, stereo matching based weld pool surface 3D reconstruction algorithm is illustrated. In this algorithm, the matching cost function is computed through the Markov random field, and then the weighted matching cost is calculated via the guided filter. Thirdly, to test the performance of our proposed algorithm, we develop an experimental platform to measure weld pool width, length, convexity and the previous inputs based on a linear model predictive controller. Experimental results demonstrate that the proposed 3D reconstruction algorithm of weld pool surface can achieve high quality under both current disturbance and speed disturbance.

scholarly journals Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Least Square ◽  
Affine Invariant ◽  
Stereo Images ◽  
Distance Ratio ◽  
Matching Algorithm ◽  
End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

A modified stereo matching algorithm suitable for implementation on a convolution specialized hardware

1992 ◽  
Vol 13 (7) ◽  
pp. 523-528 ◽  
Author(s):  
E. Stella ◽  
A. Distante ◽  
G. Attolico ◽  
T. D'Orazio
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Specialized Hardware
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