scholarly journals Review on Stereo Vision Based Depth Estimation

2020 ◽  
pp. 665-671
Author(s):  
Sheshang Degadwala ◽  
Dhairya Vyas ◽  
Arpana Mahajan
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Depth Estimation ◽  
Cost Calculation ◽  
Driver Assistance Systems ◽  
Sound System ◽  
Cost Aggregation ◽  
Exact Difference ◽  
Refinement Process ◽  
Cost Efficient

Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The fundamental test of sound system vision is to create exact difference map. Sound system vision calculations for the most part perform four stages: first, coordinating cost calculation; second, cost collection; third, dissimilarity calculation or enhancement; and fourth, divergence refinement. Sound system coordinating issues are likewise examined. An enormous number of calculations have been produced for sound system vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.

Dense Disparity Computing Method Based on Mesh Aggregation and Snake Optimization for Stereo Vision

2020 ◽  
Vol 13 (3) ◽  
pp. 95-112
Author(s):  
Liu Shuang ◽  
Yu Shuchun
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Cost Calculation ◽  
Segmentation Method ◽  
Suppression Effect ◽  
Cost Aggregation ◽  
Brightness Difference ◽  
Matching Cost ◽  
Computing Method ◽  
Snake Algorithm

In order to generate continuous and dense disparity images, a stereo matching method based on mesh aggregation and Snake optimization is proposed in this article. First, the reference pixels are obtained, so as to improve the suppression effect of the brightness difference in Census transform and improve the accuracy of initial matching cost calculation. Second, the image is divided by SLIC super pixel segmentation method, and the neighborhood pixels are searched according to the mesh search in the region, and the matching cost of these pixels are aggregated together according to the corresponding weight to complete cost aggregation of the pixels to be matched. Third, the Snake algorithm is used in optimizing the boundary of the disparity region. Eight classes of images on the Middlebury platform are selected as the test images, and the four algorithms on the Middlebury platform are selected as reference algorithms to carry out the experimental research. The experimental results show that proportion to bad pixels is low and disparity is continuous and dense on the disparity image calculated by the algorithm proposed in this article. Performance of the proposed method is close to LocalExp algorithm which is the best on the Middlebury platform, and the proposed method can be better applied in the stereo vision.

scholarly journals Shape Reserved Stereo Matching with Segment-based Cost Aggregation and Dual-path Refinement

2020 ◽  
Author(s):  
Chih-Shuan Huang ◽  
Ya-Han Huang ◽  
Din-Yuen Chan ◽  
Jar-Ferr Yang
Keyword(s):  
Computer Vision ◽  
Stereo Matching ◽  
Texture Features ◽  
Depth Estimation ◽  
Spatial Proximity ◽  
Depth Maps ◽  
Cost Aggregation ◽  
Color Similarity ◽  
Adaptive Support ◽  
Selection Of

Abstract Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various smart applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded and discontinuous regions. In this paper, we propose a robust stereo matching system, which is based on segment-based superpixels, to design adaptive matching computation and dual-path refinement. After the selection of matching costs, we suggest the segment-based adaptive support weights for cost aggregation, instead of color similarity and spatial proximity, to achieve precise depth estimation. Then, the proposed dual-path depth refinement, which refers the texture features in a cross-based support region, corrects the inaccurate disparities to successively refine the depth maps with shape reserving. Specially for left-most and right most regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system achieves higher accurate depth maps than the conventional stereo matching methods.

A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder

2014 ◽  
Vol 536-537 ◽  
pp. 67-76
Author(s):  
Xiang Zhang ◽  
Zhang Wei Chen
Keyword(s):  
Stereo Matching ◽  
Cost Calculation ◽  
Stereo Pair ◽  
Disparity Map ◽  
High Definition ◽  
Matching Algorithm ◽  
Census Transform ◽  
Cost Aggregation ◽  
Dsp Builder ◽  
Fpga Chip

This paper proposes a FPGA implementation to apply a stereo matching algorithm based on a kind of sparse census transform in a FPGA chip which can provide a high-definition dense disparity map in real-time. The parallel stereo matching algorithm core involves census transform, cost calculation and cost aggregation modules. The circuits of the algorithm core are modeled by the Matlab/Simulink-based tool box: DSP Builder. The system can process many different sizes of stereo pair images through a configuration interface. The maximum horizon resolution of stereo images is 2048.

Improving the Accuracy of Depth Estimation in Binocular Vision for Robotic Applications

2013 ◽  
Vol 284-287 ◽  
pp. 1862-1866 ◽  
Author(s):  
Kuan Yu Chen ◽  
Cheng Chin Chien ◽  
Chien Te Tseng
Keyword(s):  
Binocular Vision ◽  
Stereo Vision ◽  
Computational Models ◽  
Stereo Matching ◽  
Focal Length ◽  
Three Dimensional ◽  
Estimation Method ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Dimensional Structure

Binocular vision or stereo vision for extraction of three-dimensional information from stereo images has been widely used in many applications like robot navigation, recovering the three-dimensional structure of a scene, and optical inspection systems. More recently, the majority of research in binocular vision has focused on the establishment of stereo matching. However, to date, there has been relatively little research conducted on the effect of computational models of binocular vision with variable focal length of lens. In this paper, a modified computational model of binocular vision is presented to develop a new depth estimation algorithm with no effect of changes in focal length. This method provides an obvious advantage in accuracy of depth estimation by reducing the effect of changing the lens focal length. The experimental results show that the proposed depth estimation method in binocular vision provides better accuracy than conventional method. Finally, we apply the new depth estimation method to a stereo-vision-based automatic docking system for a mobile robot to verify its accuracy.

scholarly journals High-Accuracy Recognition and Localization of Moving Targets in an Indoor Environment Using Binocular Stereo Vision

2021 ◽  
Vol 10 (4) ◽  
pp. 234
Author(s):  
Jing Ding ◽  
Zhigang Yan ◽  
Xuchen We
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Target Localization ◽  
Parallel Structure ◽  
Moving Target ◽  
Target Area ◽  
Moving Targets ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

To obtain effective indoor moving target localization, a reliable and stable moving target localization method based on binocular stereo vision is proposed in this paper. A moving target recognition extraction algorithm, which integrates displacement pyramid Horn–Schunck (HS) optical flow, Delaunay triangulation and Otsu threshold segmentation, is presented to separate a moving target from a complex background, called the Otsu Delaunay HS (O-DHS) method. Additionally, a stereo matching algorithm based on deep matching and stereo vision is presented to obtain dense stereo matching points pairs, called stereo deep matching (S-DM). The stereo matching point pairs of the moving target were extracted with the moving target area and stereo deep matching point pairs, then the three dimensional coordinates of the points in the moving target area were reconstructed according to the principle of binocular vision’s parallel structure. Finally, the moving target was located by the centroid method. The experimental results showed that this method can better resist image noise and repeated texture, can effectively detect and separate moving targets, and can match stereo image points in repeated textured areas more accurately and stability. This method can effectively improve the effectiveness, accuracy and robustness of three-dimensional moving target coordinates.

scholarly journals A Joint 2D-3D Complementary Network for Stereo Matching

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1430
Author(s):  
Xiaogang Jia ◽  
Wei Chen ◽  
Zhengfa Liang ◽  
Xin Luo ◽  
Mingfei Wu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Research Field ◽  
Disparity Map ◽  
Improve Performance ◽  
Cost Aggregation ◽  
Disparity Range ◽  
Public Datasets ◽  
Coarse To Fine ◽  
Speed And Accuracy

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.

Symbol recognition system based on 3D stereo vision

2020 ◽  
pp. 1-10
Author(s):  
Linlin Wang
Keyword(s):  
Camera Calibration ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Recognition System ◽  
Symbol Recognition ◽  
Two Dimensional ◽  
Measuring Machine ◽  
Left And Right

With the continuous development of computer science and technology, symbol recognition systems may be converted from two-dimensional space to three-dimensional space. Therefore, this article mainly introduces the symbol recognition system based on 3D stereo vision. The three-dimensional image is taken by the visual coordinate measuring machine in two places on the left and right. Perform binocular stereo matching on the edge of the feature points of the two images. A corner detection algorithm combining SUSAN and Harris is used to detect the left and right camera calibration templates. The two-dimensional coordinate points of the object are determined by the image stereo matching module, and the three-dimensional discrete coordinate points of the object space can be obtained according to the transformation relationship between the image coordinates and the actual object coordinates. Then draw the three-dimensional model of the object through the three-dimensional drawing software. Experimental data shows that the logic resources and memory resources occupied by image preprocessing account for 30.4% and 27.4% of the entire system, respectively. The results show that the system can calibrate the internal and external parameters of the camera. In this way, the camera calibration result will be more accurate and the range will be wider. At the same time, it can effectively make up for the shortcomings of traditional modeling techniques to ensure the measurement accuracy of the detection system.

Cost Analysis for the Renewable Energy Generation to meet the Energy Security in Bangladesh

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Shakib Hassan Eon ◽  
Shakib Hassan Eon ◽  
Shakib Hassan Eon
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Energy Security ◽  
Energy Demand ◽  
Energy Generation ◽  
Cost Calculation ◽  
Renewable Power ◽  
First Choice ◽  
Cost Efficient ◽  
Renewable Energy Generation

Renewable energy generation is no more an alternative rather it becomes a choice for the power generation to meet the upcoming energy demand. Considering the non- renewable energy unavailability, as well as, the environmental impact, renewable energy should be the first choice. Most of the power generation in Bangladesh comes from nonrenewable energy and a noticeable amount of energy is imported from abroad. As a developing country, it is not cost-efficient and never ensures energy security. To ensure long-term energy security, it is time to shift power generation from nonrenewable to renewable energy generation. This paper presents an approximate calculation for the renewable power generating plant cost and returning year. The cost calculation is done in the context of Bangladesh.

Sign in / Sign up

Export Citation Format

Share Document