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 Event-based stereo matching using semiglobal matching

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775275 ◽  
Author(s):  
Zhen Xie ◽  
Jianhua Zhang ◽  
Pengfei Wang
Keyword(s):  
Stereo Matching ◽  
Dynamic Range ◽  
Three Dimensional ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Frame Rate ◽  
Estimation Accuracy ◽  
High Temporal Resolution ◽  
Stereo Pair ◽  
Event Based

In this article, we focus on the problem of depth estimation from a stereo pair of event-based sensors. These sensors asynchronously capture pixel-level brightness changes information (events) instead of standard intensity images at a specified frame rate. So, these sensors provide sparse data at low latency and high temporal resolution over a wide intrascene dynamic range. However, new asynchronous, event-based processing algorithms are required to process the event streams. We propose a fully event-based stereo three-dimensional depth estimation algorithm inspired by semiglobal matching. Our algorithm considers the smoothness constraints between the nearby events to remove the ambiguous and wrong matches when only using the properties of a single event or local features. Experimental validation and comparison with several state-of-the-art, event-based stereo matching methods are provided on five different scenes of event-based stereo data sets. The results show that our method can operate well in an event-driven way and has higher estimation 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.

Image Processing Based on Three-Dimensional Stereo Vision

2014 ◽  
Vol 556-562 ◽  
pp. 5017-5020
Author(s):  
Ting Ting Wang
Keyword(s):  
Image Processing ◽  
Binocular Vision ◽  
Stereo Vision ◽  
Error Rate ◽  
Three Dimensional ◽  
Processing Method ◽  
Stereoscopic Vision ◽  
Average Error ◽  
Traditional Model ◽  
Average Error Rate

Three-dimensional stereo vision technology has the capability of overcoming drawbacks influencing by light, posture and occluder. A novel image processing method is proposed based on three-dimensional stereoscopic vision, which optimizes model on the basis of camera binocular vision and in improvement of adding constraints to traditional model, moreover ensures accuracy of later location and recognition. To verify validity of the proposed method, firstly marking experiments are conducted to achieve fruit location, with the result of average error rate of 0.65%; and then centroid feature experiments are achieved with error from 5.77mm to 68.15mm and reference error rate from 1.44% to 5.68%, average error rate of 3.76% while the distance changes from 300mm to 1200mm. All these data of experiments demonstrate that proposed method meets the requirements of three-dimensional imageprocessing.

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.

Research and Realization of a New Algorithm for Stereo Matching Based on Binocular Vision

2013 ◽  
Vol 670 ◽  
pp. 202-207 ◽  
Author(s):  
Jun Ting Cheng ◽  
C. Zhao ◽  
W.L. Zhao ◽  
W.H. Wu
Keyword(s):  
Binocular Vision ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Cnc Machining ◽  
Matching Algorithm ◽  
Evaluation Test ◽  
Three Dimensional Measurement ◽  
Binocular Stereo ◽  
Measuring Machine ◽  
3D Solid

In the development of a three-dimensional measurement system, binocular stereo matching is the most important and difficult. In the basis of introducing selective principles of matching algorithm, a new stereo matching algorithm for binocular vision is put forward that is named noncoded difference measuring distance. The algorithm effectively grapples with the problem of searching for the coincidence relation of raster and can efficiently and accurately obtain three-dimensional world coordinates of the entities. Experiment results show that this 3D measuring machine can effectively measure the 3D solid profile of free surface. During the evaluation test for accuracy, scan a standard plane. Fit all 3D points in one plane, and then the flatness value of this plane is obtained. The flatness value of the standard plane has been ultimately measured as: ± 0.0462mm, this measuring accuracy can completely satisfy the requirements of rapid prototyping or CNC machining, it as well as achieves the stated accuracy (± 0.05mm).

Developing a Novel Stereovision Scheme for Improving Accuracy of Depth Estimation in Robotic Vision Systems

2015 ◽  
Vol 764-765 ◽  
pp. 1227-1233
Author(s):  
Kuan Yu Chen ◽  
Chien Hung Chen ◽  
Cheng Chin Chien
Keyword(s):  
Focal Length ◽  
Rapid Development ◽  
Three Dimensional ◽  
Depth Estimation ◽  
Depth Interval ◽  
Stereo Images ◽  
Depth Estimate ◽  
Parameter Matching ◽  
Improving Accuracy ◽  
Deformation Coefficient

Acquiring three-dimensional data from a pair of stereo images is called stereovision that has been studied by researchers for decades. However, most of the previous studies on this topic focused on establishment of stereovision parameter matching and made conclusions on the premise of fixed focus. With the rapid development of multimedia technology, varifocal digital cameras have been widely used in many robotic applications recently. In general, error in the depth estimate becomes bigger when the focus and aperture is unknown or not fixed. For that reason, a three-stage framework is proposed in this paper to modify the conventional stereovision model for improving accuracy of depth estimation. The first stage is to modify the computational model of conventional stereovision for varifocal cameras. Then, the spacing of depth interval in non-uniform spacing of discrete depth levels can be altered, in particular, it is unaffected by changes in focal length. Finally, with considering the affine transformation, we add the deformation coefficient into the modified stereovision model for correcting three-dimensional affine deformations. Experimental results demonstrated that the depth estimation from stereo images using the proposed scheme was more accurate than conventional method.

IN SEARCH OF THE PROTEIN NATIVE STATE WITH A PROBABILISTIC SAMPLING APPROACH

2011 ◽  
Vol 09 (03) ◽  
pp. 383-398 ◽  
Author(s):  
BRIAN OLSON ◽  
KEVIN MOLLOY ◽  
AMARDA SHEHU
Keyword(s):  
Structure Prediction ◽  
Computational Models ◽  
Three Dimensional ◽  
Structural Diversity ◽  
Search Space ◽  
Conformational Space ◽  
Conformational Search ◽  
Dimensional Structure ◽  
Computational Techniques ◽  
Native State

The three-dimensional structure of a protein is a key determinant of its biological function. Given the cost and time required to acquire this structure through experimental means, computational models are necessary to complement wet-lab efforts. Many computational techniques exist for navigating the high-dimensional protein conformational search space, which is explored for low-energy conformations that comprise a protein's native states. This work proposes two strategies to enhance the sampling of conformations near the native state. An enhanced fragment library with greater structural diversity is used to expand the search space in the context of fragment-based assembly. To manage the increased complexity of the search space, only a representative subset of the sampled conformations is retained to further guide the search towards the native state. Our results make the case that these two strategies greatly enhance the sampling of the conformational space near the native state. A detailed comparative analysis shows that our approach performs as well as state-of-the-art ab initio structure prediction protocols.

scholarly journals Depth Estimation for Monocular Image Based on Convolutional Neural Networks

2021 ◽  
Vol 15 ◽  
pp. 533-540
Author(s):  
Binglin Niu ◽  
Mengxia Tang ◽  
Xuelin Chen
Keyword(s):  
Three Dimensional ◽  
Depth Estimation ◽  
Dimensional Structure ◽  
Depth Information ◽  
Pyramid Structure ◽  
Level Information ◽  
Monocular Image ◽  
Feature Pyramid ◽  
Sampling Structure ◽  
Autonomous Movement

Perceiving the three-dimensional structure of the surrounding environment and analyzing it for autonomous movement is an indispensable element for robots to operate in scenes. Recovering depth information and the three-dimensional spatial structure from monocular images is a basic mission of computer vision. For the objects in the image, there are many scenes that may produce it. This paper proposes to use a supervised end-to-end network to perform depth estimation without relying on any subsequent processing operations, such as probabilistic graphic models and other extra fine steps. This paper uses an encoder-decoder structure with feature pyramid to complete the prediction of dense depth maps. The encoder adopts ResNeXt-50 network to achieve main features from the original image. The feature pyramid structure can merge high and low level information with each other, and the feature information is not lost. The decoder utilizes the transposed convolutional and the convolutional layer to connect as an up-sampling structure to expand the resolution of the output. The structure adopted in this paper is applied to the indoor dataset NYU Depth v2 to obtain better prediction results than other methods. The experimental results show that on the NYU Depth v2 dataset, our method achieves the best results on 5 indicators and the sub-optimal results on 1 indicator.

scholarly journals Three-dimensional mechanical parts reconstruction technology based on two-dimensional image

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091000
Author(s):  
Jiaofei Huo ◽  
Xiaomo Yu
Keyword(s):  
Image Processing ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Vision System ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Stereo Vision System ◽  
Binocular Stereo Vision ◽  
Extraction Algorithm ◽  
Dimensional Reconstruction

With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo vision system based on two-dimensional image modeling technology. The system can be divided into image processing, camera calibration, stereo matching, three-dimensional point reconstruction, and model reconstruction. In the part of image processing, common image processing methods, feature point extraction algorithm, and edge extraction algorithm are studied. On this basis, interactive local corner extraction algorithm and interactive local edge detection algorithm are proposed. It is found that the Harris algorithm can effectively remove the features of less information and easy to generate clustering phenomenon. At the same time, the method of limit constraints is used to match the feature points extracted from the image. This method has high matching accuracy and short time. The experimental research has achieved good matching results. Using the platform of binocular stereo vision system, each step in the process of three-dimensional reconstruction has achieved high accuracy, thus achieving the three-dimensional reconstruction of the target object. Finally, based on the research of three-dimensional reconstruction of mechanical parts and the designed binocular stereo vision system platform, the experimental results of edge detection, camera calibration, stereo matching, and three-dimensional model reconstruction in the process of three-dimensional reconstruction are obtained, and the full text is summarized, analyzed, and prospected.

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