scholarly journals Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach

2019 ◽  
Vol 11 (17) ◽  
pp. 1990 ◽  
Author(s):  
Mostafa Mansour ◽  
Pavel Davidson ◽  
Oleg Stepanov ◽  
Robert Piché
Keyword(s):  
Computer Vision ◽  
Stereo Vision ◽  
Binocular Disparity ◽  
Motion Parallax ◽  
Depth Map ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Camera Motion ◽  
Angular Velocities ◽  
Motion Parameters

Binocular disparity and motion parallax are the most important cues for depth estimation in human and computer vision. Here, we present an experimental study to evaluate the accuracy of these two cues in depth estimation to stationary objects in a static environment. Depth estimation via binocular disparity is most commonly implemented using stereo vision, which uses images from two or more cameras to triangulate and estimate distances. We use a commercial stereo camera mounted on a wheeled robot to create a depth map of the environment. The sequence of images obtained by one of these two cameras as well as the camera motion parameters serve as the input to our motion parallax-based depth estimation algorithm. The measured camera motion parameters include translational and angular velocities. Reference distance to the tracked features is provided by a LiDAR. Overall, our results show that at short distances stereo vision is more accurate, but at large distances the combination of parallax and camera motion provide better depth estimation. Therefore, by combining the two cues, one obtains depth estimation with greater range than is possible using either cue individually.

scholarly journals Machine Learning Based Computer Vision Application for Visually Disabled People

2021 ◽  
pp. 488-494
Author(s):  
Shubhada Mone ◽  
Nihar Salunke ◽  
Omkar Jadhav ◽  
Arjun Barge ◽  
Nikhil Magar
Keyword(s):  
Computer Vision ◽  
Distance Estimation ◽  
Depth Map ◽  
Depth Estimation ◽  
Indoor Navigation ◽  
Disabled People ◽  
Automatic Driving ◽  
Object Distance ◽  
Physical Tools ◽  
Visually Disabled

With the easy availability of technology, smartphones are playing an important role in every person’s life. Also, with the advancements in computer vision based research, Automatic Driving cars, Object Recognition, Depth Map Prediction, Object Distance Estimation, have reached commendable levels of intelligence and accuracy. Combining the research and technological advancements, we can be hopeful in creating a computer vision based mobile-application which will help guide visually disabled people in performing their day to day tasks with easily available mobile applications. With our study, the visually disabled can perform simple tasks like outdoor/indoor navigation without encountering obstacles, also they can avoid accidental collisions with objects in their surroundings. Currently, there are very few applications which provide the same assistance to the visually impaired. Using physical tools like sticks is a very common practice when it comes to avoiding obstacles in a visually disabled person’s path. Our study will be focused on object detection and depth estimation techniques- two of the most popular and advanced fields in Intelligent Computer vision studies. We have explored more on the traditional challenges and future hopes of incorporating these techniques on embedded devices.

Motion-Based Depth Estimation for 2D to 3D Video Conversion

2016 ◽  
Vol 20 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Fan Guo ◽  
◽  
Jin Tang ◽  
Beiji Zou ◽  
Keyword(s):  
Motion Parallax ◽  
Depth Map ◽  
Bilateral Filter ◽  
Depth Estimation ◽  
3D Video ◽  
Local Motion ◽  
Single View ◽  
Motion Type ◽  
Visual Artifacts ◽  
2D To 3D

Recent advances in 3D have increased the importance of stereoscopic content creation and processing. Therefore, converting existing 2D videos into 3D videos is very important for growing 3D market. The most difficult task in 2D-to-3D video conversion is estimating depth map from single-view frame images. Thus, in this paper, we propose a novel motion-based 2D to 3D video conversion method. The method first determines the motion type using the optical flow estimation. Then, different depth estimation processes are performed based on the motion type. For global motion, the depth from motion parallax provides the final depth map. For local motion, the depth from template together with the bilateral filter is used to produce the depth map. Finally, the left- and right-view images are synthesized to generate realistic stereoscopic results for viewers. During the process, the visual artifacts of the synthesized virtual views are effectively eliminated by recovering the separation and loss of foreground objects. A comparative study and quantitative evaluation with other conversion methods are carried out, which demonstrate that better overall quality results may be obtained using the proposed method.

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 Manual depth estimation for binocular disparity and motion parallax

2013 ◽  
Vol 13 (9) ◽  
pp. 971-971 ◽  
Author(s):  
Z. Leonard ◽  
M. Nawrot ◽  
K. Stroyan
Keyword(s):  
Binocular Disparity ◽  
Motion Parallax ◽  
Depth Estimation

scholarly journals Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xin Yang ◽  
Qingling Chang ◽  
Xinglin Liu ◽  
Siyuan He ◽  
Yan Cui
Keyword(s):  
Depth Map ◽  
Depth Estimation ◽  
Multi Scale ◽  
Monocular Depth

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

scholarly journals The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  
Keyword(s):  
Structural Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Image Synthesis ◽  
Map Estimation ◽  
Estimation Model ◽  
Depth Prediction ◽  
Underwater Image ◽  
Underwater Vision ◽  
Image Depth

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.

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