Depth Information Enhancement Using Block Matching and Image Pyramiding Stereo Vision Enabled RGB-D Sensor

2020 ◽  
Vol 20 (10) ◽  
pp. 5406-5414 ◽  
Author(s):  
Sunil Jacob ◽  
Varun G. Menon ◽  
Saira Joseph
Keyword(s):  
Stereo Vision ◽  
Block Matching ◽  
Depth Information

scholarly journals Implementation of Stereo Vision Semi-Global Block Matching Methods for Distance Measurement

2018 ◽  
Vol 12 (2) ◽  
pp. 585 ◽  
Author(s):  
Raden Arief Setyawan ◽  
Rudy Sunoko ◽  
Mochammad Agus Choiron ◽  
Panca Mudji Rahardjo
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Distance Estimation ◽  
Distance Measurement ◽  
Block Matching ◽  
Disparity Map ◽  
Block Matching Algorithm ◽  
Object Distance ◽  
Left And Right ◽  
3D Movie

Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance

scholarly journals Autonomous stereo vision system for depth computation of moving object

2021 ◽  
Author(s):  
Alejandro Emerio Alfonso Oviedo
Keyword(s):  
Stereo Vision ◽  
Background Subtraction ◽  
Vision System ◽  
Moving Object ◽  
Depth Information ◽  
Processing Unit ◽  
First Order ◽  
Real World Application ◽  
Stereoscopic View ◽  
Mean Filter

This work targets one real world application of stereo vision technology: the computation of the depth information of a moving object in a scene. It uses a stereo camera set that captures the stereoscopic view of the scene. Background subtraction algorithm is used to detect the moving object, supported by the recursive filter of first order as updating method. Mean filter is the pre-processing stage, combined with frame downscaling to reduce the background storage. After thresholding the background subtraction result, the binary image is sent to the software processing unit to compute the centroid of the moving area, and the measured disparity, estimate the disparity by Kalman algorithm, and finally calculate the depth from the estimated disparity. The implementation successfully achieves the objectives of resolution 720p, at 28.68 fps and maximum permissible depth error of ±4 cm (1.066 %) for a depth measuring range from 25 cm to 375 cm.

Depth information on moving objects using stereo vision

1997 ◽  
Author(s):  
Ik Soo Choy ◽  
Yonggil Sin ◽  
Jong-An Park
Keyword(s):  
Stereo Vision ◽  
Moving Objects ◽  
Depth Information

scholarly journals Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

2020 ◽  
Vol 10 (3) ◽  
pp. 974
Author(s):  
Chien-Wu Lan ◽  
Chi-Yao Chang
Keyword(s):  
Stereo Vision ◽  
Radio Frequency Identification ◽  
Vision System ◽  
Low Cost ◽  
Color Pattern ◽  
Depth Information ◽  
Rfid Tag ◽  
Intruder Detection ◽  
Stereo Vision System ◽  
Identification Function

Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.

A Research of Stereo Vision Positioning under Vibration

2010 ◽  
Vol 44-47 ◽  
pp. 1315-1319
Author(s):  
Yan Chen ◽  
Wei Liang Cai ◽  
Xiang Jun Zou ◽  
Dong Feng Xu ◽  
Tian Hu Liu
Keyword(s):  
Stereo Vision ◽  
Static Condition ◽  
Regression Equation ◽  
Depth Information ◽  
Positioning Accuracy ◽  
Experimental Platform ◽  
Vibration Condition ◽  
System Composition ◽  
Vision Positioning

To improve the positioning accuracy of picking manipulator, research of stereo vision positioning for picking object in dynamic was studied. And system composition and positioning principle of stereo vision for vibratory object were introduced. Moreover, experimental platform, which simulated the vibration while picking, was designed for the stereo vision positioning experiment in static condition or vibratory condition. Therefore, influence of vibration condition on the depth information of vision positioning can be analyzed and the regression equation of depth error can be built. The results showed that when the object vibrating, the depth error increased. The vibratory frequency was the most important factor, and the depth error would increase with the frequency increased. The influence of vibratory direction and amplitude on depth error was also significant, but much less than frequency.

Measuring Stem Diameter of Sorghum Plants in the Field Using a High-Throughput Stereo Vision System

2021 ◽  
Vol 64 (6) ◽  
pp. 1999-2010
Author(s):  
Lirong Xiang ◽  
Lie Tang ◽  
Jingyao Gai ◽  
Le Wang
Keyword(s):  
High Throughput ◽  
Stereo Vision ◽  
Point Cloud ◽  
Ground Truth ◽  
Motion Blur ◽  
Block Matching ◽  
Stem Diameter ◽  
Plant Phenotyping ◽  
Stereo Imaging ◽  
Sorghum Plant

HighlightsA custom-built camera module named PhenoStereo was developed for high-throughput field-based plant phenotyping.Novel integration of strobe lights facilitated application of PhenoStereo in various environmental conditions.Image-derived stem diameters were found to have high correlations with ground truth, which outperformed any previously reported sensing approach.PhenoStereo showed promising potential to characterize a broad spectrum of plant phenotypes.Abstract. The stem diameter of sorghum plants is an important trait for evaluation of stalk strength and biomass potential, but it is a challenging sensing task to automate in the field due to the complexity of the imaging object and the environment. In recent years, stereo vision has offered a viable three-dimensional (3D) solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping is adversely affected by textureless regions, occlusion of plants, variable outdoor lighting, and wind conditions. In this study, a portable stereo imaging module named PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo features a self-contained embedded design, which makes it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights is integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blur. PhenoStereo was used to acquire a set of sorghum plant images, and an automated point cloud data processing pipeline was developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a mask region convolutional neural network (Mask R-CNN) for detecting stalk contours and a semi-global block matching (SGBM) stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approach. These results demonstrate that, with proper customization, stereo vision can be an effective sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. Based on the results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad range of plant phenotypes, such as the leaf angle and tassel shape of maize plants and the seed pods and stem nodes of soybean plants. Keywords: Field-based high-throughput phenotyping, Point cloud, Stem diameter, Stereo vision.

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