scholarly journals A Framework for Identification of Healthy Potted Seedlings in Automatic Transplanting System Using Computer Vision

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Jin ◽  
Chenglin Wang ◽  
Kaikang Chen ◽  
Jiangtao Ji ◽  
Suchwen Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Comparison Method ◽  
Block Matching ◽  
Identification Accuracy ◽  
Position Information ◽  
Block Matching Algorithm ◽  
Fundamental Process ◽  
Vegetable Seedlings ◽  
Vegetable Cultivation

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

scholarly journals Three-Dimensional Mirror Surface Measurement Based on Local Blur Analysis of Phase Measuring Deflectometry System

2020 ◽  
Vol 37 (5) ◽  
pp. 763-771
Author(s):  
Hongyu Sun ◽  
Le Wang ◽  
Zhan Song ◽  
Geng Chen
Keyword(s):  
Measurement Techniques ◽  
Three Dimensional ◽  
Block Matching ◽  
Surface Measurement ◽  
Mirror Surface ◽  
Reconstruction Accuracy ◽  
Surface Reflection ◽  
Block Matching Algorithm ◽  
Reflective Mirror ◽  
Spatially Varying

Despite the marked progress in recent years, structured light-based three-dimensional (3D) measurement techniques still have difficulty in capturing mirror surface reflection. The accuracy of 3D reconstruction for mirror objects should be further improved to adapt to the high reflectivity and curvature of such objects. To improve the stripe definition and reconstruction accuracy of highly reflective mirror objects, this paper analyzes the local blur of defocus stripes in phase measuring deflectometry (PMD) system, and presents a method to analyze the spatially varying defocusing and de-blurring, with the aid of a 3D block matching algorithm, thereby focusing on defocus stripes. Experimental results show that the proposed method can achieve micron-level reconstruction accuracy of standard flat mirrors, and detect the defects on highly reflective mirror objects at a high precision.

scholarly journals Three-dimensional Spatial Localization Based on Binocular Vsion

2021 ◽  
Author(s):  
Lan Zang ◽  
Kun Zhang ◽  
Chuan Tian ◽  
Chong Shen ◽  
Bhatti Uzair Aslam ◽  
...  
Keyword(s):  
Spatial Data ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Target Object ◽  
Block Matching ◽  
Calibration Error ◽  
Search Range ◽  
Block Matching Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

Abstract In order to solve the problems of low accuracy and unstable system performance existing in binocular vision alone, this paper proposes a threedimensional space recognition and positioning algorithm based on binocular stereo vision and deep learning algorithms. First, a binocular camera for Zhang Zhengyou calibrated by several adjustments, calibration error will eventually set at 0.10pixels best, select and SAD in block matching algorithm in the algorithm, the matching point of the search range reduction, mitigation data for subsequent experiments burden. Then input the three-dimensional spatial data calculated by using the binocular ”parallax” principle into the Faster R-CNN model for data training, extract and classify the target features, and finally realize real-time detection of the target object and its position coordinate information. The analysis of experimental data shows that when the best calibration error is selected and the number of data training is sufficient, the algorithm in this paper can effectively improve the quality of target detection. The positioning accuracy and target recognition rate are increased by about 3%-5%, and it can achieve faster fps.

Disparity Estimation of 3-D Mesh for Stereo Video Coding

2014 ◽  
Vol 571-572 ◽  
pp. 835-839
Author(s):  
Jia Li Zheng ◽  
Tuan Fa Qin ◽  
Dong Xue Wei ◽  
Qin Huan Huang ◽  
Lin Deng
Keyword(s):  
Video Coding ◽  
Three Dimensional ◽  
Estimation Method ◽  
Block Matching ◽  
Disparity Estimation ◽  
Stereo Image ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Mesh Model ◽  
Stereo Video

This paper proposed a disparity estimation method based on the three-dimensional (3-D) mesh model. The disparity estimation is a key step in stereo video coding. By employing intermediate view synthesis, the proposed technique simulate the nodals in the 3-D sureface of stereo image efficiently. Simulated results show that the proposed technique yields a visually more accurate prediciton than the block-matching algorithm.

scholarly journals Proposing 3D Thermal Technology for Heritage Building Energy Monitoring

2021 ◽  
Vol 13 (8) ◽  
pp. 1537
Author(s):  
Antonio Adán ◽  
Víctor Pérez ◽  
José-Luis Vivancos ◽  
Carolina Aparicio-Fernández ◽  
Samuel A. Prieto
Keyword(s):  
Computer Vision ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Building Energy ◽  
Energy Monitoring ◽  
3D Computer Vision ◽  
Building Monitoring ◽  
Heritage Buildings ◽  
Heritage Building ◽  
Definition Of

The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildings.

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