scholarly journals Grading Method of Potted Anthurium Based on RGB-D Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongyu Wei ◽  
Wenqi Tang ◽  
Xuan Chu ◽  
Yinghui Mu ◽  
Zhiyu Ma
Keyword(s):  
Machine Vision ◽  
Plant Height ◽  
Detection System ◽  
Three Dimensional ◽  
Segmentation Method ◽  
Accuracy Rate ◽  
Depth Images ◽  
Crown Width ◽  
Sequencing Method ◽  
Automatic Grading

A grading method of potted Anthurium based on machine vision is proposed. A detection system is designed to acquire color images and depth images of potted Anthurium, and the three-dimensional point-cloud image is reconstructed after registration. According to the testing requirements of potted Anthurium, the minimum enclosing rectangle method is used to measure the width of crowns and spathes. The bubble sequencing method is used to measure the plant height, and the clustering segmentation method is used to calculate the number of spathes. Online automatic grading software for potted Anthurium is developed. Compared with manual measurement, the average measurement accuracies of machine vision for crown width, plant height, spathe width, and spathe number are 98.4%, 98.4%, 98.8%, and 86.7%, respectively. The accuracy rate of grading is 85.86%, which can meet the requirements of automatic grading of potted Anthurium.

scholarly journals Direct 3D model extraction method for color volume images

2021 ◽  
Vol 29 ◽  
pp. 133-140
Author(s):  
Bin Liu ◽  
Shujun Liu ◽  
Guanning Shang ◽  
Yanjie Chen ◽  
Qifeng Wang ◽  
...  
Keyword(s):  
3D Model ◽  
Clinical Medicine ◽  
Three Dimensional ◽  
Laplacian Matrix ◽  
Segmentation Method ◽  
Human Organs ◽  
Model Segmentation ◽  
Treatment Objective ◽  
Organ Models ◽  
Different Parts

BACKGROUND: There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment. OBJECTIVE: We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly. METHODS: The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks. RESULTS: We propose a direct 3D model segmentation method for volume images. This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects. CONCLUSIONS: The results of segmentation experiments on different parts of human body prove the applicability of this method.

Fabrication of Three-Dimensional Micro-Structures with Two-Photon Absorption by Femtosecond Laser

2006 ◽  
Vol 532-533 ◽  
pp. 568-571
Author(s):  
Ming Zhou ◽  
Hai Feng Yang ◽  
Li Peng Liu ◽  
Lan Cai
Keyword(s):  
Photonic Crystal ◽  
Femtosecond Laser ◽  
Detection System ◽  
Three Dimensional ◽  
Photon Absorption ◽  
Micro Fabrication ◽  
Two Photon Absorption ◽  
Two Photon ◽  
Photo Polymerization ◽  
System A

The photo-polymerization induced by Two-Photon Absorption (TPA) is tightly confined in the focus because the efficiency of TPA is proportional to the square of intensity. Three-dimensional (3D) micro-fabrication can be achieved by controlling the movement of the focus. Based on this theory, a system for 3D-micro-fabrication with femtosecond laser is proposed. The system consists of a laser system, a microscope system, a real-time detection system and a 3D-movement system, etc. The precision of micro-machining reaches a level down to 700nm linewidth. The line width was inversely proportional to the fabrication speed, but proportional to laser power and NA. The experiment results were simulated, beam waist of 0.413μm and TPA cross section of 2×10-54cm4s was obtained. While we tried to optimize parameters, we also did some research about its applications. With TPA photo-polymerization by means of our experimental system, 3D photonic crystal of wood-pile structure twelve layers and photonic crystal fiber are manufactured. These results proved that the micro-fabrication system of TPA can not only obtain the resolution down to sub-micron level, but also realize real 3D micro-fabrication.

T1595: Three Dimensional Image Overlay Navigation Using Magnetic Endoscope Detection System for NOTES® and SILS

2010 ◽  
Vol 71 (5) ◽  
pp. AB318 ◽  
Author(s):  
Maki Sugimoto ◽  
Yoshinori Morita ◽  
Tsuyoshi Sanuki ◽  
Hiromu Kutsumi ◽  
Takeshi Azuma
Keyword(s):  
Detection System ◽  
Three Dimensional ◽  
Dimensional Image ◽  
Image Overlay

Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

2021 ◽  
pp. 004051752110342
Author(s):  
Sifundvolesihle Dlamini ◽  
Chih-Yuan Kao ◽  
Shun-Lian Su ◽  
Chung-Feng Jeffrey Kuo
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection System ◽  
Vision System ◽  
Good Precision ◽  
Machine Vision System ◽  
Time Machine ◽  
Textile Fabric

We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.

scholarly journals Aplikasi Machine Vision pada Hexacopter untuk Deteksi Survival Kits di Bidang Mitigasi Bencana

2020 ◽  
Vol 12 (2) ◽  
pp. 72-79
Author(s):  
Ismawan Noor Ikhsan ◽  
Son Ali Akbar
Keyword(s):  
Machine Vision ◽  
Color Space ◽  
Region Of Interest ◽  
Contour Detection ◽  
Segmentation Method ◽  
Smart System ◽  
Hsv Color Space ◽  
Aerial Vehicle ◽  
Detection Evaluation ◽  
Flying Robots

Hexacopter belongs to one of flying robots that is used to carry out a special mission such as retrieving and delivering survival kits object. Thus, it should be built by smart system to determine the object accurately. However, there was an interference from other object that made it difficult to recognize the survival kits object. Therefore, the development of machine vision with the integration of the hexacopter control system is expected to improve the object recognition process. This study intends to develop a survival kit detection using the image processing method, which involved 1) segmentation on the Hue, Saturation, Value (HSV) color space, 2) contour detection, and 3) Region of Interest (ROI) selected detection. The evaluation of the segmentation method performances was done through the three-part experiments (i.e., the similar shape, variety of a color object, and an object shape). The result of survival kits object detection evaluation obtained an accuracy of 90.33%, precision of 99.63%, and recall of 91.24%. According to the performances obtained in this study, the development of machine vision systems on Unmanned Aerial Vehicle (UAV) has a high accuracy for the object survival kits detection even with another object interference.

scholarly journals Deep learning based MRI contrast synthesis using full volume prediction

2021 ◽  
Author(s):  
Jose V Manjon ◽  
Jose E Romero ◽  
Pierrick Coupé
Keyword(s):  
Three Dimensional ◽  
Patient Comfort ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Complementary Information ◽  
Mri Contrast ◽  
Ambiguity Reduction ◽  
Magnetic Resonance Imaging Mri ◽  
Time Limitations ◽  
Full Volume

Abstract In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

scholarly journals Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems

2021 ◽  
Vol 11 (22) ◽  
pp. 10976
Author(s):  
Rana Almohaini ◽  
Iman Almomani ◽  
Aala AlKhayer
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Hybrid System ◽  
State Of The Art ◽  
Detection System ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Hybrid Detection ◽  
Dynamic Analyses ◽  
Data Files

Android ransomware is one of the most threatening attacks that is increasing at an alarming rate. Ransomware attacks usually target Android users by either locking their devices or encrypting their data files and then requesting them to pay money to unlock the devices or recover the files back. Existing solutions for detecting ransomware mainly use static analysis. However, limited approaches apply dynamic analysis specifically for ransomware detection. Furthermore, the performance of these approaches is either poor or often fails in the presence of code obfuscation techniques or benign applications that use cryptography methods for their APIs usage. Additionally, most of them are unable to detect ransomware attacks at early stages. Therefore, this paper proposes a hybrid detection system that effectively utilizes both static and dynamic analyses to detect ransomware with high accuracy. For the static analysis, the proposed hybrid system considered more than 70 state-of-the-art antivirus engines. For the dynamic analysis, this research explored the existing dynamic tools and conducted an in-depth comparative study to find the proper tool to integrate it in detecting ransomware whenever needed. To evaluate the performance of the proposed hybrid system, we analyzed statically and dynamically over one hundred ransomware samples. These samples originated from 10 different ransomware families. The experiments’ results revealed that static analysis achieved almost half of the detection accuracy—ranging around 40–55%, compared to the dynamic analysis, which reached a 100% accuracy rate. Moreover, this research reports some of the high API classes, methods, and permissions used in these ransomware apps. Finally, some case studies are highlighted, including failed running apps and crypto-ransomware patterns.

Online Biscuit Detection System Based on Machine Vision

2013 ◽  
Vol 341-342 ◽  
pp. 597-600
Author(s):  
Xin Wei ◽  
Guang Feng Chen ◽  
Lin Lin Zhai ◽  
Qing Qing Huang
Keyword(s):  
Machine Vision ◽  
Detection Rate ◽  
Detection System ◽  
Control Logic ◽  
Control Software ◽  
Matching Algorithm ◽  
Geometric Matching ◽  
Detection Software ◽  
And Control ◽  
Manipulator Control

In order to complete the automated sorting, the manipulator needs the accurate coordinate and angle information of the biscuits. This article design a machine vision based online biscuit detection system. Devise the hardware structure and control logic. Base on geometric matching algorithm, develop the detection software with NI Vision. The software could acquire video to analysis to get the coordinates of biscuits, and update and exchange the data with manipulator control software. The system has been tested to achieve a complete detection rate about 96%.

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