2P1-N-033 A 3-D Image Acqusition System Using a Varifocal Mirror(Machine Vision and Visual Inspection,Mega-Integration in Robotics and Mechatronics to Assist Our Daily Lives)

2005 ◽  
Vol 2005 (0) ◽  
pp. 185
Author(s):  
Kouhei KITAGAWA ◽  
Toshinori TAMAI ◽  
Shinichi HIRAI ◽  
Akira ISHII
Keyword(s):  
Machine Vision ◽  
Visual Inspection ◽  
Daily Lives ◽  
Mirror Machine

A Comparison of Orthogonal Transforms in Engineering Computer Vision

1990 ◽  
Vol 204 (5) ◽  
pp. 321-327 ◽  
Author(s):  
G A H Al-Kindi ◽  
R M Baul ◽  
K F Gill
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Visual Inspection ◽  
Computational Time ◽  
Transform Coefficients ◽  
Orthogonal Transforms ◽  
The Impact

A comparison of a number of commonly used orthogonal transforms, when applied to the recognition and visual inspection of engineering components, has been made. The impact on the performance and computational time for the machine vision process due to varying numbers of transform coefficients is assessed.

scholarly journals INTELLIGENT MACHINE VISION BASED RAILWAY INFRASTRUCTURE INSPECTION AND MONITORING USING UAV

2019 ◽  
Vol 17 (3) ◽  
pp. 357 ◽  
Author(s):  
Milan Banić ◽  
Aleksandar Miltenović ◽  
Milan Pavlović ◽  
Ivan Ćirić
Keyword(s):  
Machine Vision ◽  
Visual Inspection ◽  
Cost Effective ◽  
Extraction Methods ◽  
Ease Of Use ◽  
Inspection System ◽  
Infrastructure Monitoring ◽  
Vision Sensors ◽  
Railway Infrastructure ◽  
Infrastructure Inspection

Traditionally, railway inspection and monitoring are considered a crucial aspect of the system and are done by human inspectors. Rapid progress of the machine vision-based systems enables automated and autonomous rail track detection and railway infrastructure monitoring and inspection with flexibility and ease of use. In recent years, several prototypes of vision based inspection system have been proposed, where most have various vision sensors mounted on locomotives or wagons. This paper explores the usage of the UAVs (drones) in railways and computer vision based monitoring of railway infrastructure. Employing drones for such monitoring systems enables more robust and reliable visual inspection while providing a cost effective and accurate means for monitoring of the tracks. By means of a camera placed on a drone the images of the rail tracks and the railway infrastructure are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The preliminary obtained results are promising.

scholarly journals Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

2019 ◽  
Author(s):  
Ruofeng Wei ◽  
Yunbo Bi
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defects ◽  
Average Precision ◽  
Saliency Maps ◽  
Aluminum Profile ◽  
Safety And Reliability ◽  
Aluminum Profiles

Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

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