FEASIBILITY ANALYSIS OF AN AUTOMATED DETECTION OF PHYSICAL DEFECT VIA COMPUTER VISION

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Por Jing Zhao ◽  
Shafriza Nisha Basah ◽  
Shazmin Aniza Abdul Shukor
Keyword(s):  
Computer Vision ◽  
Low Cost ◽  
Image Data ◽  
Automated Detection ◽  
Building Construction ◽  
High Demand ◽  
Major City ◽  
Cost System ◽  
Kinect Camera ◽  
Wall Condition

High demand of building construction has been taking places in the major city of Malaysia. However, despite this magnificent development, the lack of proper maintenance has caused a large portion of these properties deteriorated over time. The implementation of the project - Automated Detection of Physical Defect via Computer Vision - is a low cost system that helps to inspect the wall condition using Kinect camera. The system is able to classify the types of physical defects -crack and hole - and state its level of severity.The system uses artificial neural network as the image classifier due to its reliability and consistency. The validity of the system is shown using experiments on synthetic and real image data. This automated physical defect detection could detect building defect early, quickly, and easily, which results in cost saving and extending building life span. 

Image Capture Board for The PC

1995 ◽  
Vol 32 (3) ◽  
pp. 235-255
Author(s):  
T. David Binnie ◽  
I. Reading
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Personal Computer ◽  
Low Cost ◽  
Random Access ◽  
Image Data ◽  
Image Capture ◽  
Design And Implementation

Image capture board for the PC We report the design and implementation of a low cost, image capture board for an IBM type personal computer. The board is particularly suited to computer vision education. The board provides: image capture at video rate, random access to xy addressable image data, and options for on-board image processing hardware.

Low-cost electromyograph combined with markerless pose detection

2021 ◽  
Vol 88 (s1) ◽  
pp. s71-s76
Author(s):  
Florian Scheible ◽  
Raphael Lamprecht ◽  
Marc Rives ◽  
Alexander Sutor
Keyword(s):  
Computer Vision ◽  
Muscle Activity ◽  
Low Cost ◽  
Image Data ◽  
Knee Angle ◽  
Pose Detection ◽  
The Subject ◽  
Sample Rate ◽  
Good Agreement

Abstract This papers presents a low-cost electromyograph combined with marker-less pose detection using computer vision. The developed and build three channel electromyograph is tested by measuring the muscle activity of one leg, while the subject is performing squats. Simultaneously, a camera records the exercise and subsequently the image data is evaluated by OpenPose. We could show that this simple setup enables the user to evaluate the muscle activity of three independent muscles as function of the knee angle. These results are in good agreement to the expected muscle activity. The sample-rate of the EMG device is 2 kHz. The overall cost of the developed device is under 100 €. To our knowledge, this is the first work combining these two methods for dynamic exercises. The method is well customizable for other sports due to the battery powered device and its handy size.

scholarly journals Picroscope: low-cost system for simultaneous longitudinal biological imaging

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Victoria T. Ly ◽  
Pierre V. Baudin ◽  
Pattawong Pansodtee ◽  
Erik A. Jung ◽  
Kateryna Voitiuk ◽  
...  
Keyword(s):  
Low Cost ◽  
Image Data ◽  
Biological Imaging ◽  
Cost System ◽  
Long Time ◽  
Culture Models ◽  
3D Printed ◽  
Longitudinal Imaging ◽  
Printed Materials ◽  
Multiple Conditions

AbstractSimultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here, we propose the Picroscope, which is the first low-cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here, we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish, and planaria worms. We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long-time periods.

scholarly journals Development of a Low-Cost System for Simultaneous Longitudinal Biological Imaging

2021 ◽  
Author(s):  
Victoria T Ly ◽  
Pierre V Baudin ◽  
Pattawong Pansodtee ◽  
Erik A Jung ◽  
Kateryna Voitiuk ◽  
...  
Keyword(s):  
Low Cost ◽  
Image Data ◽  
Biological Imaging ◽  
Cost System ◽  
Long Time ◽  
Culture Models ◽  
3D Printed ◽  
Longitudinal Imaging ◽  
Printed Materials ◽  
Multiple Conditions

Simultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here we propose the Picroscope, which is the first low cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish and planaria worms.We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long time periods.

Low-Cost System Handles Patient Phone Calls

2007 ◽  
Vol 40 (11) ◽  
pp. 53
Author(s):  
BRUCE K. DIXON
Keyword(s):  
Low Cost ◽  
Cost System ◽  
Phone Calls

Intelligent, low-cost system for fume control during laser material processing

2005 ◽  
Author(s):  
Ramin Sattari ◽  
Stephan Barcikowski ◽  
Thomas Püster ◽  
Andreas Ostendorf ◽  
Heinz Haferkamp
Keyword(s):  
Material Processing ◽  
Low Cost ◽  
Laser Material Processing ◽  
Laser Material ◽  
Cost System

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals A Low-cost System for High-frequency Solar Imagery and Power Data Acquisition

2021 ◽  
Vol 1826 (1) ◽  
pp. 012082
Author(s):  
G F Bassous ◽  
R F Calili ◽  
C R H Barbosa
Keyword(s):  
Data Acquisition ◽  
High Frequency ◽  
Low Cost ◽  
Cost System
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