Computer vision application programming for settlement monitoring in a drainage tunnel

Most mobile devices nowadays have a camera. Besides, posting and sharing images have been found as one of the most frequent and engaging Internet activities. However, to our knowledge, no research has explored the feasibility of asking respondents of online surveys to upload images to answer survey questions. The main goal of this article is to investigate the viability of asking respondents of an online opt-in panel to upload during a mobile web survey: First, a photo taken in the moment, and second, an image already saved on their smartphone. In addition, we want to test to what extent the Google Vision application programming interface (API), which can label images into categories, produces similar tags than a human coder. Overall, results from a survey conducted among millennials in Spain and Mexico ( N = 1,614) show that more than half of the respondents uploaded an image. Of those, 77.3% and 83.4%, respectively, complied with what the question asked. Moreover, respectively, 52.4% and 65.0% of the images were similarly codified by the Google Vision API and the human coder. In addition, the API codified 1,818 images in less than 5 min, whereas the human coder spent nearly 35 hours to complete the same task.

Download Full-text

A review of computer vision-based approaches for physical rehabilitation and assessment

Multimedia Systems ◽

10.1007/s00530-021-00815-4 ◽

2021 ◽

Author(s):

Bappaditya Debnath ◽

Mary O’Brien ◽

Motonori Yamaguchi ◽

Ardhendu Behera

Keyword(s):

Computer Vision ◽

Human Motion ◽

Physical Rehabilitation ◽

Human Motion Analysis ◽

Functional Rehabilitation ◽

Physical Impairments ◽

The Past ◽

Future Direction ◽

Assessment Research ◽

Vision Application

AbstractThe computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered.

Download Full-text

Computer vision application to determine crowdedness in public transport stations

10.1109/ecai52376.2021.9515102 ◽

2021 ◽

Author(s):

Razvan Andrei Gheorghiu ◽

Valentin Iordache ◽

Valentin Alexandru Stan

Keyword(s):

Computer Vision ◽

Public Transport ◽

Vision Application

Download Full-text

LPDDR4 SIPI Co-Simulation and Measurement Correlation for IOT Computer Vision Application

2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) ◽

10.1109/epeps47316.2019.193230 ◽

2019 ◽

Author(s):

Benjamin Silva ◽

Mohamed Eldessouki ◽

Yan Fen Shen

Keyword(s):

Computer Vision ◽

Vision Application

Download Full-text

INSPECTION AND QUALITY CHECKING OF CERAMIC CUP USING MACHINE VISION TECHNIQUE: DESIGN AND ANALYSIS

Jurnal Teknologi ◽

10.11113/jt.v79.11280 ◽

2017 ◽

Vol 79 (5-2) ◽

Author(s):

Nursabillilah Mohd Ali ◽

Mohd Safirin Karis ◽

Siti Azura Ahmad Tarusan ◽

Gao-Jie Wong ◽

Mohd Shahrieel Mohd Aras ◽

...

Keyword(s):

Image Processing ◽

Computer Vision ◽

Machine Vision ◽

Image Sensor ◽

Histogram Method ◽

Inspection Method ◽

Inspection Process ◽

Set Up ◽

Vision Application ◽

Vision Technique

The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.

Download Full-text

Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090532 ◽

2020 ◽

Vol 9 (9) ◽

pp. 532 ◽

Cited By ~ 1

Author(s):

Xiaohui Liu ◽

Bandana Kar ◽

Francisco Alejandro Montiel Ishino ◽

Chaoyang Zhang ◽

Faustine Williams

Keyword(s):

Risk Communication ◽

Emergency Response ◽

Application Programming Interface ◽

Accurate Information ◽

Text And Image ◽

Application Programming ◽

Text Information ◽

Efficient Processing ◽

Information Verification ◽

Vision Application

While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms.

Download Full-text

SCREEN IMAGE SEGMENTATION AND CORRECTION FOR A COMPUTER DISPLAY

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001414540020 ◽

2014 ◽

Vol 28 (04) ◽

pp. 1454002 ◽

Cited By ~ 5

Author(s):

YUNG-SHENG CHEN ◽

KUN-LI LIN

Keyword(s):

Computer Vision ◽

Image Segmentation ◽

Affine Transformation ◽

Physical World ◽

Screen Image ◽

Digital World ◽

The Look ◽

The Given ◽

Deformation Correction ◽

Vision Application

Perception of content displayed on the screen of a computer display using computer vision is a challenging topic if the treated target is changed from physical world to digital world. Screen area from the given computer display image should be segmented and corrected primarily before perceiving the content displayed on the screen. An automatic approach is proposed to the segmentation and deformation correction of screen area for a computer display image. Due to some inherent characteristics existing on ordinary computer displays, the segmentation can be performed by contour tracing. After contouring the screen area, its four corner locations can be readily identified. By approximating the obtained corners to the closest normal screen region, the deformed screen image can be further restored with affine transformation. As a computer vision application on the "look at" screen image, the effectively segmented screen region can be fixed after a little time. The experiments demonstrate that about 70% cases can be fixed under 33 processed frames, others under 51 processed frames, and thus confirm the feasibility of the proposed approach.

Download Full-text