Implementing Computer Vision in Android Apps and Presenting the Background Technology with Mathematical Demonstrations

The aim of this paper is to create image-processing Android apps to launch on the Google Play Store. Three apps with different usages will be presented for different situations. The first app is a night-vision app on an Android phone that uses OpenCV. The second app is a tooth-brushing assistant application. The app is made for mobile phones and uses advanced image-processing techniques to detect when the tooth is brushed correctly or incorrectly. The main focus is on the direction of the toothbrush movement because this is one of the key aspects of correctly brushing teeth. The direction of movement of the brush is detected using movement vectors. The third app is a lane-detection app on the smartphone. Lane detection is carried out using OpenCV and TensorFlow libraries. The mobile app was implemented on the Android operating system. The app has a live video feed of the surroundings. When in the area of view, there will be a road with a lane. The system detects the lane and draws a green line over it.

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Publication Details

Journal
Technologies
Published
2025-01-09
DOI
https://doi.org/10.3390/technologies13010027
Citations
1
Primary Topic
Video Surveillance and Tracking Methods
Type
article
Field-Weighted Citation Impact
1.28
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article

Implementing Computer Vision in Android Apps and Presenting the Background Technology with Mathematical Demonstrations

Roland Szabó
1 citations
Technologies
Video Surveillance and Tracking Methods
1.28
article

Implementing Computer Vision in Android Apps and Presenting the Background Technology with Mathematical Demonstrations

Roland Szabó
article en
1 citations

Abstract

The aim of this paper is to create image-processing Android apps to launch on the Google Play Store. Three apps with different usages will be presented for different situations. The first app is a night-vision app on an Android phone that uses OpenCV. The second app is a tooth-brushing assistant application. The app is made for mobile phones and uses advanced image-processing techniques to detect when the tooth is brushed correctly or incorrectly. The main focus is on the direction of the toothbrush movement because this is one of the key aspects of correctly brushing teeth. The direction of movement of the brush is detected using movement vectors. The third app is a lane-detection app on the smartphone. Lane detection is carried out using OpenCV and TensorFlow libraries. The mobile app was implemented on the Android operating system. The app has a live video feed of the surroundings. When in the area of view, there will be a road with a lane. The system detects the lane and draws a green line over it.

TechnologiesVol. 13(1)
Polytechnic University of Timişoara (RO)
Sustainable cities and communities
Openalex Percentile: Top 24%
Video Surveillance and Tracking Methods
1.28
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Implementing Computer Vision in Android Apps and Presenting the Background Technology with Mathematical Demonstrations — Roland Szabó · Technologies (2025) | TGRS Research Map | TGRS