Embedded target detection system based on raspberry pi system

Gamal, Bahaa Eldin; Ouda, Ahmed Nasr; Elhalwagy, Yehia Z.; Elnashar, Gamal

doi:10.1109/icenco.2016.7856461

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…By transforming, noise reduction, and noise reduction to reduce the useless information in the image, remove the noise in the image noise, and highlight data features for edge detection [11][12][13][14], feature extraction, image segmentation, and character recognition, the digital image processing aims to reduce the useless information in the image are used. In order to perform edge detection, feature extraction, image segmentation, and character recognition later, digital image processing reduces the image's unnecessary information, removes image noise, and highlights the features of the data image.…”

Section: Algorithm Implementationmentioning

confidence: 99%

Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Yan

Wanting

Liu

et al. 2022

MIST

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This study proposed an intelligent algorithm based on digital image processing and character recognition to address the current situation in which the National Medical Products Administration promulgated the relevant regulations on the complete prohibition of the use of mercury sphygmomanometers in 2020 and the currently widely used electronic sphygmomanometers need to undergo regular verification and quality testing. The intelligent algorithm made it possible to automatically acquire the electronic sphygmomanometer indication value during quality assurance or verification. The images of the electronic sphygmomanometer were captured using a Raspberry Pi-connected video camera head; in the software development, automatic detection of the electronic sphygmomanometer's indication value was achieved by running the computer vision-based OpenCV library on the Raspberry Pi and utilizing image preprocessing techniques like scale transformation, grayscale conversion, Gaussian smoothing and edge detection, and character segmentation. The development of intelligent devices for the automatic verification of electronic sphygmomanometers has a technical foundation in the research and design of the digital recognition algorithm, and it has a certain reference value for character recognition of electronic instruments or the creation of automatic instrument indication value recording devices.

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Section: Algorithm Implementationmentioning

confidence: 99%