Medical Image Fusion using Local IFS-Entropy in NSST Domain by Stimulating PCNN

Mishra, Niladri Shekhar; Dhabal, Supriya

doi:10.1109/icce50343.2020.9290666

Cited by 2 publications

(1 citation statement)

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“…Finally, the segmentation results are filtered and merged hierarchically into groups. The typical algorithm is Fast R-CNN (Fast Region-based Convolutional Neural Network) [9]. The pedestrian detection method [10] based on candidate regions and PCNN (Parallel Convolutional Neural Network) improves the selective indexing for the extraction of candidate regions, which effectively solves the problem of people in the image.…”

Section: Literature Surveymentioning

confidence: 99%

Image Recognition Based on Deep Learning with Thermal Camera Sensing

Sung¹,

Lin²,

Hsiao³

2023

Computer Systems Science and Engineering

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As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost.

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Section: Literature Surveymentioning

confidence: 99%