V. Voronin, E. Semenishchev, A. Zelensky, O. Tokareva, S. Agaian
Image segmentation is the critical step in imaging including applications such as video surveillance and security in controlled areas: detection and recognition of objects, their classification, analysis of crowd behavior, for identification (face recognition), for remote sensing for objects of critical infrastructure for manmade disasters and other hazards. Recently several image segmentations tools have been developed. However, these tools have limitations and sometimes not aureate since the capture devices usually generate low-resolution images, which are mostly noise and blurry. The goal of this study are: (1) To map optimally images into color images to enhance their contrast and the visibility of otherwise obscured details; (2) To perform an automated segmentation analysis using modified Chan and Vese method; and (3) To study the impact of the segmentation evaluation method. Computer simulations on the thermal dataset show that the new segmentation algorithm exhibits better results compared to state-of-the-art techniques.
{"title":"Image segmentation in a quaternion framework for remote sensing applications","authors":"V. Voronin, E. Semenishchev, A. Zelensky, O. Tokareva, S. Agaian","doi":"10.1117/12.2556314","DOIUrl":"https://doi.org/10.1117/12.2556314","url":null,"abstract":"Image segmentation is the critical step in imaging including applications such as video surveillance and security in controlled areas: detection and recognition of objects, their classification, analysis of crowd behavior, for identification (face recognition), for remote sensing for objects of critical infrastructure for manmade disasters and other hazards. Recently several image segmentations tools have been developed. However, these tools have limitations and sometimes not aureate since the capture devices usually generate low-resolution images, which are mostly noise and blurry. The goal of this study are: (1) To map optimally images into color images to enhance their contrast and the visibility of otherwise obscured details; (2) To perform an automated segmentation analysis using modified Chan and Vese method; and (3) To study the impact of the segmentation evaluation method. Computer simulations on the thermal dataset show that the new segmentation algorithm exhibits better results compared to state-of-the-art techniques.","PeriodicalId":443798,"journal":{"name":"Mobile Multimedia/Image Processing, Security, and Applications 2020","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122983956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human body temperature is an important vital sign especially for health monitoring and exercise training. In this study, we propose a CNN plus support vector machine (SVM) approach (CNN-SVM) to estimate body temperature from a sequence of facial images. The sequence images could be from multiple shots or from video frames using a smartphone camera. First, the facial region is cropped out from a digital picture using a face detection algorithm, which can be implemented on the smartphone or at cloud side. Second, normalize the batch of facial images, and extract the facial features using a pretrained CNN model. Lastly, train a body temperature prediction model with the CNN features using a multiclass SVM classifier. The feature extraction and classification are performed in the cloud side with GPU acceleration and the predicted temperature is then sent back to the mobile app for display. We have a facial sequence database from 144 subjects. There are 12-18 shots of facial images taken from each subject. We selected AlexNet, ResNet-50, VGG-19, or Inception-ResNet-v2 models for feature extraction. The initial results show that the performance of the proposed method is very promising.
{"title":"Mobile application for monitoring body temperature from facial images using convolutional neural network and support vector machine","authors":"Yufeng Zheng, Hongyu Wang, Yingguang Hao","doi":"10.1117/12.2557856","DOIUrl":"https://doi.org/10.1117/12.2557856","url":null,"abstract":"Human body temperature is an important vital sign especially for health monitoring and exercise training. In this study, we propose a CNN plus support vector machine (SVM) approach (CNN-SVM) to estimate body temperature from a sequence of facial images. The sequence images could be from multiple shots or from video frames using a smartphone camera. First, the facial region is cropped out from a digital picture using a face detection algorithm, which can be implemented on the smartphone or at cloud side. Second, normalize the batch of facial images, and extract the facial features using a pretrained CNN model. Lastly, train a body temperature prediction model with the CNN features using a multiclass SVM classifier. The feature extraction and classification are performed in the cloud side with GPU acceleration and the predicted temperature is then sent back to the mobile app for display. We have a facial sequence database from 144 subjects. There are 12-18 shots of facial images taken from each subject. We selected AlexNet, ResNet-50, VGG-19, or Inception-ResNet-v2 models for feature extraction. The initial results show that the performance of the proposed method is very promising.","PeriodicalId":443798,"journal":{"name":"Mobile Multimedia/Image Processing, Security, and Applications 2020","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121042924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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