Moya-Albor, Ernesto
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Moya-Albor, Ernesto
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Moya Albor, Ernesto
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emoya
Moya-Albor, E.
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55523223700
Researcher ID
J-1891-2019

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A Computer Vision Approach to Terminus Movement Analysis of Viedma Glacier

2023 , Moya-Albor, Ernesto , Schwartzman, Armin , Brieva, Jorge , Pardo, Mauricio , Ponce, Hiram , Chávez-Domínguez, Rodrigo

In this paper, an automatic segmentation approach of the Viedma glacier terminus is proposed. The method uses multi-spectral images from the Landsat-5 satellite to determine the area of the glacier through computer vision techniques. The area of the glacier is estimated, and a linear model is fitted, obtaining a correlation of 0.968 between the measured area and a fit linear regression model. On the other hand, a bio-inspired optical flow estimation approach is used to calculate and visualize the displacement of the glacier through time. In addition, an analysis is performed between the temperature variation in the Southern Cone and the decrease of the glacier in the function of time. A linear trend (r2=0.95) shows that the analyzed area of the glacier has decreased by about 1.9% annually in the observation season. It reveals an inverse relationship between the change in the size of the glacier and global warming, showing that if the same conditions remain, the glacier's zone analyzed in this work would be close to its disappearance in around 70 years, the time lapse in which a global temperature increase of 1.24 oC would be reached. © 2023 IEEE.

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Non-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networks

2020 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram

In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ˜ 98 %. © 2020 SPIE

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Non-contact breathing rate monitoring system using a magnification technique and convolutional networks

2020 , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and a system based on a Convolutional Neural Network (CNN). After the magnification procedure, a CNN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. Two strategies are used as input to the CNN. A CNN-ROI proposal where a region of interest is selected manually on the image frame and in the second case, a CNN-Whole-Image proposal where the entire image frame is selected. Finally, the RR is estimated from the classified frames. The CNN-ROI proposal is tested on five subjects in lying face down position and it is compared to a procedure using different image processing steps to tag the frames as inhalation or exhalation. The mean average error in percentage obtained for this proposal is 2.326±1.144%. The CNN-whole-image proposal is tested on eight subjects in lying face down position. The mean average error in percentage obtained for this proposal is 2.115 ± 1.135%. © COPYRIGHT 2020 SPIE. Downloading of the abstract is permitted for personal use ONLY.

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A perceptive watermarking approach applied to COVID-19 imaging data

2020 , Gomez-Coronel, Sandra L. , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This work presents a watermarking algorithm applied to medical images of COVID-19 patients. The principal objective is to protect the information of the patient using an imperceptible watermarking and to preserve its diagnose. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images. © 2020 SPIE

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