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
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J-1891-2019

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Now showing 1 - 10 of 72
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Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral Gradient

2024 , Diego Renza , Moya-Albor, Ernesto , Adrian Chavarro

The main objective of a machine learning (ML) system is to obtain a trained model from input data in such a way that it allows predictions to be made on new i.i.d. (Independently and Identically Distributed) data with the lowest possible error. However, how can we assess whether the training and test data have a similar distribution? To answer this question, this paper presents a proposal to determine the degree of distribution shift of two datasets. To this end, a metric for evaluating complexity in datasets is used, which can be applied in multi-class problems, comparing each pair of classes of the two sets. The proposed methodology has been applied to three well-known datasets: MNIST, CIFAR-10 and CIFAR-100, together with corrupted versions of these. Through this methodology, it is possible to evaluate which types of modification have a greater impact on the generalization of the models without the need to train multiple models multiple times, also allowing us to determine which classes are more affected by corruption.

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Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review

2024-01-01 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram , Escobedo-Gordillo, Andrés

Nowadays, the healthcare is a priority for both governments and persons. Vital sign monitoring allows knowing the health status and is widely used for prevention, diagnosis, and treatment of determined illnesses. In particular, breathing and heart rate are traditionally considered the most relevant and accessible vital signs. However, oxygen saturation was essential in the COVID-19 pandemic. On the other hand, contact techniques to estimate these vital signs are a standard monitoring reference. However, non-contact estimation methods have gained relevance in the last few years in those cases where there is the possibility of suffering stress, pain, and skin irritation in specific situations, as in the case of vulnerable skin in burn patients and neonates. In this chapter, a review of contactless video-based vital-sign methods is presented. The selected methods have a data-driven approach as an alternative when there is not theoretical model of the physiological phenomenon. Finally, a new framework with a general data-driven approach to estimate the most used vital signs is proposed. This framework includes a region of interest extraction stage, a video magnification technique to reveals subtle changes, and a machine learning method to estimate the vital signs. In addition, each step describes some recommendations and best practices found ©Springer.

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Nuclear density analysis in microscopic images for the characterization of retinal geographic atrophy

2020 , Peralta Ildefonso, Martha Janneth , Moya-Albor, Ernesto , Brieva, Jorge , Lira, Esmeralda , Pérez Ortiz, Andric Christopher , Coral-Vázquez, Ramón , Estrada Mena, Francisco Javier

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in industrialized countries. It is estimated that AMD affects at least 1 in 10 Hispanics. Previous reports have shown that AMD has multiple risk factors. Recently, we demonstrated that some genetic variants in the SGCD gene are involved in AMD developments, especially in early-stage (geographic atrophy, GA). Therefore, to evaluate the relationship between SGCD's absence and the loss of photoreceptors in GA, we worked with a genetically modified mouse model, SGCD deficient (Sgcd-/-) and a control mouse C57BL/6J (Sgcd+/+). First, we obtained hematoxylin and eosin (H&E) retina staining microscopic images. Then, we coarsely selected the outer and inner nuclear retinal layer (ONL and INL respectively) and finally, we applied an automatic nuclei segmentation to calculate the nuclear density in each region. Our results showed that Sgcd absence does not result in photoreceptor loss, on the contrary, it promotes an increment in nuclear density by 8.7% in ONL and 20.1% in INL compared with control eyes (p = 0.0033 and p < 0.0001 respectively). This could be explained by the fact that SGCD codifies the delta-sarcoglycan protein and there is evidence that showed a relationship between the absence of this protein with the activation of a cell proliferation signaling pathway. Finally, our results show that the delta-sarcoglycan protein could play an important role in the pathogenesis of the geographic atrophy. Moreover, there are promising perspectives for the systematic approach applied for cell image analysis, as an important tool to determine the nuclear density for assessing the progression of AMD. ©COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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GABOT: Garbage Autonomous Collector for Indoors at Low Cost

2019 , Mayorga, Carlos , Gómez, Cristina , Díaz Ramos, Gabriel , Vázquez, Carlos , Kobayashi, Rafael , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

The constant raise in the levels of garbage in our city contributes to the worldwide pollution nowadays and this becoming a health issue for the entire world. Another problem is to avoid more garbage collectors get ill because of the exposure they have to solid wastes that can make them have health issues. This lead us to design a garbage collector that can move around looking for garbage and can store it in a specific container. It will have a manipulator that will be moving thanks to a vehicle that can avoid obstacles. A camera will detect the garbage whenever it is in front of it and the manipulator will take it. In this work we propose a proof of concept of a mechatronic system that can detect and pick up garbage on a zone using a camera and sensors. © 2019 IEEE.

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A novel artificial organic control system for mobile robot navigation in assisted living using vision- and neural-based strategies

2018 , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Robots in assisted living (RAL) are an alternative to support families and professional caregivers with a wide range of possibilities to take care of elderly people. Navigation of mobile robots is a challenging problem due to the uncertainty and dynamics of environments found in the context of places for elderly. To accomplish this goal, the navigation system tries to replicate such a complicated process inspired on the perception and judgment of human beings. In this work, we propose a novel nature-inspired control system for mobile RAL navigation using an artificial organic controller enhanced with vision-based strategies such as Hermite optical flow (OF) and convolutional neural networks (CNNs). Particularly, the Hermite OF is employed for obstacle motion detection while CNNs are occupied for obstacle distance estimation. We train the CNN using OF visual features guided by ultrasonic sensor-based measures in a 3D scenario. Our application is oriented to avoid mobile and fixed obstacles using a monocular camera in a simulated environment. For the experiments, we use the robot simulator V-REP, which is an integrated development environment into a distributed control architecture. Security and smoothness metrics as well as quantitative evaluation are computed and analyzed. Results showed that the proposed method works successfully in simulation conditions.

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Mobile Robot with Movement Detection Controlled by a Real-Time Optical Flow Hermite Transform

2015 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This chapter presents a new algorithm inspired in the human visual system to compute optical flow in real-time based on the Hermite Transform. This algorithm is applied in a vision-based control system for a mobile robot. Its performance is compared for different texture scenarios with the classical Horn and Schunck algorithm. The design of the nature-inspired controller is based on the agent-environment model and agent’s architecture. Moreover, a case study of a robotic system with the proposed real-time Hermite optical flow method was implemented for braking and steering when mobile obstacles are close to the robot. Experimental results showed the controller to be fast enough for real-time applications, be robust to different background textures and colors, and its performance does not depend on inner parameters of the robotic system. © Springer International Publishing Switzerland 2016.

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Computer-Aided Diagnosis of Diabetic Retinopathy Lesions Based on Knowledge Distillation in Fundus Images

2024 , Moya-Albor, Ernesto , Alberto Leandro Figueroa Soliz , Sebastian Herrera Uribe , Diego Renza , Brieva, Jorge

At present, the early diagnosis of diabetic retinopathy (DR), a possible complication of diabetes due to elevated glucose concentrations in the blood, is usually performed by specialists using a manual inspection of high-resolution fundus images based on lesion screening, leading to problems such as high work-intensity and accessibility only in specialized health centers. To support the diagnosis of DR, we propose a deep learning-based (DL) DR lesion classification method through a knowledge distillation (KD) strategy. First, we use the pre-trained DL architecture, Inception-v3, as a teacher model to distill the dataset. Then, a student model, also using the Inception-v3 model, is trained on the distilled dataset to match the performance of the teacher model. In addition, a new combination of Kullback–Leibler (KL) divergence and categorical cross-entropy (CCE) loss is used to measure the difference between the teacher and student models. This combined metric encourages the student model to mimic the predictions of the teacher model. Finally, the trained student model is evaluated on a validation dataset to assess its performance and compare it with both the teacher model and another competitive DL model. Experiments are conducted on the two datasets, corresponding to an imbalanced and a balanced dataset. Two baseline models (Inception-v3 and YOLOv8) are evaluated for reference, obtaining a maximum training accuracy of 66.75% and 90.90%, respectively, and a maximum validation accuracy of 35.94% and 81.52%, both for the imbalanced dataset. On the other hand, the proposed DR classification model achieves an average training accuracy of 99.01% and an average validation accuracy of 97.30%, overcoming the baseline models and other state-of-the-art works. Experimental results show that the proposed model achieves competitive results in DR lesion detection and classification tasks, assisting in the early diagnosis of diabetic retinopathy.

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A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

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

The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect 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 the automatic strategy is 3.28± 3.33% with and agreement with respect of the reference of 98%. © 2020 by the authors.

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Motion estimation and segmentation in CT cardiac images using the Hermite transform and active shape models

2013 , Boris Escalante-Ramírez , Ernesto Moya-Albor , Leiner Barba-J , Fernando Arambula Cosio , Enrique Vallejo , Andrew G. Tescher

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Challenges and trends in multimodal fall detection for healthcare

2020 , Ponce, Hiram , Brieva, Jorge , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , HIRAM EREDIN PONCE ESPINOSA;376768 , JORGE EDUARDO BRIEVA RICO;121435

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.

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