Espinosa Loera, Ricardo Abel
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Espinosa Loera, Ricardo Abel
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Espinosa Loera, Ricardo Abel
Alternative Name
respinosa
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ORCID
Scopus Author ID
57211517018

Research Output
Now showing 1 - 10 of 17
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Color-aware Exposure Correction for Endoscopic Imaging using a Lightweight Vision Transformer

2024 , Espinosa Loera, Ricardo Abel , Eluney Hernández , Gilberto Ochoa-Ruiz , Christian Daul

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Design and Implementation of a Node Geolocation System for Fire Monitoring through LoRaWAN

2020 , Pedro Luna , Gutiérrez, Sebastián , Espinosa Loera, Ricardo Abel

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Renewable Energy Prediction through Machine Learning Algorithms

2020 , Luisa Fernanda Jimenez Alvarez , Sebastian Ramos Gonzalez , Antonio Delgado Lopez , Diego Alonso Hernandez Delgado , Espinosa Loera, Ricardo Abel , Gutiérrez, Sebastián

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A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

2019 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Moya-Albor, Ernesto , Martinez-Villaseñor, Lourdes , Brieva, Jorge , Gutiérrez, Sebastián

The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture. © 2019 Elsevier Ltd

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Un enfoque basado en la visión para la detección de caídas utilizando múltiples cámaras y redes neuronales convolucionales: un caso de estudio en UP-Fall Detection Data-Set

2019-12 , Espinosa Loera, Ricardo Abel , HIRAM EREDIN PONCE ESPINOSA;376768 , JOSÉ SEBASTIÁN GUTIÉRREZ CALDERÓN;494470 , Ponce, Hiram , Gutiérrez, Sebastián , Campus Aguascalientes

Actualmente, el reconocimiento automático de caídas humanas es un tema de investigación importante para la visión por computadora y la comunidad de la inteligencia artificial. Para el análisis de imágenes, es común usar un enfoque basado en visión para la detección de caídas y sistemas de clasificación debido al aumento exponencial actual en el uso de cámaras. Además, las técnicas de deep learning han revolucionado las técnicas basadas en visión. Han sido consideradas robustas y confiables en la detección y clasificación de problemas, principalmente usando Redes Neuronales Convolucionales (CNN). Recientemente, nuestro grupo de investigación lanzo un nuevo Data Set multimodal para la detección de caídas (Up-Fall Detecction dataset), y se requieren diferentes estudios de enfoques de modalidades para la detección y clasificación de caídas. Centrándonos solo en un enfoque basado en visión, en este articulo presentamos un sistema de detección de caídas basado en 2D CNN como método de inferencia y varias cámaras. Este enfoque analiza imágenes en marcos de ventana de tiempo fijo que extraen características utilizando un método de flujo óptico que obtiene información de movimiento relativo entre dos imágenes consecutivas. Para resultados experimentales, probamos este enfoque en nuestro dataset público. Los resultados mostraron que nuestra propuesta de enfoque basado en la visión múltiple detecta caídas humanas que alcanzan un 95.64% de precisión con una arquitectura de red CNN simple en comparación con otros métodos de vanguardia.

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Click Event Sound Detection Using Machine Learning in Automotive Industry

2020 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián , Hernández Cornu, Javier Eluney

Artificial intelligence has been playing an important role when it comes to the automotive industry and its quality of assemblies in the production line, this is because since the arrival of the industry 4.0 it has been subject to change and continuous improvement. In the past, we’ve observed how many machine learning architectures have been used to create environmental sound classification systems in order to improve traditional systems, thus overcoming efficiency issues with great results. In this work, we present a machine learning solution/approach for click event sound detection using audio sensors that are used in the assembly of electric harnesses for engines, this being done on an automotive production line, where we divided our workflow into: data collection, pre-processing, feature extraction, training and inference and finally the detection of the click event sounds. We created a dataset that is composed by 25,000 audio files that have an average duration of 0.025 seconds per click sound with the purpose of training a Multi-layer Perceptron and bring it into the inference phase. In order to test this approach, we’ve performed various implementations in a laboratory and in the real automotive industry. We obtained 95.23% in F1-Score Metric in a laboratory, while in real conditions, we obtained less reliable results, as 84.00% as the best results. © 2020, Springer Nature Switzerland AG.

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Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

2021 , Ponce, Hiram , Cevallos, Claudio , Gutiérrez, Sebastián , Espinosa Loera, Ricardo Abel

Tomato crops are considered the most important agricultural products worldwide. However, the quality of tomatoes depends mainly on the nutrient levels. Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants. Recently, precision agriculture has explored opportunities to automate nutrient level monitoring. Previous work has demonstrated that a convolutional neural network (CNN) is able to estimate low nutrients in tomato plants using images of their leaves. However, the performance of the CNN was not adequate. Thus, this work proposes a novel CNNbased classifier, namely CNN+AHN, for estimating low nutrients in tomato crops using an image of the tomato leaves. The CNN+AHN incorporates a set of convolutional layers as the feature extraction part, and a supervised learning method called artificial hydrocarbon network (AHN) as the dense layer. Different combinations of the architecture of CNN+AHN were examined. Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95:57% and F1-score of 95:75%, outperforming the literature.

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A deep learning-based image pre-processing pipeline for enhanced 3D colon surface reconstruction robust to endoscopic illumination artifacts

2024 , Espinosa Loera, Ricardo Abel , Javier Cerriteño , Saul Gonzalez-Dominguez , Gilberto Ochoa-Ruiz , Christian Daul

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Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging

2023 , Axel García-Vega , Espinosa Loera, Ricardo Abel , Luis Ramírez-Guzmán , Thomas Bazin , Luis Falcón-Morales , Gilberto Ochoa-Ruiz , Dominique Lamarque , Christian Daul

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GYMetricPose: A light-weight angle-based graph adaptation for action quality assessment

2024 , Ulises Pajares Gallardo , Martin Fernando Caro Zamorano , Javier Eluney Hernández Cornu , Espinosa Loera, Ricardo Abel , Gilberto Ochoa-Ruiz

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