Espinosa Loera, Ricardo Abel

Main Affiliation

Facultad de Ingeniería - CampAGS

Preferred name

Official Name

Espinosa Loera, Ricardo Abel

ORCID

0000-0003-2573-7853

Scopus Author ID

57211517018

Now showing 1 - 10 of 20

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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
13
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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
15
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A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-Based Photometric Image Enhancement Models
(2022)
Axel García-Vega
;
Espinosa Loera, Ricardo Abel
;
Gilberto Ochoa-Ruiz
;
Thomas Bazin
;
Luis Falcón-Morales
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumination artefacts due to the enlightenment source orientation. There exist two major issues when the endoscope’s light source pose suddenly changes: overexposed and underexposed tissue areas are produced. These two scenarios can result in misdiagnosis due to the lack of information in the affected zones or hamper the performance of various computer vision methods (e.g., SLAM, structure from motion, optical flow) used during the non invasive examination. The aim of this work is two-fold: i) to introduce a new synthetically generated data-set generated by a generative adversarial techniques and ii) and to explore both shallow based and deep learning-based image-enhancement methods in overexposed and underexposed lighting conditions. Best quantitative results (i.e., metric based results), were obtained by the deep learning-based LMSPEC method, besides a running time around 7.6 fps.
Scopus© Citations 6 1 11
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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
18
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Prompt Assisted Enhancement for Correcting Illumination Artifacts in Endoscopic Images
(Springer Nature Switzerland, 2025-10-24)
Espinosa Loera, Ricardo Abel
;
Eluney Hernández
;
Javier Cerriteño Magaña
;
Gilberto Ochoa
;
Christian Daul
13
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Design and Implementation of a Node Geolocation System for Fire Monitoring through LoRaWAN
(2020)
Gutiérrez, Sebastián
;
Gutiérrez, Sebastián
;
Espinosa Loera, Ricardo Abel
Scopus© Citations 4 1 14
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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
Scopus© Citations 3 2 35
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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
Scopus© Citations 10 1 9
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Click-event sound detection in automotive industry using machine/deep learning
(2021)
Espinosa Loera, Ricardo Abel
;
Ponce, Hiram
;
Gutiérrez, Sebastián
In the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from −16.67 dB to −12.87 dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of 94.55±0.83 %. To the best of our knowledge, this is the first time a click sounds detection system in assembling electrical harnesses of engines for giving feedback to the workers is proposed and implemented in a real-world automotive production line. We consider this work valuable for the automotive industry on how to apply ML/DL approaches for improving the quality of semi-automatic assembly operations. © 2021 Elsevier B.V.
Scopus© Citations 22 2 31
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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.
2Scopus© Citations 31 18

Espinosa Loera, Ricardo Abel

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