Ana Beatriz Alvarez

Scopus Publications

Illumination-Robust Conjunctival Image Preprocessing for Accurate Segmentation and Anemia Detection Using Deep Learning
Jose Humberto Fuentes-Beingolea, Facundo Palomino-Quispe, Julio Cesar Herrera-Levano, Willy Vargas-Mateos, Ruben Florez, Ana Beatriz Alvarez
International Journal of Online and Biomedical Engineering, 2025
Anemia, defined by reduced hemoglobin or red blood cell levels, remains a critical public health issue, particularly in resource-limited settings where traditional diagnostics are inaccessible. Non-invasive detection via ocular conjunctiva imaging offers a viable solution but is challenged by illumination variability in outdoor environments. This study introduces a novel preprocessing pipeline to standardize conjunctival images, employing grayscale histogram normalization, LAB color space-based glare inpainting, and adaptive contrast enhancement to counter uneven lighting and reflections. Segmentation performance was assessed using U-Net, BiSeNet, and ConjunctiveNet; U-Net outperformed the others, achieving a precision of 84.22% with preprocessing versus 80.08% without preprocessing. For anemia classification, an artificial neural network (ANN), CNN-ResNet, and SLIC-GAT models were tested on the CP-AnemiC (Ghana) and Eyes-defy-anemia (India) datasets. Preprocessing significantly boosted ANN accuracy from 81.54% to 85.51% (Ghana) and 85.94% to 88.28% (India), with precision increasing by up to 6.33%. For CNN-ResNet, F1-scores improved from 81.91% to 89.15% (Ghana), while for ANN on the India dataset, F1-scores increased from 85.73% to 87.35%. These results highlight the pipeline’s ability to enhance segmentation accuracy and classification reliability, reducing false positives and enabling robust anemia detection under variable lighting, thus advancing non-invasive diagnostics for field applications.
Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model
Gustavo de Souza Ferreti, Thuanne Paixão, Ana Beatriz Alvarez
Applied Sciences Switzerland, 2025
The generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their applicability on devices with moderate resources. In this context, this paper presents a lightweight architecture for reconstructing HDR images from three Low-Dynamic-Range inputs. The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. The evaluation of the proposal is conducted through quantitative, qualitative, and computational cost analyses based on the number of parameters and FLOPs. Regarding the qualitative analysis, a comparison between the models was performed using samples that present reconstruction challenges. The proposed model achieves a PSNR-μ of 43.51 dB and SSIM-μ of 0.9917, achieving competitive quality metrics comparable to HDR-GAN while reducing the computational cost by 6× in FLOPs and 7× in the number of parameters, using approximately half the GPU memory consumption, demonstrating an effective balance between visual fidelity and efficiency.
A Review Toward Deep Learning for High Dynamic Range Reconstruction
Gabriel de Lima Martins, Josue Lopez-Cabrejos, Julio Martins, Quefren Leher, Gustavo de Souza Ferreti, Lucas Hildelbrano Costa Carvalho, Felipe Bezerra Lima, Thuanne Paixão, Ana Beatriz Alvarez
Applied Sciences Switzerland, 2025
High Dynamic Range (HDR) image reconstruction has gained prominence in a wide range of fields; not only is it implemented in computer vision, but industries such as entertainment and medicine also benefit considerably from this technology due to its ability to capture and reproduce scenes with a greater variety of luminosities, extending conventional levels of perception. This article presents a review of the state of the art of HDR reconstruction methods based on deep learning, ranging from classical approaches that are still expressive and relevant to more recent proposals involving the advent of new architectures. The fundamental role of high-quality datasets and specific metrics in evaluating the performance of HDR algorithms is also discussed, as well as emphasizing the challenges inherent in capturing multiple exposures and dealing with artifacts. Finally, emerging trends and promising directions for overcoming current limitations and expanding the potential of HDR reconstruction in real-world scenarios are highlighted.
Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
Jonatan Fragoso, Clécio Silva, Thuanne Paixão, Ana Beatriz Alvarez, Olacir Castro Júnior, Ruben Florez, Facundo Palomino-Quispe, Lucas Graciolli Savian, Paulo André Trazzi
Applied Sciences Switzerland, 2025
Coffee cultivation is vital to the global economy, but faces significant challenges with diseases such as rust, miner, phoma, and cercospora, which impact production and sustainable crop management. In this scenario, deep learning techniques have shown promise for the early identification of these diseases, enabling more efficient monitoring. This paper proposes an approach for detecting diseases and pests on coffee leaves using an efficient single-shot object-detection algorithm. The experiments were conducted using the YOLOv8, YOLOv9, YOLOv10 and YOLOv11 versions, including their variations. The BRACOL dataset, annotated by an expert, was used in the experiments to guarantee the quality of the annotations and the reliability of the trained models. The evaluation of the models included quantitative and qualitative analyses, considering the mAP, F1-Score, and recall metrics. In the analyses, YOLOv8s stands out as the most effective, with a mAP of 54.5%, an inference time of 11.4 ms and the best qualitative predictions, making it ideal for real-time applications.
Comparative Performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Layout Analysis of Historical Documents Images
Eder Silva dos Santos Júnior, Thuanne Paixão, Ana Beatriz Alvarez
Applied Sciences Switzerland, 2025
The digitalization of historical documents is of interest for many reasons, including historical preservation, accessibility, and searchability. One of the main challenges with the digitization of old newspapers involves complex layout analysis, where the content types of the document must be determined. In this context, this paper presents an evaluation of the most recent YOLO methods for the analysis of historical document layouts. Initially, a new dataset called BHN was created and made available, standing out as the first dataset of historical Brazilian newspapers for layout detection. The experiments were held using the YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures. For training, validation, and testing of the models, the following historical newspaper datasets were combined: BHN, GBN, and Printed BlaLet GT. Recall, precision, and mean average precision (mAP) were used to evaluate the performance of the models. The results indicate that the best performer was YOLOv8, with a Recalltest of 81% and an mAPtest of 89%. This paper provides insights on the advantages of these models in historical document layout detection and also promotes improvement of document image conversion into editable and accessible formats.
Performance Evaluation of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Stamp Detection in Scanned Documents
João Bento, Thuanne Paixão, Ana Beatriz Alvarez
Applied Sciences Switzerland, 2025
Stamps are an essential mechanism for authenticating documents in various sectors and institutions. Given the high volume of documents and the increase in forgery, it is necessary to adopt automated methods to identify stamps on documents. In this context, techniques based on deep learning stand out as an efficient solution for automating this process. To this end, this article presents a performance evaluation of YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s in detecting stamps on scanned documents. To train, validate, and test the models, an adapted dataset with 732 images from the combination of the StaVer and DDI-100 datasets is used. The performance of the models is evaluated by means of quantitative and qualitative analyses and by analyzing the computational cost. The results show that, in terms of performance, the YOLOv9s model obtained the best result, with a mAP (Mean Average Precision) of 98.7% for a precision and recall of 97.6%. In terms of computational cost and shorter inference time, the YOLOv11s model stands out. This comparative approach is a contribution to the state of the art for implementation in automatic stamp authentication devices.
An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction
Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque
Sensors, 2025
High-dynamic-range (HDR) image reconstruction involves creating an HDR image from multiple low-dynamic-range images as input, providing a computational solution to enhance image quality. This task presents several challenges, such as frame misalignment, overexposure, and motion, which are addressed using deep learning algorithms. In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. This paper proposes an HDR reconstruction architecture using a Transformer-based approach to achieve results competitive with the state of the art while reducing computational cost. The number of self-attention blocks was reduced for feature refinement. To prevent quality degradation, a Convolutional Block Attention Module was added, enhancing image features by using the central frame as a reference. The proposed architecture was evaluated on two datasets, achieving the best results on Tel’s dataset in terms of quality metrics. The computational cost indicated that the architecture was significantly more efficient than other Transformer-based approaches for reconstruction. The results of this research suggest that low-complexity Transformer-based architectures have great potential, with applications extending beyond HDR reconstruction to other domains.
Skew Logistic Distribution Applied as Activation Function in Artificial Neural Networks
Eder Silva Dos Santos, Altemir da Silva Braga, Ana Beatriz Alvarez, Thuanne Paixão
IEEE Access, 2025
In recent years, Artificial Neural Networks (ANNs) have stood out among machine learning algorithms in many applications, such as image and video pattern recognition. Activation functions play a crucial role in the operation of these algorithms, directly influencing the effectiveness of ANNs. The logistic (or sigmoid) function is often used as a standard activation function, but the existing literature lacks in-depth investigations into the potential of the Skew Logistic (SL) function. This work investigates the SL as an activation function in ANNs, exploring its ability to handle imbalanced data distributions. To achieve this, the function was implemented and evaluated on four binary classification datasets using accuracy, precision, recall, and F1-score. The results show that SL improved performance on the datasets selected for experiments 1 to 3, where an increase was observed in some performance metrics compared to the sigmoid function. And in experiment 4, competitive performance was obtained with softmax, using a multiclass version of SL. In particular, it was noted that it is possible to improve precision or recall, adjusting the asymmetry parameter (λ). It is concluded that the SL function offers a viable and promising alternative to conventional activation functions.
Denoising Diffusion Probabilistic Models for Cloud Removal and Land Surface Temperature Retrieval from a Single Sample
Quefren O. Leher, Emili S. Bezerra, Thuanne Paixão, Facundo Palomino-Quispe, Ana Beatriz Alvarez
IEEE Access, 2025
Land Surface Temperature (LST) monitoring is a key factor in demonstrating global climate change. Considering that cloud cover often obscures accurate remotely sensed LST data, it is important to develop approaches to reconstruct occluded areas. This paper presents an inpainting technique, designated RePaint, which utilizes Denoising Diffusion Probabilistic Models (DDPM) for the removal of clouds and the retrieval of LST data from Landsat-8 remote sensing imagery. The DDPM was trained with cloud-free LST image patches, and binary masks were formed by the segmentation process using the Ukis Cloud Shadow Mask to condition the region to be reconstructed. To simulate cloud occlusion in the test data, three cloud cover scenarios with 15%, 22% and 50% missing rate data were investigated. The performance of the approach was evaluated through a combination of quantitative and qualitative assessments. The quantitative analysis showed that the model exhibited consistent performance during the reconstruction process, achieving a mean squared error 0.0001, a peak signal to noise ratio 41.2888, and a structural similarity index 0.9826. In order to provide a more comprehensive visual representation of the results, boxplots and the Gaussian distribution of the temperature error in the LST reconstruction were included. Images of the absolute error of the reconstruction and scatterplots were used for qualitative analysis of randomly selected LST patches. The results demonstrate that the characteristics of the land surface have been preserved, exhibiting a minimum incidence of temperature reconstruction error and spatial coherence across all scenarios.
A Low Computational Cost Deep Learning Approach for Localization and Classification of Diseases and Pests in Coffee Leaves
Clécio Elias Silva E. Silva, Jonatan Borges Fragoso, Thuanne Paixão, Ana Beatriz Alvarez, Facundo Palomino-Quispe
IEEE Access, 2025
Coffee cultivation is of extreme economic importance in many regions of the world, but productivity is hampered by the various diseases and pests that affect the leaves of the plants, damaging both the quality and yield of the harvest. In this context, deep learning presents itself as a promising solution for the automatic identification of plant diseases, reducing dependence on human inspection and increasing efficiency in crop management. In this sense, this study proposes a novel two-stage approach, detecting the diseased region of the coffee leaf and classifying the diseases into Miner, Rust, Cercospora and Phoma on coffee leaves. A new dataset, derived from the BRACOL and Diseases and Pests in Coffee Leaves datasets, was created and used to improve class balance and robustness. In the first stage, the YOLOv8 model is being used to detect the diseased regions. For the second stage, the InceptionResNetv2, DenseNet169, Resnet50 and ShuffleNet models are being trained and used to classify the detected region, and a modification to a low computational cost classification architecture called PavicNet-MCv2 is being proposed. The results obtained are compared and the performance analysis of the detection models shows that YOLOv8 obtained the best performance with a mAP (Mean Average Precision) of 85.1% and for classification the DenseNet169 model obtained the highest average accuracy with 97.93%. The PavicNet-MCv2 model presents itself as the best alternative with reduced complexity and an accuracy of 97.77%. The combination of promising performance and reduced computational cost suggests that PavicNet-MCv2 can be integrated into plantation monitoring systems, contributing to more effective management of diseases and pests affecting coffee production.
Defect Detection in Printed Circuit Boards: A Comparative Analysis of Object Detection Models with Depthwise Convolution Adaptation
Julio Martins, Josue Lopez-Cabrejos, Quefren Leher, Thuanne Paixão, Ana Beatriz Alvarez, Facundo Palomino-Quispe
IEEE Latin America Transactions, 2025
PSHDR: HDR Image Reconstruction from a Single-Exposure LDR Using Residual Features
Lucas Hildelbrano Costa Carvalho, Quefren Leher, Josue Lopez-Cabrejos, Gustavo Ferreti, Ana Beatriz Alvarez, Thuanne Paixão, Olacir Rodrigues Castro Junior
Brazilian Symposium of Computer Graphic and Image Processing, 2025
PavicHDR: An Efficient Architecture for HDR Image Reconstruction Based on CNN and Attention Mechanisms
Josue Lopez-Cabrejos, Quefren Leher, Gustavo Ferreti, Lucas Hildelbrano Costa Carvalho, Thuanne Paixão, Ana Beatriz Alvarez, D. B. Luque
Brazilian Symposium of Computer Graphic and Image Processing, 2025
FLASH: Fast and Lightweight Architecture Using State Space Models for HDR Multi-Exposure Reconstruction
Josue Lopez-Cabrejos, Lucas Carvalho, Quefren Leher, Gustavo Ferreti, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque
IEEE Access, 2025
A Deformable Convolutional Neural Network with Dual Attention for Multi-Exposure HDR Reconstruction
Quefren Leher, Josue Lopez-Cabrejos, Gustavo Ferreti, Lucas Hildelbrano Costa Carvalho, Ana Gabriela Alvarez, Thuanne Paixão, Ana Beatriz Alvarez, Facundo Palomino-Quispe
Brazilian Symposium of Computer Graphic and Image Processing, 2025
A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio
Ruben Florez, Facundo Palomino-Quispe, Ana Beatriz Alvarez, Roger Jesus Coaquira-Castillo, Julio Cesar Herrera-Levano
Sensors, 2024
Exploratory Analysis Using Deep Learning for Water-Body Segmentation of Peru’s High-Mountain Remote Sensing Images
William Isaac Perez-Torres, Diego Armando Uman-Flores, Andres Benjamin Quispe-Quispe, Facundo Palomino-Quispe, Emili Bezerra, Quefren Leher, Thuanne Paixão, Ana Beatriz Alvarez
Sensors, 2024
Comparison Between Boundary Color Method and Haar Cascade Classifier Applied on Sheep Detection and Quantification on a Live Video Streaming for a Quadcopter UAV
Ivan Delgado Huayta, Gabino Rey Vidangos Ponce, Ana Beatriz Alvarez, Karlos A. C. Chirapo
Lecture Notes in Networks and Systems, 2024
DDPM-based inpainting for ghosting artifact removal in high dynamic range image reconstruction
Jonas Feitosa de Paiva, Salomão Machado Mafalda, Quefren Oliveira Leher, Ana Beatriz Alvarez
2024 9th International Conference on Image Vision and Computing Icivc 2024, 2024
Occluded Image Reconstruction under Single Sample Condition Using Diffusion Models Aiming Facial Recognition
Uendel Diego Da Silva Alvez, Quefren Oliveira Leher, Emili Silva Bezerra, Salomão Mafalda, Thuanne Paixão, Ana Beatriz Alvarez
2024 9th International Conference on Image Vision and Computing Icivc 2024, 2024
Fuzzy Controller Implemented for Movement of a Tendon-Driven 3D Robotic Lumbar Spine Mechanism †
Thuanne Paixão, Ana Beatriz Alvarez, Ruben Florez, Facundo Palomino-Quispe
Sensors, 2023
A Cloud Coverage Image Reconstruction Approach for Remote Sensing of Temperature and Vegetation in Amazon Rainforest
Emili Bezerra, Salomão Mafalda, Ana Beatriz Alvarez, Diego Armando Uman-Flores, William Isaac Perez-Torres, Facundo Palomino-Quispe
Applied Sciences Switzerland, 2023
A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
Ruben Florez, Facundo Palomino-Quispe, Roger Jesus Coaquira-Castillo, Julio Cesar Herrera-Levano, Thuanne Paixão, Ana Beatriz Alvarez
Applied Sciences Switzerland, 2023
Fuzzy Control for Simplified Lumbar Spine Robotic Mechanism Motion
Thuanne Paixão, Ana Beatriz Alvarez, Ruben Florez, Facundo Palomino-Quispe
Icnsc 2023 20th IEEE International Conference on Networking Sensing and Control, 2023
Optic Disc Localization from Retinal Fundus Image using Discrete Cosine and Hough Transforms
Márcio Antônio Pontes Da Silva, Me. Salomão Machado Mafalda, Ana Beatriz Alvarez, Roger Fredy Larico Chavez
Proceedings 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering Bibe 2023, 2023
Motion Control of a Robotic Lumbar Spine Model
Thuanne Paixão, Ana Beatriz Alvarez, Ruben Florez, Facundo Palomino-Quispe
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
Development of Simplified Lumbar Spine Mechanism Implemented with Tendon-Driven Motion
Ruben Florez, Facundo Palomino-Quispe, Thuanne Paixão, Ana Beatriz Alvarez, Lucas Angst, Luis Maggi
2023 IEEE Latin American Electron Devices Conference Laedc 2023, 2023
Development of Simplified Lumbar Spine Mechanism Implemented with Tendon-Driven Motion
Thuanne Paixão, Ana Beatriz Alvarez, Ruben Florez, Facundo Palomino-Quispe, Lucas Angst, Luis Maggi
2023 27th International Conference on Methods and Models in Automation and Robotics Mmar 2023 Proceedings, 2023
Manufacture of Sensitized Grätzel Solar Cells with NPs-TiO 2 and Natural Organic Dye Purple Corn (Zea mays L.) as Potential Use in the Photovoltaic Industry
Ruth Yanina Condori Queque, Maria Esther Quintana Cáceda, Ana Beatriz Alvarez, Gabino Rey Vidangos Ponce
Smart Innovation Systems and Technologies, 2022
A CNN Approach Implemented to Emotional Facial Expression Recognition for the Prevention of Autistic Meltdowns
Jacqueline Lopes Silva, Italo Oliveira, Zabelita Topolniak, Ana Beatriz Alvarez
2021 2nd Sustainable Cities Latin America Conference Scla 2021, 2021
EmotiTEA: A visual monitoring module based on the recognition of facial emotions with CNN
Italo Oliveira, Jacqueline Lopes Silva, Facundo Palomino Quispe, Ana Beatriz Alvarez
Proceedings of the 2021 IEEE Engineering International Research Conference Eircon 2021, 2021
Water stress analysis using aerial multispectral images of an avocado crop
Miguel Angel Castillo-Guevara, Facundo Palomino-Quispe, Ana Beatriz Alvarez, Roger Jesus Coaquira-Castillo
Proceedings of the 2020 IEEE Engineering International Research Conference Eircon 2020, 2020
Design a PLL controller using a Particle Swarm Optimization algorithm
Lucas Lima Rodrigues, Ana Beatriz Alvarez, Omar A. Chura Vilcanqui, Huilman Sanca Sanca
Proceedings of the 2020 IEEE 27th International Conference on Electronics Electrical Engineering and Computing Intercon 2020, 2020
Pattern recognition through digital image processing for unmanned aerial vehicles
Gabino Rey Vidangos Ponce, Kishankumar Bhimani, Jalu A. Prakosa, M. Ana Beatriz Alvarez
Proceedings of the 2019 IEEE 26th International Conference on Electronics Electrical Engineering and Computing Intercon 2019, 2019

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