Pattern Recognition Systems (ICPRS), International Conference on
2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)
4-7 July 2023
Showing 1-25 of 48
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The proceedings of this conference will be available for purchase through Curran Associates.
Pattern Recognition Systems (ICPRS), 2023 IEEE 13th International Conference on
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Candy classification using convolutional neural networks, data augmentation and transfer learning: Application and a new public dataset
Publication Year: 2023,Page(s):1 - 7
Quality control is an important task in confectionery because industrially produced candies are prone to suffer from a number of defects, such as cracks, crazing, unacceptable sizes and irregular shapes. Therefore, categorizing the candies into two classes (Non-Defective and Defective) is a key issue in the manufacturing process for the subsequent stages of packaging, pricing and selling as either...Show More
From Synthetic Data to Real Palm Vein Identification: a Fine-Tuning Approach
Ruber Hernández-García;Edwin H. Salazar-Jurado;Ricardo J. Barrientos;Francisco Manuel Castro;Julián Ramos-Cózar;Nicolás Guil
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
Palm vein recognition has relevant advantages in comparison with most traditional biometrics, such as high security and recognition performance. In recent years, CNN-based models for vascular biometrics have improved the state-of-the-art, but they have the disadvantage of requiring a larger number of samples for training. In this context, the generation of synthetic databases is very effective for...Show More
Abnormal Scene Classification using Image Captioning Technique: A Landslide Case Study
Narongthat Thanyawet;Photchara Ratsamee;Yuki Uranishi;Haruo Takemura
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
Disaster occurs in various regions, which affects human life and activities. For instance, the one disaster we faced in the mountainous area was a landslide that could occur for various reasons, such as heavy precipitation, failure of slope stability, or earthquake. After this event, humans must evaluate the damage and the damaged area to recover it. Therefore, we must focus on autonomous damage d...Show More
Wh-AI-les: Exploring harmonized vision models robustness against distribution shift
Mehdi Mounsif;Mohamed Benabdelkrim;Marie-Anne Bauda;Yassine Motie
Publication Year: 2023,Page(s):1 - 5
The remarkable and increasing efficiency of learning-based vision strategies has induced strong paradigm shift in favor of neural architectures that are consequently finding their way into real-world applications with significant impact. Nevertheless, neural networks display a particular brittleness that can significantly hurt performance when deployed outside lab conditions, which is symptomatic ...Show More
Extending the U-Net Architecture for Strokes Segmentation on CT Scan Images
Iván Guerrón;Noel Peréz;Diego Benítez;Felipe Grijalva;Daniel Riofrío;Maria Baldeon-Calisto
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
Brain stroke is the second leading cause of death worldwide after heart disease, and one of the most concerning types is intracranial hemorrhage (ICH). This type of bleeding, caused by ruptures of blood vessels within the brain, affects the brain, prevents cell oxygenation, and causes nerve damage. Although recent medical advances have helped many patients, doctors are still subject to human error...Show More
Enhancing Schizophrenia Prediction Using Class Balancing and SHAP Explainability Techniques on EEG Data
Publication Year: 2023,Page(s):1 - 5
Cited by: Papers (1)
Machine learning (ML) makes predictions or supports decision making based on data, achieving high accuracy, saving time and resources, and even running real-time analysis. However, one drawback of these models is the lack of transparency in complex models, reducing confidence in sensitive fields such as health. This paper analyzes electroencephalogram (EEG) data to predict schizophrenia in patient...Show More
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches
Gisel Bastidas-Guacho;Patricio Moreno-Vallejo;Boris Vintimilla;Angel D. Sappa
Publication Year: 2023,Page(s):1 - 8
Cited by: Papers (1)
Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fus...Show More
Spatiotemporal Variations of $\mathbf{PM}_{2.5}$ Concentration and Its Heterogeneous Relationship with Natural and Humanity Factors in Handan of China
Die Lu;Tanhua Jin;Xiang Zhang;Han Wang;Shifan Han;Yuhui Huang;Jun Cao;Lizhong Gao;Junyan Yang;Qiao Wang
Publication Year: 2023,Page(s):1 - 8
The problem of $\mathbf{PM}_{2.5}$ air pollution is increasingly severe in China due to rapid urbanization, especially in developing villages. However, the vast majority of existing research on $\mathbf{PM}_{2.5}$ is limited to the provincial or municipal scales and fails to consider regional heterogeneity. This study employs a spatial panel regression model to investigate the spatial spillover ef...Show More
Automatic Subtractive Clustering Algorithm Model (ASCAM) for Blind Identification of QAM Constellations
Publication Year: 2023,Page(s):1 - 7
Blind System Identification (BSI) is a major pattern recognition task in many fields, including digital communication systems. Recently, many machine learning techniques have been applied to identify modulation types of signals solely by the receiver without prior knowledge of the transmission protocols. In this paper, we propose Automatic Subtractive Clustering Algorithm Model (ASCAM), a novel hy...Show More
Several Kinds of Residual Neural Networks Driven by Partial Differential Equations and Their Applications in Image Classification and Denoising
Publication Year: 2023,Page(s):1 - 5
In the past few years, many scholars gradually began to interpret the conventional residual neural Networks (ResNet) as the partial differential equations. Some scholars called it Neural PDE. Ruthotto, Haber, Lin and others have done relevant researches on this interpretation. This paper mainly studies the residual neural network model driven by the heat equation (HERN) and the convection-diffusio...Show More
DeepSIT: Deeply Supervised Framework for Image Translation on Breast Cancer Analysis
Maria Baldeon-Calisto;Francisco Rivera-Velastegui;Daniel Riofrío;Ricardo Flores-Moyano;Noel Peréz;Diego Benítez
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
Image translation networks are deep learning models that can convert an image from one domain to another while preserving the semantic content. These networks are helpful in the medical field for noise reduction, reconstruction, and modality conversion. In this work, we propose DeepSIT, a deeply supervised framework for image translation. DeepSIT is a conditional generative adversarial network com...Show More
Mathematical Palm Vein Modeling for Large-Scale Biometric Recognition
Edwin H. Salazar-Jurado;Ruber Hernández-García;Karina Vilches Ponce;Ricardo J. Barrientos
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
Individual recognition through palm vein authentication has gained the attention of the scientific community due to its high level of security. However, the algorithms for recognition are validated with a limited number of images due to the small number of subjects in public databases, making it challenging to implement deep learning-based methods and evaluate scalability for mass identification. ...Show More
Connected-Components-based Post-processing for Retinal Vessels Deep-Learning Segmentation
Idris Dulau;Benoit Recur;Catherine Helmer;Cecile Delcourt;Marie Beurton-Aimar
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (2)
The analysis of fundus images may reflect systemic and cerebral vascular status through a non-invasive, rapid, and cost-effective method. Accurate characterization of the retinal vessels is critical for this status assessment. Medical professionals can perform diagnosis on measurements extracted from the retinal vessels, which are identified through segmentation. Supervised-Learning is used to per...Show More
A Deep Learning-Based Algorithm for ECG Arrhythmia Classification
Daniela Espin-Ramos;Vicente Alvarado;Edwin Valarezo Añazco;Erick Flores;Bolívar Nuñez;José Santos;Sara Guerrero;Jonathan Aviles-Cedeno
Publication Year: 2023,Page(s):1 - 6
Cited by: Papers (1)
This paper aims to automatically classify five classes of arrhythmia present in Electrocardiograms (ECG) by using two Deep Learning (DL)-based models. One based on Convolutional Neural Network (CNN) and the other based on Residual Networks (ResNet). The main motivation of this research is to enhance the field of medicine and assist doctors in the diagnosis of arrhythmia. The DL-based models were t...Show More
A centered kernel alignment-based strategy for pest evolution tracing: Myiopsitta monachus case
Andrea Viazzi;Avril Maciel Rios;Juan Sebastián Blandón;Julián Gil González
Publication Year: 2023,Page(s):1 - 5
Cited by: Papers (1)
This paper presents a novel strategy for tracing the evolution of Myiopsitta monachus populations using a centered kernel alignment (CKA)-based approach. This species is of particular interest due to having been declared a pest. The proposed method utilizes a vector representation of bird sightings in Uruguayan territory, divided into 492 cells of 24 kilometers $\times$ 24 kilometers resolution. K...Show More
Addressing Bias in Fine-Grained Classification Datasets: A Strategy for Reliable Evaluation
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
The high specificity of classes in fine-grained clas-sification tasks leads to a small number of images per class in the common research datasets. Thus, the intra-class variance, such as differences in vehicle colors for fine-grained vehicle classification, may not be properly represented. Consequently, there can be a heavy bias in regards to certain attributes, such as vehicle colors, leading to ...Show More
Study of machine learning techniques for pedestrian dynamics simulation models
Pedro Reyes Norambuena;Juan Bekios-Calfa;Javier Martínez Torres
Publication Year: 2023,Page(s):1 - 8
Pedestrian modeling has made significant progress, studying the pedestrian as an individual and also their behavior in the face of obstacles in their environment, from confined access spaces to the interaction of the movement of a large number of other people. In this paper we wish to explore hybrid models that allow simulating pedestrian dynamics supported by machine learning techniques to make. ...Show More
A Novel Kernel Extension for the Nearest Feature Line Classifier
Manuele Bicego;Eduardo José Villegas-Jaramillo;Mauricio Orozco-Alzate
Publication Year: 2023,Page(s):1 - 6
The Nearest Feature Line (NFL) rule represents a widely applied variant of the Nearest Neighbor rule whose usefulness has been shown in many different contexts. However, even though the NFL rule has been extended in different directions, only one is based on kernels. In this paper we make one step forward, proposing a novel kernel extension of the NFL rule called Kernel Rectified NFL classifier. O...Show More
gAIt: Deep Learning Based Evaluation of Injurious Running Biomechanics Using Pose Estimation
Publication Year: 2023,Page(s):1 - 6
Biomechanical errors in running form have increasingly been linked to the development of musculoskeletal injuries, precluding runners from the physical and mental benefits of long-distance running. Existing works on biomechanical anal-ysis of running form utilize expensive equipment and focus on understanding injury causes instead of predicting injury risk. In this paper, we present a novel datase...Show More
Melanoma Cancer Classification using Deep Convolutional Neural Networks
José M. Cadena;Noel Peréz;Diego Benítez;Felipe Grijalva;Ricardo Flores;Oscar Camacho;Yovani Marrero-Ponce
Publication Year: 2023,Page(s):1 - 7
Cancerous melanoma is a relatively rare skin lesion that, if detected, can cause death due to its high mortality rate. The excessive production of melanocytes causes cancerous melanoma in the skin due to high exposure to solar radiation and poor skin care against these conditions. For this reason, we decided to use deep learning models to help detect melanoma without removing skin samples for biop...Show More
Surface sampling for optimal viewpoint generation
Vanessa Staderini;Tobias Glück;Philipp Schneider;Roberto Mecca;Andreas Kugi
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (5)
The automation of visual quality inspection is becoming increasingly important in manufacturing industries. Visual quality inspection aims to ensure that intermediate and/or final products meet certain quality features. However, automating quality inspection is challenging because the inspection process depends on the object geometry and the types of defects often encountered during manufacturing ...Show More
Quantifying the Uncertainty in 3D CT Lung Cancer Images Classification
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
Lung cancer has caused the deaths of millions of people. Early diagnosis is proven vital to increase survival rate. This can be done using computed tomography screening, and when such a system is in place with a computer-aided diagnostic system and deep learning algorithms, it has been proven to be an effective approach. However, most existing systems do not quantify the uncertainty in the predict...Show More
Construction of Spatial Pedigree of Chinese Typical Village Settlements
Publication Year: 2023,Page(s):1 - 7
This study endeavors to construct a spatial pedigree on a village scale, which includes a considerable number of administrative regions at the village level throughout China in 2020. In order to comprehensively assess various factors such as natural and cultural elements, analytical techniques such as factor analysis and natural breaks are utilized. By conducting a specific regional analysis, this...Show More
Detecting Multi Thoracic Diseases in Chest X-Ray Images Using Deep Learning Techniques
Publication Year: 2023,Page(s):1 - 7
Cited by: Papers (1)
This doctoral proposal introduces a novel method for detecting and diagnosing various thoracic diseases in chest images using advanced deep-learning approaches. The research aims to establish a powerful and effective technique for promptly recognizing multiple pathologies in chest radiographs, which holds significant implications for patient outcomes and healthcare resources. Additionally, the stu...Show More
A New Methodology for Analysing Interactions between an Autonomous Vehicle and other Users
Josué Manuel Rivera Velázquez;Pascal Houssam Salmane;Guillaume Saint-Pierre;Louahdi Khoudour;Olivier Huynh;Jean-Paul Garrigos
Publication Year: 2023,Page(s):1 - 6
The increasingly present automation of vehicles generates great concern both in the industrial and public sectors. Evidently, the autonomy of vehicles is accompanied by a large number of scientific questions: what are the real benefits of automating transport services? what are the new expected situations due to the presence of an autonomous vehicle? etc. In terms of road safety, great interest ha...Show More
Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Pattern Recognition Systems (ICPRS), 2023 IEEE 13th International Conference on
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