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Anais do XVII Workshop de Visão Computacional (WVC 2021)最新文献

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UniMRI: Unified Repository of Magnetic Resonance Images for Multiple Sclerosis Diagnosis 用于多发性硬化症诊断的磁共振图像统一库
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18912
Wellington Silveira, Rafael Korb, G. DiMuro, Rodrigo Andrade de Bem
{"title":"UniMRI: Unified Repository of Magnetic Resonance Images for Multiple Sclerosis Diagnosis","authors":"Wellington Silveira, Rafael Korb, G. DiMuro, Rodrigo Andrade de Bem","doi":"10.5753/wvc.2021.18912","DOIUrl":"https://doi.org/10.5753/wvc.2021.18912","url":null,"abstract":"Multiple sclerosis is an autoimmune disease that affects the central nervous system, destroying myelin. To detect multiple sclerosis, you need to have MRI scans so you can see the areas where myelin has been damaged. This analysis is complex and costly due to the time required to assess injuries. The use of machine learning is desirable as these exams are taken periodically. However, the number of public databases present in the literature containing patients with multiple sclerosis is small when compared to the amount of data needed to train deep neural networks. Thus, the objective of this work is to join public databases of magnetic resonance images existing in the literature, proposing a software library to manipulate and pre-process these data.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126575331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computational Methodology for Iris Segmentation and Detection in Images from the Eyes Region Using Convolutional Neural Networks 基于卷积神经网络的眼部图像虹膜分割与检测的计算方法
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18910
Fredson Costa Rodrigues, A. C. D. Paiva, João Almeida, Geraldo Braz Júnior, Aristófanes Corrêa, A. C. B. Soares
{"title":"Computational Methodology for Iris Segmentation and Detection in Images from the Eyes Region Using Convolutional Neural Networks","authors":"Fredson Costa Rodrigues, A. C. D. Paiva, João Almeida, Geraldo Braz Júnior, Aristófanes Corrêa, A. C. B. Soares","doi":"10.5753/wvc.2021.18910","DOIUrl":"https://doi.org/10.5753/wvc.2021.18910","url":null,"abstract":"Eye tracking is an application of computer vision responsible for detecting the iris and pupil in the eye region. The usefulness of this tracking contributes to research that assesses cognitive aspects through pupillary reactions identified in these detected regions. Another application in this task is iris recognition in digital biometrics. This study aims to carry out the verification and detection of the iris in images of the eye region occluded by eyelashes, eyelids and specular reflexes, through a deep neural network called At-Unet in this article. In order to assist in eye tracking this method achieves 95.32 % of data coefficient when segmenting the iris of the eyes, indicating the efficiency of this methodology.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134507786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Systematic Mapping on Detection of Human Mouth Landmarks 人类口腔标志检测的系统制图
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18894
Pedro Henrique D’Almeida G. Rissato, R. Bulcão-Neto, Alessandra Alaniz Macedo
{"title":"A Systematic Mapping on Detection of Human Mouth Landmarks","authors":"Pedro Henrique D’Almeida G. Rissato, R. Bulcão-Neto, Alessandra Alaniz Macedo","doi":"10.5753/wvc.2021.18894","DOIUrl":"https://doi.org/10.5753/wvc.2021.18894","url":null,"abstract":"Facial landmarks represent regions of interest whose detection and localization generate features supporting the identification of movements, feelings, and reactions. Most facial feature detection algorithms focus on entire semantic areas, such as the region of a mouth which allows grained manipulation that is essential for a wide domain variety. This paper describes a systematic mapping of the detection of landmarks in human faces and their application domains. The identification and selection methods of primary studies include automatic search on information sources, inclusion, and exclusion criteria over 344 scientific papers from 2015 and 2021, from which we analyzed and synthesized 115 primary studies. Our analysis considered the implementation of methods, types, and uses of data extracted from the mouth. The mapping brought exciting information as new methods, datasets, and domains researched through the time interval reviewed as well research gaps that can be explored.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automatic Segmentation and ROI detection in cardiac MRI of Cardiomyopathy using q-Sigmoid as preprocessing step 以q- s型为预处理步骤的心肌病MRI自动分割与ROI检测
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18904
Eduardo Coltri, G. Costa, Kelvin Lins Silva, Pedro Zigante Martim, L. Bergamasco
{"title":"Automatic Segmentation and ROI detection in cardiac MRI of Cardiomyopathy using q-Sigmoid as preprocessing step","authors":"Eduardo Coltri, G. Costa, Kelvin Lins Silva, Pedro Zigante Martim, L. Bergamasco","doi":"10.5753/wvc.2021.18904","DOIUrl":"https://doi.org/10.5753/wvc.2021.18904","url":null,"abstract":"The growth of data volume is a reality in all such as segments of our society. Despite of personalized experiences, accurate and fast information, new challenges had arisen. For healthcare industry, for example, it was noted an increase of radiologists workload which may cause visual fatigue and, consequently, errors during diagnosis. intelligence was pointed as an option to support Artificial physicians analysis and reduce the visual fatigue. Thus, this paper focus on the proposal of a novel strategy to enhance cardiac magnetic resonance images (MRI) and automatically detect their region-of-interest (ROI) using a convolutional neural network (CNN). Our object of study is the disease of Cardiomyopathy and the desirable ROI is the left ventricle from axial slices. We evaluated q-Sigmoid performance using it as a preprocessing step and validate the results through modified CNNs: U-Net and ResNet.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116836645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Circular Hough Transform to improve viable Saccharomyces cerevisiae identification 循环霍夫变换提高酿酒酵母菌鉴定
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18895
Felipe Silveira Brito Borges, Diogo Soares da Silva, Lia Nara Balta Quinta, A. I. Mongelo, M. Cereda, H. Pistori
{"title":"Circular Hough Transform to improve viable Saccharomyces cerevisiae identification","authors":"Felipe Silveira Brito Borges, Diogo Soares da Silva, Lia Nara Balta Quinta, A. I. Mongelo, M. Cereda, H. Pistori","doi":"10.5753/wvc.2021.18895","DOIUrl":"https://doi.org/10.5753/wvc.2021.18895","url":null,"abstract":"Yeast counting is an important step in monitoring the fermentation process in sugarcane mills to optimize ethanol production. There is a need for a faster method to count viable cells in place of the fastidious and operator-dependent traditional method. In this paper, the application of a slightly modified version of the standard Circular Hough Transforms to automate the inoculated fermentation process of Saccharomyces cerevisae is reported. The results of several experiments with different preprocessing algorithms and parameter adjustments are presented. The resulting system will be part of a microbiological control procedure that is being developed to respond to Brazilian ethanol sugarcane mill's demands.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Discriminant analysis of background noise in extremity magnetic resonance images 四肢磁共振图像背景噪声的判别分析
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18891
Carlos José Andrioli, C. Thomaz
{"title":"Discriminant analysis of background noise in extremity magnetic resonance images","authors":"Carlos José Andrioli, C. Thomaz","doi":"10.5753/wvc.2021.18891","DOIUrl":"https://doi.org/10.5753/wvc.2021.18891","url":null,"abstract":"Since the creation of the first magnetic resonance imaging (MRI) equipment in 1974, experts have been studying the continuous improvement of image quality. This work aims to study the types of background noise in images from extremity MRI system of high-field, mainly caused by Faraday Cage problems. Phantom images of 1T equipment were investigated for this study. For the acquisition of these images, a protocol called DQA (Daily Quality Assurance) was used. For this work, 45 MRI images were acquired, which were pre-classified by an expert, and analyzed by SNR, an index that quantifies the ratio between signal and image noise, and by the multivariate statistical methods PCA + MLDA. PCA served as a statistical filter, which considerably decreased the amount of input information for MLDA. When all main components were used, MLDA showed an accuracy of 93.33% and results that allowed to discriminate background noise from these images in complementarity with SNR.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126945780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Proposed Analysis and Digital Image Processing For Indication of Tailings Dam Failure 尾矿坝溃决的一种分析与数字图像处理方法
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18889
Fernando Paim Lima, Bruna Mendes, José Luis Pimenta, Paloma Oliveira Maira Lima
{"title":"A Proposed Analysis and Digital Image Processing For Indication of Tailings Dam Failure","authors":"Fernando Paim Lima, Bruna Mendes, José Luis Pimenta, Paloma Oliveira Maira Lima","doi":"10.5753/wvc.2021.18889","DOIUrl":"https://doi.org/10.5753/wvc.2021.18889","url":null,"abstract":"Due to the recent accidents with ore tailings dams in Brazil, many researchers have been interested in alternatives to minimize the impact of such disasters. This paper proposes a method for analysis of surveillance images related to Córrego do Feijão mine dam, located in the city of Brumadinho, Minas Gerais, Brazil. The method uses digital image processing and computer vision algorithms to obtain an indication of the moment of its rupture. The results found show that the technique is relevant to the identification of dam rupture. The methodology in this paper can be used to develop autonomous monitoring solutions for the purpose of failure warning, which might provide time for people to take refuge in higher places before being reached by the slurry wave.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130411738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computer Vision and Neural Networks for Libras Recognition 图书馆识别的计算机视觉和神经网络
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18903
Silas Luiz Furtado, Jauvane de Oliveira
{"title":"Computer Vision and Neural Networks for Libras Recognition","authors":"Silas Luiz Furtado, Jauvane de Oliveira","doi":"10.5753/wvc.2021.18903","DOIUrl":"https://doi.org/10.5753/wvc.2021.18903","url":null,"abstract":"In recent years, one can find several efforts to increase the inclusion of people with some type of disability. As a result, the global study of sign language has become an important research area. Therefore, this project aims at developing an information system for the automatic recognition of the Brazilian Sign Language (LIBRAS). The recognition shall be done through the processing of videos, without relying on support hardware. Given the great difficulty of creating a system for this purpose, an approach was developed by dividing the process into stages. In addition to dynamically identifying signs and context, neural network concepts and tools were used to extract the characteristics of interest and classify them accordingly. In addition, a dataset of signs, referring to the alphabet in LIBRAS, was built as well as a tool to interpret, with the aid of a webcam, the signal executed by a user, transcribing it on the screen.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Few Data Diversification in Training Generative Adversarial Networks 生成对抗网络训练中的少量数据多样化
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18892
Lucas Fontes Buzutti, C. Thomaz
{"title":"Few Data Diversification in Training Generative Adversarial Networks","authors":"Lucas Fontes Buzutti, C. Thomaz","doi":"10.5753/wvc.2021.18892","DOIUrl":"https://doi.org/10.5753/wvc.2021.18892","url":null,"abstract":"The first GANs have initially produced sharp images in relatively small resolution and with limited variations, and unstable training. Later works proposed new GAN models capable of generating sharp images in high resolution and with a high level of variation. However, these models use unlimited and highly diversified image sets. We discuss here the use of these models with real-world image sets, since they are composed of limited sample size sets.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122940991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors 一种基于形态学和非形态学描述符的多类淋巴瘤分类方法
Anais do XVII Workshop de Visão Computacional (WVC 2021) Pub Date : 2021-11-22 DOI: 10.5753/wvc.2021.18911
Tiago P. de Faria, M. Z. Nascimento, L. G. A. Martins
{"title":"A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors","authors":"Tiago P. de Faria, M. Z. Nascimento, L. G. A. Martins","doi":"10.5753/wvc.2021.18911","DOIUrl":"https://doi.org/10.5753/wvc.2021.18911","url":null,"abstract":"Lymphoma is one of the most common types of cancer and its treatment can be more effective if the disease variant is correctly diagnosed. Many works have been done using computer vision and machine learning to classify the images. This work presents lymphoma based on histological a method using simple descriptors and a decision tree-based ensemble classifier, aiming to maintaing the interpretability of the data and understand what information in most important to the classification task. We use morphological and non morphological descriptors extracted from the cells nuclei, a feature selection method based on principal component analysis (PCA), and a gradient boosting decision tree (GBDT) method for multiclass classification. Our approach achieves an average accuracy of 0.932. this result is close to those obtained in the state of the art, while it uses simpler descriptors and better interpretable classification models.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121363486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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