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2020 International Conference on Systems, Signals and Image Processing (IWSSIP)最新文献

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Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images 不同倍率组织病理图像对乳腺癌识别的特征分析
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145129
R. Carvalho, A. S. Martins, L. A. Neves, M. Z. Nascimento
{"title":"Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images","authors":"R. Carvalho, A. S. Martins, L. A. Neves, M. Z. Nascimento","doi":"10.1109/IWSSIP48289.2020.9145129","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145129","url":null,"abstract":"Breast cancer is one of the most common diseases in women in the world. There are various imaging techniques employed in the diagnosis. The histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. The attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms with the 10-fold cross-validation technique. In this study, we investigated the proposed approach with images obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. The proposed approach achieved significant results of AUC and accuracy for all cases analyzed. The proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. These results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129820222","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: 6
Message from the Publication Chairs 出版主席致辞
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/iwssip48289.2020.9145124
{"title":"Message from the Publication Chairs","authors":"","doi":"10.1109/iwssip48289.2020.9145124","DOIUrl":"https://doi.org/10.1109/iwssip48289.2020.9145124","url":null,"abstract":"The Main Track of the 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) featured a particularly rich technical program including 62 papers. Those papers were selected from about 140 submissions written by authors from 24 countries from 5 continents. Each submission was singleblind reviewed by at least two independent reviewers and evaluated with particular attention to its originality, significance, and clarity to ensure high standards of quality. The program resulted from dedicated multiple months of work of 35 International Program Committee members, 33 Local Program Committee members, and 156 reviewers. Keeping its traditional soul, the scientific program of the main conference is divided into 14 sessions focusing on Biomedical Signal Processing and Analysis; Face Recognition and Biometry; Human-Computer Interaction and Education; Image Processing; Image-Based Detection, Recognition and Classification; Machine Learning and Prediction; Networks and Wireless Communications; Signal Processing; and Speech and Audio Processing. A selection of outstanding Main Track papers will be invited to submit extended versions of their works for review and potential publication on a special issue of the International Journal of Innovative Computing and Applications (IJICA). The Program Chairs wish to thank Prof. Nadia Nedjah, Editor-in-Chief of IJICA, for this opportunity. We acknowledge the authors for their contributions. We are also grateful to all reviewers and members of the International and Local Program Committees for all their hard work in the reviewing process.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131072557","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
The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods 基于一维GLCM的颜色纹理特征提取与深度学习方法的比较
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145263
M. Benco, P. Kamencay, M. Radilova, R. Hudec, M. Šinko
{"title":"The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods","authors":"M. Benco, P. Kamencay, M. Radilova, R. Hudec, M. Šinko","doi":"10.1109/IWSSIP48289.2020.9145263","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145263","url":null,"abstract":"In this paper, the comparison between deep learning methods and feature extraction algorithms is presented. The principle of Grey-Level Co-occurrence Matrix (GLCM) and its modifications are used for our research. The main idea was to design a method for the description of combined features and textures. The texture classification process is carried out with the robust support vector machine classifier (SVM). We compare these feature extraction methods with proposed Convolutional Neural Networks (CNN). This proposed network contains 25 layers. Finally, the all evaluation and comparison of color texture retrieval results for all used methods are presented. The all feature extraction algorithms and proposed CNN have been tested on two different color texture datasets (Outex and Vistex datasets).","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132093477","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: 9
Evaluation of Output Representations in Neural Network-based Trajectory Predictions Systems 基于神经网络的轨迹预测系统中输出表征的评估
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145309
Andrea Ek-Hobak, Á. Sánchez, J. Hayet
{"title":"Evaluation of Output Representations in Neural Network-based Trajectory Predictions Systems","authors":"Andrea Ek-Hobak, Á. Sánchez, J. Hayet","doi":"10.1109/IWSSIP48289.2020.9145309","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145309","url":null,"abstract":"This work deals with the challenging problem of pedestrian trajectory prediction, when observations from these pedestrians can be gathered through a urban video monitoring system. Since most of state-of-the-art systems in this field are now based on deep recurrent neural networks, here we study one specific characteristic of these systems, namely the way they encode their output. We compare three different representations of the output, and show that those representations working on residuals (in particular, displacements with respect of last pedestrian position or linear regression models of residual errors) produce much more accurate predictions than those ones handling absolute coordinates.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965605","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
Parameterizing Variational Methods Through Data-Driven Inverse Problems for Image Processing Applications 通过数据驱动逆问题参数化变分方法在图像处理中的应用
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145179
Italo M. F. Santos, G. Giraldi, P. Blanco, A. Loula
{"title":"Parameterizing Variational Methods Through Data-Driven Inverse Problems for Image Processing Applications","authors":"Italo M. F. Santos, G. Giraldi, P. Blanco, A. Loula","doi":"10.1109/IWSSIP48289.2020.9145179","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145179","url":null,"abstract":"The development of techniques to automatic set up parameters in image processing methods based on variational approaches is cumbersome because the model sensitivity to parameters values is not known in general. In this paper, we address this issue through a data-driven inverse problem. Specifically, our methodology receives pairs (input image(s), desired result(s)) and seeks for the near optimum parameter vector through an inverse problem based on minimization scheme. The methodology is not restricted to a particular functional and it does not require large annotated data sets as input. Besides, methods based on partial differential equations (PDEs) can be also accommodated in our approach. We validate the methodology for calibrating parameters using as test-bed a variation of Mumford-Shah method. The obtained solutions using the parameters found in this paper are compared with literature results in order to show the efficiency of our technique.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127969606","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
Special: Session Signal and Image Processing for Smart Cities 专题:智慧城市的会话信号和图像处理
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/iwssip48289.2020.9145288
{"title":"Special: Session Signal and Image Processing for Smart Cities","authors":"","doi":"10.1109/iwssip48289.2020.9145288","DOIUrl":"https://doi.org/10.1109/iwssip48289.2020.9145288","url":null,"abstract":"","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748440","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
Main Track: Human Computer Interaction and Education 主攻方向:人机交互与教育
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/iwssip48289.2020.9145252
{"title":"Main Track: Human Computer Interaction and Education","authors":"","doi":"10.1109/iwssip48289.2020.9145252","DOIUrl":"https://doi.org/10.1109/iwssip48289.2020.9145252","url":null,"abstract":"","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123163224","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
IWSSIP 2020 Organization IWSSIP 2020组织
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/iwssip48289.2020.9145078
{"title":"IWSSIP 2020 Organization","authors":"","doi":"10.1109/iwssip48289.2020.9145078","DOIUrl":"https://doi.org/10.1109/iwssip48289.2020.9145078","url":null,"abstract":"","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122774690","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
Multilevel CNN for Angle Closure Glaucoma Detection using AS-OCT Images 基于AS-OCT图像的闭角型青光眼多层CNN检测
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145110
M. M. Ferreira, Giovanna Pavani Esteve, G. B. Junior, J. Almeida, A. Paiva, R. Veras
{"title":"Multilevel CNN for Angle Closure Glaucoma Detection using AS-OCT Images","authors":"M. M. Ferreira, Giovanna Pavani Esteve, G. B. Junior, J. Almeida, A. Paiva, R. Veras","doi":"10.1109/IWSSIP48289.2020.9145110","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145110","url":null,"abstract":"Glaucoma is identified as one of the main global causes of visual impairment or blindness. There is no cure or possible corrections in case of visual impairment, so the early diagnosis is essential to delay or prevent its progression. However, the disease is asymptomatic in its early stages. The disease can be detected in routine eye exams like anterior segment optical coherence tomography, which is analyzed by a specialist, but the analysis of many patients demands much time. There are two main types of the disease, open angle and angle closure glaucoma. In this paper, we propose a method for automatic detection of angle closure glaucoma in anterior segment optical coherence tomography images, based on transfer learning and multilevel convolutional neural networks to extract visual features. In the proposed method, the multilevel architecture models achieve as the best result an AUC of 0.972.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131472015","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: 3
Hybrid Capsule Network Architecture Estimation for Melanoma Detection 黑色素瘤检测的混合胶囊网络体系结构估计
2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145127
José M. C. Boaro, P. T. C. Santos, Carlos V. M. Rocha, Thamila Fontenele, G. B. Junior, J. Almeida, A. Paiva, S. Rocha
{"title":"Hybrid Capsule Network Architecture Estimation for Melanoma Detection","authors":"José M. C. Boaro, P. T. C. Santos, Carlos V. M. Rocha, Thamila Fontenele, G. B. Junior, J. Almeida, A. Paiva, S. Rocha","doi":"10.1109/IWSSIP48289.2020.9145127","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145127","url":null,"abstract":"As uncommon as its incidence is, melanoma is considered to be one of the most aggressive and deadly skin cancers today. Risk factors for the development of the disease are related to exposure to ultraviolet rays and family history. Currently, the diagnosis is made through dermatoscopy, a noninvasive technique that aims to observe the structures of the skin. The diagnosis of melanoma is linked to the professional's experience in identifying the disease. With the advancement of computing, solutions based on deep learning were suggested to help diagnose the disease. This work has as main objective the adaptation of a capsule neural network for the task of detecting melanoma in medical images. The proposed architecture consists of the VGG16 neural network combined with a capsule neural network. The results obtained by the proposed model fed with the ISIC Archive database proved to be promising when compared to the literature, obtaining an accuracy of 92.6, sensitivity of 90.9 and specificity of 92.8.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132561158","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: 3
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