2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)最新文献_第5页

Exploring the Specificities and Challenges of Testing Big Data Systems 探索测试大数据系统的特殊性和挑战

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00055

D. Staegemann, M. Volk, Abdulrahman Nahhas, Mohammad Abdallah, K. Turowski

引用次数: 12

Manifold Extraction in Fluorescent Stack via Deep Learning 基于深度学习的荧光堆流形提取

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00032

Jianfeng Cao, Hong Yan

引用次数: 0

Human Tracking for Children Behavior Analysis in Nursery Schools 幼儿园儿童行为分析的人类跟踪

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00044

Yuan Lin, Y. Obuchi, Xueting Wang, T. Yamasaki, S. Toriumi, M. Hayashi, S. Nozawa, Midori Takahashi, T. Endo, K. Akita

引用次数: 0

An Online Tool for Displaying and Processing Spectral Reflectance Images 显示和处理光谱反射图像的在线工具

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00118

P. Colantoni, Jean-Baptiste Thomas, M. Hébert, A. Trémeau

引用次数: 2

Cycle-Consistent InfoGAN for Speech Enhancement in Various Background Noises 不同背景噪声下语音增强的周期一致信息gan

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00043

Wonsup Shin, Sung-Bae Cho

{"title":"Cycle-Consistent InfoGAN for Speech Enhancement in Various Background Noises","authors":"Wonsup Shin, Sung-Bae Cho","doi":"10.1109/SITIS.2019.00043","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00043","url":null,"abstract":"Speech enhancement is one of the crucial research topics applied to various fields. In addition, due to the progress of wireless communication technology, the need for speech enhancement research to remove various background noise occurring in the real world is increasing. Recently, a speech enhancement model based on generative adversarial learning, which can build a significant loss function by itself, has been proposed and outperformed the conventional methods. However, these models assume parallel datasets for learning, and there is a problem that the performance decreases for the signal containing various kinds of noise. This paper proposes a novel speech enhancement model based on generative adversarial network (GAN). The proposed method additionally uses cycle-consistency loss for learning on non-parallel datasets, where the InfoGAN mechanism is used to cluster noise information in an unsupervised learning manner. The proposed model can form cluster-specific mapping by using the obtained clustering information. We quantitatively verify the speech enhancement performance of the proposed method through several metrics such as MOS, SI-SNR, and PESQ, and achieve about 55% better MOS performance than the previous GAN-based models.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127038425","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

Hybrid Data Assimilation: An Ensemble-Variational Approach 混合数据同化:一种集成-变分方法

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00104

Edward M. Lim, Miguel Molina-Solana, C. Pain, Yi-Ke Guo, Rossella Arcucci

{"title":"Hybrid Data Assimilation: An Ensemble-Variational Approach","authors":"Edward M. Lim, Miguel Molina-Solana, C. Pain, Yi-Ke Guo, Rossella Arcucci","doi":"10.1109/SITIS.2019.00104","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00104","url":null,"abstract":"Data Assimilation (DA) is a technique used to quantify and manage uncertainty in numerical models by incorporating observations into the model. Variational Data Assimilation (VarDA) accomplishes this by minimising a cost function which weighs the errors in both the numerical results and the observations. However, large-scale domains pose issues with the optimisation and execution of the DA model. In this paper, ensemble methods are explored as a means of sampling the background error at a reduced rank to condition the problem. The impact of ensemble size on the error is evaluated and benchmarked against other preconditioning methods explored in previous work such as using truncated singular value decomposition (TSVD). Localisation is also investigated as a form of reducing the long-range spurious errors in the background error covariance matrix. Both the mean squared error (MSE) and execution time are used as measure of performance. Experimental results for a 3D case for pollutant dispersion within an urban environment are presented with promise for future work using dynamic ensembles and 4D state vectors.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124451565","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: 2

Unsupervised Spectral Clustering of Music-Related Brain Activity 与音乐相关的大脑活动的无监督谱聚类

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00041

S. Ntalampiras

{"title":"Unsupervised Spectral Clustering of Music-Related Brain Activity","authors":"S. Ntalampiras","doi":"10.1109/SITIS.2019.00041","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00041","url":null,"abstract":"The recent advancements in Music Information Retrieval are now giving birth to new exciting fields, one of which is concerned with understanding the relationship existing between brain activity and the music stimuli evoking it. Thus, Music Imagery Information Retrieval (MIIR) has emerged with its goal being to bridge the gap existing between encephalographic responses and the respective music signal. This paper employs the OpenMIIR dataset which includes synchronized recordings of brain activity and music signals, thus facilitating MIIR research. Three tasks have been defined, i.e. stimuli identification, group and meter classification, which examine the problem from different viewpoints. After extracting parameters of linear time-invariant models elaborating on electroencephalographic responses, we demonstrate a suitably-designed unsupervised spectral clustering scheme. Such a scheme highlights the connection existing between responses and the audio structure of the music classes corresponding to the three tasks. We show that there is a strong connection w.r.t stimuli identification and meter classification tasks; however that is not true for the group classification case.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070921","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

How to Identify Competence from Interactions 如何从互动中识别能力

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00101

Hocine Merzouki, N. Matta, Hassan Atifi

引用次数: 3

Visual Navigation Using a Webcam Based on Semantic Segmentation for Indoor Robots 基于语义分割的室内机器人网络摄像头视觉导航

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00015

Miho Adachi, Sara Shatari, R. Miyamoto

引用次数: 15

Breast Ultrasound Image Classification Using a Pre-Trained Convolutional Neural Network 基于预训练卷积神经网络的乳腺超声图像分类

2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Pub Date : 2019-11-01 DOI: 10.1109/SITIS.2019.00037

M. Daoud, Samir Abdel-Rahman, R. Alazrai

{"title":"Breast Ultrasound Image Classification Using a Pre-Trained Convolutional Neural Network","authors":"M. Daoud, Samir Abdel-Rahman, R. Alazrai","doi":"10.1109/SITIS.2019.00037","DOIUrl":"https://doi.org/10.1109/SITIS.2019.00037","url":null,"abstract":"Breast ultrasound (BUS) imaging is commonly used for breast cancer diagnosis, but the interpretation of BUS images varies based on the radiologist's experience. Computer-aided diagnosis (CAD) systems have been proposed to provide the radiologist with an objective, computer-based classification of BUS images. Nevertheless, the majority of these systems are based on handcrafted features that are designed manually to quantify the tumor. Hence, the accuracy of these CAD systems depends on the capability of the handcrafted features to differentiate between benign and malignant tumors. Convolutional neural networks (CNNs) provide a promising approach to improve the classification of BUS images due to their ability to achieve data-driven extraction of objective, accurate, and generalizable image representations. However, the limited size of the available BUS image databases might restrict the capability of training the CNNs from scratch. To address this limitation, we investigate the use of two approaches, namely the deep features extraction approach and transfer learning approach, to enable the use of a pre-trained CNN model to achieve accurate classification of BUS images. The results show that the deep features extraction approach outperforms the transfer learning approach. Moreover, the results indicate that the extraction of deep features from the pre-trained CNN model, which is combined with effective features selection, has enabled accurate BUS image classification with accuracy, sensitivity, and specificity values of 93.9%, 95.3%, and 92.5%, respectively. These results suggest the feasibility of combining deep features extracted from pre-trained CNN models with effective features selection algorithms to achieve accurate BUS image classification.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126505889","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: 16