2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第9页

A comparative study of voice conversion techniques: A review 语音转换技术的比较研究综述

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075528

Kadria Ezzine, M. Frikha

引用次数: 6

Tumor extraction and elimination of pectoral muscle based on hidden Markov and region growing: Applied based MIAS 基于隐马尔可夫和区域生长的胸肌肿瘤提取与消除:基于MIAS的应用

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075583

Soukaina El Idrissi El Kaitouni, A. Abbad, H. Tairi

引用次数: 1

Embedded approach for a Riemannian-based framework of analyzing 3D faces 基于黎曼的三维人脸分析框架的嵌入式方法

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075548

T. Frikha, Faten Chaabane, Boukhchim Said, Hassen Drira, Mohamed Abid, C. Amar, Lifl Lille

引用次数: 0

Landmine detection improvement using one-class SVM for unbalanced data 基于一类支持向量机的非平衡数据地雷探测改进

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075597

Khaoula Tbarki, S. B. Said, Riadh Ksantini, Z. Lachiri

引用次数: 8

A fusion-based blind image quality metric for blurred stereoscopic images 基于融合的模糊立体图像盲图像质量度量

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075530

A. Chetouani

引用次数: 4

Automatic body segmentation from computed tomography image 计算机断层扫描图像的人体自动分割

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075612

O. Dorgham

{"title":"Automatic body segmentation from computed tomography image","authors":"O. Dorgham","doi":"10.1109/ATSIP.2017.8075612","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075612","url":null,"abstract":"Medical imaging segmentation provides vital information for surgical diagnosis, and usually demands an accurate segmentation. A fully automated computed tomography image segmentation method is proposed. This method is unsupervised and automatic estimation of the required parameters for identifying the human body as a region of interest. The proposed methodology consists of four steps: First, a body region of interest is masked by a method based on thresholding and basic morphological operations. Second, a body region of interest is identified using chain codes and a method for collecting adjacent contours. Next, the identification of background non-regions of interest is performed using an entropy algorithm. Finally, the human body segment is identified using a GrabCut algorithm. According to the visual evaluation results, segmentation of the human body, from the Computed Tomography images, was seen to be precise and accurate. The analysis provided evidence that the human body segmentation method could be applied to segmenting other organs, registering different image modalities or speeding-up the generation of digitally reconstructed radiographs.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700572","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

Hybrid context dependent CD-DNN-HMM keywords spotting on continuous speech 基于上下文的CD-DNN-HMM连续语音关键字识别

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075541

Hinda Dridi, K. Ouni

引用次数: 1

Use of ropes histograms as joints trajectories representation for human motion recognition 绳索直方图作为关节轨迹表示在人体运动识别中的应用

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075532

Z. Nejim, Makrem Mestiri, H. Amiri

引用次数: 0

Breast tumor classification based on deep convolutional neural networks 基于深度卷积神经网络的乳腺肿瘤分类

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075562

I. Bakkouri, K. Afdel

{"title":"Breast tumor classification based on deep convolutional neural networks","authors":"I. Bakkouri, K. Afdel","doi":"10.1109/ATSIP.2017.8075562","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075562","url":null,"abstract":"This paper presents a novel deep learning approach focused on the classification of tumors in mammograms as malignant or benign. It is a modern machine learning method which promises to create models that learn from large dataset and make accurate predictions. In this study, we propose a discriminative objective for supervised feature learning by training a Convolutional Neural Network (CNN). Choosing CNN involves input image with a fixed-length and as a consequence, we equip our networks with a scaling process based on Gaussian pyramids for obtaining regions of interest with normalized size. The dataset used in this research is augmented with applying the geometric transformation techniques in order to prevent overfitting and create a robust deep learning model. We perform classification with Softmax layer. It is used to train CNN for classification. We evaluate our methodology on both of the publicly available dataset DDSM and BCDR. In comparison with the current state-of-the-art methods, the experiments show that our proposed system provides good results, achieving high accuracy of 97.28% that will assist radiologists in making diagnostic decisions without increasing false negatives.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116617720","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: 26

Usefulness of time-frequency treatment to an acoustic signal 声信号时频处理的有效性

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075578

A. Elhanaoui, G. Maze, E. Aassif, D. Decultot

引用次数: 0