2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第6页

Spatially-variant mathematical morphology for color images 彩色图像的空间变化数学形态学

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-22 DOI: 10.1109/ATSIP.2017.8075539

Sara Belmil, M. Charif-Chefchaouni

引用次数: 0

Boundary connectedness based video cut for moving object segmentation 基于边界连通性的视频切割运动目标分割

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-22 DOI: 10.1109/ATSIP.2017.8075596

Hiba Ramadan, H. Tairi

引用次数: 0

Quantitative measures in ISAR image formation based on time-frequency representations 基于时频表示的ISAR图像生成定量度量

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-22 DOI: 10.1109/ATSIP.2017.8075610

J. Cexus, A. Toumi, Orian Couderc

引用次数: 2

A CAD system for the detection and classification of abnormalities in dense mammograms using electromagnetism-like optimization algorithm 一种利用类电磁优化算法对致密乳房x线检查异常进行检测和分类的CAD系统

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-22 DOI: 10.1109/ATSIP.2017.8075533

Khaoula Belhaj Soulami, Mohamed Nabil Saidi, A. Tamtaoui

{"title":"A CAD system for the detection and classification of abnormalities in dense mammograms using electromagnetism-like optimization algorithm","authors":"Khaoula Belhaj Soulami, Mohamed Nabil Saidi, A. Tamtaoui","doi":"10.1109/ATSIP.2017.8075533","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075533","url":null,"abstract":"The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most efficient technique in terms of identifying the suspicious lesions in the breast. However, the interpretation of this screening remains so hard and could lead to inaccurate detection known as false positive and false negative. Dense breast category mammograms particularly, are difficult to read, because it may contain abnormal structures that are similar to the normal breast tissue. In this paper, we introduce an effecient Computer-Aided-Diagnosis system for the detection and classification of the ambiguous areas in dense breast mammograms. After noise and artifacts removal from the images using 2D Median filtering and labeling, we isolate the abnormalities using the metaheuristic algorithm Electromagnetism-like Optimization (EML), then we extract shape-based descriptors from the region of interest(ROI) using Zernike Moments. The detected abormal regions were classified into normal and abnormal based on the extracted shape features and through the Support Vector Machine(SVM) classification.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321226","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: 7

A study of LoRa low power and wide area network technology LoRa低功耗广域网技术的研究

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-22 DOI: 10.1109/ATSIP.2017.8075570

U. Noreen, A. Bounceur, L. Clavier

引用次数: 179

Automatic detection of early stages of Parkinson's disease through acoustic voice analysis with mel-frequency cepstral coefficients 通过mel频率倒谱系数的声学声音分析自动检测早期帕金森病

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-22 DOI: 10.1109/ATSIP.2017.8075567

Laetitia Jeancolas, H. Benali, B. Benkelfat, G. Mangone, J. Corvol, M. Vidailhet, S. Lehéricy, D. Petrovska-Delacrétaz

{"title":"Automatic detection of early stages of Parkinson's disease through acoustic voice analysis with mel-frequency cepstral coefficients","authors":"Laetitia Jeancolas, H. Benali, B. Benkelfat, G. Mangone, J. Corvol, M. Vidailhet, S. Lehéricy, D. Petrovska-Delacrétaz","doi":"10.1109/ATSIP.2017.8075567","DOIUrl":"https://doi.org/10.1109/ATSIP.2017.8075567","url":null,"abstract":"Vocal impairments are one of the earliest disrupted modalities in Parkinson's disease (PD). Most of the studies whose aim was to detect Parkinson's disease through acoustic analysis use global parameters. In the meantime, in speaker and speech recognition, analyses are carried out by short-term parameters, and more precisely by Mel-Frequency Cepstral Coefficients (MFCC), combined with Gaussian Mixture Models (GMM). This paper presents an adaptation of the classical methodology used in speaker recognition to the detection of early stages of Parkinson's disease. Automatic analyses were performed during 4 tasks: sustained vowels, fast syllable repetitions, free speech and reading. Men and women were considered separately in order to improve the classification performance. Leave one subject out cross validation exhibits accuracies ranging from 60% to 91% depending on the speech task and on the gender. Best performances are reached during the reading task (91% for men). This accuracy, obtained with a simple and fast methodology, is in line with the best classification results in early PD detection found in literature, obtained with more complex methods.","PeriodicalId":259951,"journal":{"name":"2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129870374","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: 32

Multiscale approach for skin lesion analysis and classification 皮肤病变分析与分类的多尺度方法

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075545

Y. Filali, A. Ennouni, M. A. Sabri, A. Aarab

引用次数: 16

An-open system for off-line handwritten signature identification and verification using histogram of templates and SVM 基于模板直方图和支持向量机的离线手写签名识别与验证开放系统

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075594

Feriel Boudamous, H. Nemmour, Yasmine Serdouk, Y. Chibani

引用次数: 7

Pedestrian group attributes detection in crowded scenes 拥挤场景下行人群体属性检测

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075584

Nuhu Aliyu Shuaibu, A. Malik, I. Faye, Y. Ali

引用次数: 1

Per-pixel displacement mapping using hybrid cone approach 使用混合锥方法的逐像素位移映射

2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2017-05-01 DOI: 10.1109/ATSIP.2017.8075577

Adnane Ouazzani Chahdi, A. Halli, Anouar Ragragui, K. Satori

引用次数: 3