2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)最新文献_第5页

Neural controller of autonomous driving mobile robot by an embedded camera 基于嵌入式摄像头的自主驾驶移动机器人神经控制器

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364445

Hajer Omrane, M. Masmoudi, M. Masmoudi

引用次数: 5

Forecasting of the normalized difference vegetation index time series in Jbeniana 青海地区归一化植被指数时间序列的预测

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364499

Marwa Hachicha, M. Louati, A. Kallel

引用次数: 1

An adaptive semantic dimensionality reduction approach for hyperspectral imagery classification 一种用于高光谱图像分类的自适应语义降维方法

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364504

Rawaa Hamdi, A. Sellami, I. Farah

{"title":"An adaptive semantic dimensionality reduction approach for hyperspectral imagery classification","authors":"Rawaa Hamdi, A. Sellami, I. Farah","doi":"10.1109/ATSIP.2018.8364504","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364504","url":null,"abstract":"Hyperspectral imagery (HSI) is widely used for several fields of remote sensing such as agriculture, land cover monitoring, and deforestation. However, the HSI classification is a challenge task due to the large number of spectral bands, unavailability of training samples, and the high correlation inter-bands. To address these challenges, we propose in this work a semantic reduction dimensionality approach based on the principal component analysis (PCA) and mutual information-based band selection (MI). Firstly, we project the original HSI using PCA to obtain a novel subspace with lower dimensions. Using the obtained components, a set of rules can be generated to find the relevant spectral bands based on score contribution coefficient. Moreover, the mutual information (MI) is used to select the spectral bands that contain a higher information based on the entropy criterion. We propose then to exploit the selected bands for HSI classification using SVM technique. Experiment results demonstrate that our proposed approach is effective and perform for HSI classification compared to other dimensionality reduction approaches.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128714162","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

Comparing the scalogram images of the Beta-Globin gene in Homosapiens and Pan Troglodytes 现代人与泛穴居人β -珠蛋白基因的尺度图比较

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364466

Imen Messaoudi, A. Oueslati, Z. Lachiri

引用次数: 0

FFT implementation and optimization on FPGA FFT在FPGA上的实现与优化

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364454

T. Belabed, S. Jemmali, C. Souani

引用次数: 5

Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging 宫颈癌磁共振成像的半自动淋巴结分割与分类

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364480

Nesrine Bnouni, Olfa Mechi, I. Rekik, M. S. Rhim, N. Amara

{"title":"Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging","authors":"Nesrine Bnouni, Olfa Mechi, I. Rekik, M. S. Rhim, N. Amara","doi":"10.1109/ATSIP.2018.8364480","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364480","url":null,"abstract":"The segmentation and classification of Lymph Nodes (LNs) is a fundamental but challenging step in the analysis of medical images of cervical cancer. Both tasks can leverage morphological features such as size, shape, contour, and heterogeneous appearance. However, these features might vary with the progressive state of LNs. Hence, accurate detection of LNs boundary is an essential step sing to classify LN as suspect (malignant) and non-suspect (benign). However, manual delineation of LNs might produce classification errors due to the inter and intra-observer variability. Semi-automatic and automatic LNs segmentation methods are greatly desired as they would help improve patient diagnosis and treatment processes. Currently, Magnetic Resonance Imaging (MRI) is widely used to diagnose cervical cancer and LN involvement. Diffusion Weighted (DW)-MRI exhibits metastatic LN as bright regions. This paper presents a semi-automatic segmentation and classification method of LNs. Specifically, we propose a novel approach which leverages (1) the complementarity of structural and diffusion MR images through a fusion step and (2) morphological features of the segmented metastatic LNs for classification. The contribution of our proposed algorithm is threefold. First, we fuse the axial T2-Weighted (T2-w) anatomical image and the DW image. Second, we detect LNs using region-growing method in order to compute the final classification. Third, segmentation results are then used to classify LNs based on a gray level dependency matrix technique which extracts LN features. We evaluated our method using 10 MR images T2-w and DW with 47 metastatic LNs. We obtained an average accuracy of 70.21% for cervical cancer nodule classification.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121639151","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: 11

Non blind image restoration scheme combining parametric wiener filtering and BM3D denoising technique 结合参数维纳滤波和BM3D去噪技术的图像非盲恢复方案

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364524

Zouhair Mbarki, H. Seddik, E. B. Braiek

引用次数: 8

Semi-automatic algorithm for 3D volume reconstruction of inner ear structures based on CT-scan images 基于ct扫描图像的内耳结构三维体积重建半自动算法

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364474

Adil Bouchana, Jamal Kharroubi, Mohammed Ridal

引用次数: 3

Haralick feature selection for material rigidity recognition using ultrasound echo 基于超声回波的材料刚度识别Haralick特征选择

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364483

Sonda Ammar Bouhamed, Marwa Chakroun, I. Kallel, H. Derbel

{"title":"Haralick feature selection for material rigidity recognition using ultrasound echo","authors":"Sonda Ammar Bouhamed, Marwa Chakroun, I. Kallel, H. Derbel","doi":"10.1109/ATSIP.2018.8364483","DOIUrl":"https://doi.org/10.1109/ATSIP.2018.8364483","url":null,"abstract":"Object classification based on its rigidity requires principally the recognition of its material consistency. Generally, material consistency can be divided into two families, hard material and soft one. In this context, a new approach based on ultrasonic signal for consistency recognition of object materials is proposed. This approach allows distinguishing between the hard and the soft objects. Material consistency determination is based on Haralick's texture features. Then, a feature selection step is considered to select the most discriminative features. Only three Haralick features were used to assess the efficiency classifications models. As there is no affording dataset of ultrasonic signals acquired for material rigidity recognition, we develop our dataset using two ultrasonic sensors. In this context, no previous work has considered such a challenge. The analysis results show that three parameters (entropy, sum of entropy and variance) were found to be effective to discriminate between the two classes of material rigidity. The obtained results show the efficiency of the proposed method.","PeriodicalId":332253,"journal":{"name":"2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127825317","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

Semi fragile watermarking scheme for image recovery in wavelet domain 小波域图像恢复的半脆弱水印方案

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) Pub Date : 2018-03-21 DOI: 10.1109/ATSIP.2018.8364447

Hanen Rhayma, A. Makhloufi, H. Hamam, A. Hmida

引用次数: 5