2016 IEEE International Conference on Signal and Image Processing (ICSIP)最新文献_第8页

A constructing vehicle intrusion detection algorithm based on BOW presentation model 一种基于BOW表示模型的车辆入侵检测算法

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888249

Jie Yuan, Weihua Cheng, Lingqing Sun, Yiqing Cheng

引用次数: 3

Automatic segmentation algorithm of breast ultrasound image based on improved level set algorithm 基于改进水平集算法的乳腺超声图像自动分割算法

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888276

Xilin Li, Chunlan Yang, Shuicai Wu

{"title":"Automatic segmentation algorithm of breast ultrasound image based on improved level set algorithm","authors":"Xilin Li, Chunlan Yang, Shuicai Wu","doi":"10.1109/SIPROCESS.2016.7888276","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888276","url":null,"abstract":"Breast cancer is one of the leading causes of death in women worldwide. Therefore, ultrasound examination has become an important method of detecting breast tumors. However, given the special features of ultrasonic imaging, lesion segmentation is a challenging task in computer-aided diagnosis systems. In this study, we proposed a complex and automated approach to segment breast ultrasound images. In the preliminary contour selection, an efficient method was performed by preprocessing of breast ultrasound images, selecting the iterative threshold, filtrating candidate areas, and ranking remaining areas to confirm the region of interest (ROI). After the selection of the ROI, a seed point could be determined. Then, region growing started from the selected seed to obtain a preliminary contour that will serve as the intermediate result. Finally a novel and improved level set algorithm was proposed to confirm the final contour, combined with global statistics, local statistics, and region-based energy constraint. The proposed algorithm was tested on a database of 44 breast ultrasound images, and the experimental results proved high accuracy. Compared with the classic Chan-Vese model, the proposed method increases the similarity rate and reduces the error rate.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"367 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122150250","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: 5

Image fusion based on region structure similarity and contourlet transform 基于区域结构相似性和contourlet变换的图像融合

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888263

Jiamin Gong, Beibei Wang, Yingna Deng, Lin Qiao, Huabo Liu, Jiachi Xu, Zhengjun Zhang

引用次数: 1

Low rank matrix recovery from sparse noise by ℓ2,1 loss function 利用损失函数从稀疏噪声中恢复低秩矩阵

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888331

Ji Li, Lina Zhao, Meiling Zhang, Xuke Hou

引用次数: 2

A study of diabetes mellitus detection using sparse representation algorithms with facial block color features 基于面部块颜色特征的稀疏表示算法在糖尿病检测中的应用研究

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888325

Peng Zhang, Bob Zhang

{"title":"A study of diabetes mellitus detection using sparse representation algorithms with facial block color features","authors":"Peng Zhang, Bob Zhang","doi":"10.1109/SIPROCESS.2016.7888325","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888325","url":null,"abstract":"Each year more and more people are diagnosed with Diabetes Mellitus. As this disease continues to grow, it will have an enormous effect on society. Recently, a computerized noninvasive diagnostic method was proposed using facial block color features with a sparse representation classifier. This method eliminated the need to extract bodily fluids, and any feelings of pain and discomfort associated with a Fasting Plasma Glucose test. Though its result is promising and the detection can be considered to be accurate, there is still much room for improvement and increment in the diagnostic accuracy. In addition, the effects of sparse representation have not been extensively investigated for this application. In this paper a study of sparse representation algorithms is carried out to determine its effectiveness at distinguishing facial block(s) from two classes, Diabetes Mellitus and Healthy. Four groups of sparse representation algorithms are examined. They include greedy strategy approximation, constrained optimization strategy, proximity algorithm based optimization strategy, and homotopy algorithm based sparse representation. Facial block color features are extracted and used with a representative method from each group to perform classification. The experimental results show that the orthogonal matching pursuit algorithm from the greedy strategy approximation group achieves the best performance of 99.65% — sensitivity, 97.93% — specificity, and 99.06% — accuracy at discriminating individuals from either class using their facial block(s).","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128910712","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

Design of wideband radar signal simulator based on high-speed DDS technology 基于高速DDS技术的宽带雷达信号模拟器设计

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888322

Liu Xing-hai, Yang Jian, Yu Yong, Shen Kai

引用次数: 3

Self-adaptive level set methods combined with geometric active contour 结合几何活动轮廓的自适应水平集方法

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888328

Heng Wang, Zihan Zhuo, Jianan Wu, Jingtian Tang

引用次数: 1

An improved method of nonlinear distortion correction for wide-angle lens 一种改进的广角镜头非线性畸变校正方法

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888226

Xianlun Wang, M. Li, Yuxia Cui

引用次数: 1

Speech recognition based on convolutional neural networks 基于卷积神经网络的语音识别

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888355

Du Guiming, Wang Xia, Wang Guangyan, Zhang Yan, Li Dan

引用次数: 31

A novel reversible ternary embedding algorithm based on modified full context prediction errors 一种基于修正全上下文预测误差的可逆三元嵌入算法

2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888318

Li Li, Chinchen Chang, K. Bharanitharan, Yanjun Liu

引用次数: 0