发布求助
文献互助 智能选刊 最新文献

2016 IEEE International Conference on Signal and Image Processing (ICSIP)最新文献

筛选
英文 中文
Study on the extract method of time domain characteristic parameters of pluse wave 脉冲波时域特征参数提取方法的研究
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888299
Ding Xing-yun, C. Qing, Song Chao
{"title":"Study on the extract method of time domain characteristic parameters of pluse wave","authors":"Ding Xing-yun, C. Qing, Song Chao","doi":"10.1109/SIPROCESS.2016.7888299","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888299","url":null,"abstract":"PhotoPlethysmoGraphy(PPG) is a more important method in the current study of blood pressure measurement. And when PPG is used to measure the blood pressure, denoising and feature extraction are the two most important steps, which directly affects the results of the blood pressure measurement. Therefore, how to get more accurate denoise waveform and characteristic parameters is the key point of the current research. This paper designs a real-time, high compatibility and adaptive method to identify the characteristics of pulse wave in time domain based on the extremum method as well as the threshold method, and experiments are used to prove that this method has a certain maneuverability and accuracy.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"44 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":"122498477","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
Brain tissue segmentation with the GKA method in MRI 基于GKA方法的MRI脑组织分割
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888266
Zhiguang Qin, Fei Wang, Zhe Xiao, Tian Lan, Yi Ding
{"title":"Brain tissue segmentation with the GKA method in MRI","authors":"Zhiguang Qin, Fei Wang, Zhe Xiao, Tian Lan, Yi Ding","doi":"10.1109/SIPROCESS.2016.7888266","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888266","url":null,"abstract":"A novel method will be proposed to automatically segment the tissue of brain in magnetic resonance (MR) images. The core idea behind this method is the mixed use of Gaussian mixture model and K-means Algorithm (GKA). In this paper, the brain tissue of MR images will be segmented into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) by adopting the GKA method. Both the classic Gaussian Mixture Model (GMM) clustering algorithm and the classic K-means clustering algorithm have its own shortcomings when segmenting the brain tissue. In order to improve the accuracy of segment result, the GKA fusion method has been proposed to obtain the advantages of both GMM and K-means, which is based on the characteristics of brain tissue MR images. The experiments show that the novel method can achieve a better result.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"42 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":"125018563","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: 4
Mining algorithm of spatial-temporal co-occurrence pattern based on vehicle GPS trajectory 基于车载GPS轨迹的时空共现模式挖掘算法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888240
Zhang Yongmei, Guo Sha, Xing Kuo, Liu Mengmeng
{"title":"Mining algorithm of spatial-temporal co-occurrence pattern based on vehicle GPS trajectory","authors":"Zhang Yongmei, Guo Sha, Xing Kuo, Liu Mengmeng","doi":"10.1109/SIPROCESS.2016.7888240","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888240","url":null,"abstract":"In the calculation process of spatial-temporal co-occurrence patterns, traditional methods often set the whole time frame as the actual existence time by default for all moving targets. However, in practice, existence time frame of different types is not necessarily the whole time frame. Based on this fact, the paper describes the calculation method of spatial-temporal interest degree-spatial frequency and time frequency in order to improve the practicability of co-occurrence patterns. In addition, the paper sets spatial-temporal weight coefficient for every pattern and sorts all candidates of co-occurrence patterns based on their weight. Then high efficiency co-occurrence patterns can be selected easily. Thus the proposed algorithm in this paper provides a solution to the difficulty of setting time thresholds and space thresholds in advance. And the experiment results show that the method can improve the effectiveness of spatial-temporal co-occurrence patterns simultaneously.","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":"130839001","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
Feature selection and regional labeling of significant detection for pulmonary nodules in CT images CT图像中肺结节显著性检测的特征选择与区域标记
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888224
Guilai Han, Yuan Jiao
{"title":"Feature selection and regional labeling of significant detection for pulmonary nodules in CT images","authors":"Guilai Han, Yuan Jiao","doi":"10.1109/SIPROCESS.2016.7888224","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888224","url":null,"abstract":"The function of visual attention mechanism is to acquire the useful visual information at the fastest speed. The Itti visual attention model commonly used at present has achieved good effects in natural image. In order to find the region of interest as soon as possible, this paper attempts to introduce visual attention mechanism into pulmonary nodules detection. However, the Itti model is more to detect significant regions in image as a whole, and it does not reflect size and shape of significant goal. In order to improve detection accuracy, this paper attempt to detect pulmonary nodules by Itti model combined with features of pulmonary nodules. Some primary features such as gray, direction, corner point, edge and local entropy were chosen to generate saliency map. This paper compares emphatically their respective effects and marks the significant areas that they have detected in original image.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"42 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":"134366655","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
Regression-based single image super-resolution via adaptive patches 基于自适应补丁的单幅图像超分辨率回归
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888221
Jing Hu, Jiliu Zhou, Yanfang Wang
{"title":"Regression-based single image super-resolution via adaptive patches","authors":"Jing Hu, Jiliu Zhou, Yanfang Wang","doi":"10.1109/SIPROCESS.2016.7888221","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888221","url":null,"abstract":"Single image super-resolution (SR) generates a high-resolution (HR) image by estimating the mapping function between image patches of different resolutions. By leveraging the notion of regression, the mapping function estimation task is often transformed into predicting mapping function's derivatives. Although higher-orders of derivative lead to a more accurate mapping function, current algorithms only achieve the first-order derivative estimation, due to the ill-conditioned nature of such estimation problem. By observing that the size of patches not only influences the illness of this estimation problem, but also affects the detail reconstruction in the final HR image, we incorporate an adaptive patch size scheme into single image SR in this paper, so as to facilitate the SR algorithm to detail preservation. Experiments on standard images demonstrate the effectiveness of the proposed method both quantitatively and qualitatively, when comparing to other advanced SR algorithms.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"283 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":"132023531","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
Ambiguities in fit-evaluation for selector models 选择器模型的拟合评估的模糊性
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888366
Bhekisipho Twala, M. V. Seotlo
{"title":"Ambiguities in fit-evaluation for selector models","authors":"Bhekisipho Twala, M. V. Seotlo","doi":"10.1109/SIPROCESS.2016.7888366","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888366","url":null,"abstract":"The use of the direct evaluation of the Gaussian Process, using the square exponential function kernel prediction at the given data points is often misleading towards evaluation of the fit, given by the coefficient of determination. The predicted value at the data points when using the Gaussian Process, is almost at all cases equal to the original value. As such, interpretation problems arise when coefficient of determination suggest the model to be a good fit, but visual representations suggest otherwise. We illustrate the difficulties in presenting the coefficient of determination for the Gaussian Process and recommend the use of alternative methods for the evaluation of the predicted value, thus realizing the true function of the coefficient of determination.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"311 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":"132814853","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
Simple signal extension method for discrete wavelet transform 离散小波变换的简单信号扩展方法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888319
David Barina, P. Zemčík, Michal Kula
{"title":"Simple signal extension method for discrete wavelet transform","authors":"David Barina, P. Zemčík, Michal Kula","doi":"10.1109/SIPROCESS.2016.7888319","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888319","url":null,"abstract":"Discrete wavelet transform of finite-length signals must necessarily handle the signal boundaries. The state-of-the-art approaches treat such boundaries in a complicated and inflexible way, using special prolog or epilog phases. This holds true in particular for images decomposed into a number of scales, exemplary in JPEG 2000 coding system. In this paper, the state-of-the-art approaches are extended to perform the treatment using a compact streaming core, possibly in multi-scale fashion. We present the core focused on CDF 5/3 wavelet and the symmetric border extension method, both employed in the JPEG 2000. As a result of our work, every input sample is visited only once, while the results are produced immediately, i.e. without buffering.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"73 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":"114832692","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
Analysis dictionary learning based on max transvection function 基于最大横切函数的分析字典学习
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888253
Haolong Wang, Wanting Fang, Wenwu Wang, Ye Zhang, S. Sanei
{"title":"Analysis dictionary learning based on max transvection function","authors":"Haolong Wang, Wanting Fang, Wenwu Wang, Ye Zhang, S. Sanei","doi":"10.1109/SIPROCESS.2016.7888253","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888253","url":null,"abstract":"Analysis dictionary learning (ADL) aims to design dictionaries from training data based on an analysis sparse representation model. Sparse analysis model is an alternative model to the sparse synthesis model used in a variety of signal processing areas. This paper introduces a new ADL method called MAX-ADL algorithm used to estimate the dictionary directly from the noisy measurements. The algorithm employs MAX transvection function instead of 11-norm to construct the objective function, and then the analysis dictionary can be obtained by using a gradient method to iteratively optimize the objective function. Experimental results show that the algorithm performs well in natural image denoising.","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":"128733128","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
Speech enhancement based on Emd and compressed sensing 基于Emd和压缩感知的语音增强
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888353
Wang Dan, Wang Xia, Wang Guangyan, Zhang Yan
{"title":"Speech enhancement based on Emd and compressed sensing","authors":"Wang Dan, Wang Xia, Wang Guangyan, Zhang Yan","doi":"10.1109/SIPROCESS.2016.7888353","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888353","url":null,"abstract":"Compressed sensing, as a novel framework, needs a small amount of characteristic datas to reconstruct the original datas and breakthroughs the limitation of the Nyquist sampling law, which effectively relieves the pressure of dealing with a large amount of datas in the practical application. But the noise reduction performance in traditional compressed sensing is very poor, in order to improve the situation, this paper proposes a new system that combines compressed sensing techniques with empirical mode deposition signal analysis method, which possesses both the advantages of the compressed sensing and empirical mode deposition. Experimental results show that the system not only can reconstruct the speech signal well, but also plays good noise reduction effect.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"17 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":"132774678","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
A novel integration method for nonuniform pulse-train signal 非均匀脉冲序列信号的一种新的积分方法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888323
Shuai Ding, Tuo Fu, Defeng Chen, M. Gao
{"title":"A novel integration method for nonuniform pulse-train signal","authors":"Shuai Ding, Tuo Fu, Defeng Chen, M. Gao","doi":"10.1109/SIPROCESS.2016.7888323","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888323","url":null,"abstract":"When the nonuniform pulse-train signal is applied to the problem of weak target detection, the traditional long-time integration methods based on FFT are no longer valid. A novel method based on Keystone transform and nonuniform FFT is proposed here to accumulate the echo energy, wherein the former is employed to remove the range walk and the latter realizes the coherent integration of nonuniform pulses. The algorithm's effectivity is further evaluated and verified by numerical simulation experiments.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"16 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":"133547205","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信