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

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A Gaussian mixture probability hypothesis density smoothing algorithm for multi-target track-before-detect 多目标检测前跟踪的高斯混合概率假设密度平滑算法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888346
Zhu Hongpeng, Huang Yong, Jiang Baichen, Guan Jian
{"title":"A Gaussian mixture probability hypothesis density smoothing algorithm for multi-target track-before-detect","authors":"Zhu Hongpeng, Huang Yong, Jiang Baichen, Guan Jian","doi":"10.1109/SIPROCESS.2016.7888346","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888346","url":null,"abstract":"When the signal-to-noise ratio (SNR) is reduced in case of track-before-detect (TBD) for weak target detection, the TBD algorithm based on Gaussian mixture probability hypothesis density (GM-PHD) filter cannot estimate the number or status of targets accurately. In order to solve this problem, a TBD algorithm based on GM-PHD smoothing filter (SGM-PHD-TBD) is proposed. Within the framework of TBD standard observation model, the algorithm employs smooth recursive method, using quantities of measurement data to smooth the filtering results. The simulation result shows that the proposed algorithm is better than the GM-PHD-TBD algorithm under low SNR, especially in the accuracy of target number estimation and the precision of target status estimation.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"11 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":"130607959","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
A traffic sign detection algorithm based on deep convolutional neural network 一种基于深度卷积神经网络的交通标志检测算法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888348
Changzhen Xiong, W. Cong, Weixin Ma, Shang Yanmei
{"title":"A traffic sign detection algorithm based on deep convolutional neural network","authors":"Changzhen Xiong, W. Cong, Weixin Ma, Shang Yanmei","doi":"10.1109/SIPROCESS.2016.7888348","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888348","url":null,"abstract":"Traffic sign detection plays an important role in driving assistance systems and traffic safety. But the existing detection methods are usually limited to a predefined set of traffic signs. Therefore we propose a traffic sign detection algorithm based on deep Convolutional Neural Network (CNN) using Region Proposal Network(RPN) to detect all Chinese traffic sign. Firstly, a Chinese traffic sign dataset is obtained by collecting seven main categories of traffic signs and their subclasses. Then a traffic sign detection CNN model is trained and evaluated by fine-tuning technology using the collected dataset. Finally, the model is tested by 33 video sequences with the size of 640×480. The result shows that the proposed method has towards real-time detection speed and above 99% detection precision. The trained model can be used to capture the traffic sign from videos by on-board camera or driving recorder and construct a complete traffic sign dataset.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"71 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":"132270867","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: 46
Depth camera based fall detection using human shape and movement 基于深度相机的基于人体形状和运动的跌倒检测
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888330
Fairouz Merrouche, N. Baha
{"title":"Depth camera based fall detection using human shape and movement","authors":"Fairouz Merrouche, N. Baha","doi":"10.1109/SIPROCESS.2016.7888330","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888330","url":null,"abstract":"The number of elderly people living alone have increased over the last years and fall is one of major risks that threaten their lives. Computer vision is one of the accurate solution for fall detection. In this paper, we propose a new method for fall detection using depth camera. This method combines human shape analysis, head tracking and center of mass detection by exploiting the advantages of Kinect. In addition, we take into account the motion information, and use the relationship between time and distance translated by covariance to discriminate falls. The experiments with SDUFall dataset which contains 20 subjects performing five daily activities and falls demonstrate that the proposed method can achieve up to 92.98% accuracy.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"18 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":"130288141","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: 31
Doppler radar real-time signal processing based on FPGA 基于FPGA的多普勒雷达实时信号处理
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888301
Yafei Li, Yingying Du, X. Ye, Zhengyu Cai
{"title":"Doppler radar real-time signal processing based on FPGA","authors":"Yafei Li, Yingying Du, X. Ye, Zhengyu Cai","doi":"10.1109/SIPROCESS.2016.7888301","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888301","url":null,"abstract":"Doppler radar real-time signal processing is one of the core equipment in the speed measuring system. The working principle is presented in this paper, and an efficient real sequence FFT algorithm is proposed based on the Fast Fourier Transform, which can reduce the computation complexity effectively. The design with low cost FPGA implementation, high performance, high flexibility, speed and other characteristic feature, and through the engineering application proves that the design is correct and feasible.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"41 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":"125508599","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
Improved wavelet transform for noise reduction in power analysis attacks 改进的小波变换在功率分析攻击中的降噪
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888333
J. Ai, Zhu Wang, Xinping Zhou, Changhai Ou
{"title":"Improved wavelet transform for noise reduction in power analysis attacks","authors":"J. Ai, Zhu Wang, Xinping Zhou, Changhai Ou","doi":"10.1109/SIPROCESS.2016.7888333","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888333","url":null,"abstract":"In side channel attacks (SCA), noise has been a hot topic for affecting the quality of obtained observations. In this paper, we propose a kind of improved wavelet transform denoising method based on singular spectral analysis (SSA) and detrended fluctuation analysis (DFA). Principal signal component in SSA can be selected by DFA adaptively, and residual part can be denoised by wavelet transform to retrieve important information. The method of superposition between signal component and denoised residual part improves the denoising efficiency of original wavelet transform. In order to verify the usefulness of the proposed method, we choose the correlation power analysis (CPA) to attack hard implementation of AES by using wavelet transform and the proposed method for preprocessing. Results show that the proposed method improve the success rate whilst decrease the necessary number of power consumption traces significantly. And the proposed method outperforms wavelet transform in noise elimination.","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":"125581160","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
Segmentation algorithm for MRI images using global entropy minimization 基于全局熵最小化的MRI图像分割算法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888212
Weihua Zhu
{"title":"Segmentation algorithm for MRI images using global entropy minimization","authors":"Weihua Zhu","doi":"10.1109/SIPROCESS.2016.7888212","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888212","url":null,"abstract":"Medical image processing plays an important role in supporting the diagnosis of various diseases. Brain magnetic resonance imaging (MRI) image is widely used to support the decisions from doctors who will decide if there are any issues in a brain. The essence of the MRI is segmentation which is the basic for damaged area selection, quantitative measurement and 3-dimensional reconstruction. In order to effectively identify the located objects, this paper introduces a segmentation algorithm using global entropy minimization. This algorithm uses two times segmentation approach based on the cluster area image model to overcome the negative influences of shifted segmentation. From the experiments, the proposed algorithm get the best performance and keeps the highest accuracy. For the similarity, the proposed algorithm has almost the same performance of least biased fuzzy clustering (LBFC) which have 10% out performance on fuzzy C-means algorithm (FCMA).","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"99 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":"126361913","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
Mis-modeling and mis-correction of mutual coupling in an antenna array — A case study in the context of direction finding using a linear array of identical dipoles 天线阵列中相互耦合的错误建模和错误校正-在使用相同偶极子线性阵列的测向背景下的一个案例研究
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888302
Y. Wu, G. Arada, W. Tam, K. T. Wong
{"title":"Mis-modeling and mis-correction of mutual coupling in an antenna array — A case study in the context of direction finding using a linear array of identical dipoles","authors":"Y. Wu, G. Arada, W. Tam, K. T. Wong","doi":"10.1109/SIPROCESS.2016.7888302","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888302","url":null,"abstract":"In an array of antennas, the inter-antenna electromagnetic mutual coupling, unless properly corrected, would degrade an antenna array's performance in direction finding. In the special case of a uniformly spaced linear array of identical antennas, Azarbar, Dadashzadeh & Bakshi have suggested discarding the antennas' collected data at both ends of the linear array, on the assumption that all middle antennas experience near-identical mutual coupling, as each middle antenna would have many antennas on either side. The antennas whose data will be discarded are called the “auxiliary” antennas. While Azarbar, Dadashzadeh & Bakshi modeled the mutual coupling matrix as Toeplitz and banded, this key assumption is invalidated by simulations using the “method of moments” (as realized in the software “EMCoS Antenna VLab”). Using such more realistic values for the mutual coupling matrix for a uniform linear array (ULA) of identical dipoles, instead of the idealistic assumptions of a Toeplitz banded mutual coupling matrix, the method in Azarbar, Dadashzadeh & Bakshi's paper is found here to perform very poorly.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"36 5 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":"115253739","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
An active contour method using harmonic mean 一种基于谐波平均的主动轮廓法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888269
Amir Razi, Wei-wei Wang, Xiangchu Feng
{"title":"An active contour method using harmonic mean","authors":"Amir Razi, Wei-wei Wang, Xiangchu Feng","doi":"10.1109/SIPROCESS.2016.7888269","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888269","url":null,"abstract":"Active Contour method has been shown very effective in detecting the contour of region(s)-of-interest(ROI) and is widely used in image processing and computer vision. In this work, we aim to improve the performance of Zhang's method in detecting boundary of ROIs. Specifically, we will generalize the CV energy functional and give a new special case. The new energy functional penalize the approximation error (of the original image by a constant) weaker than the CV energy functional, which can better preserve the subtle difference between the intensity of ROIs and that of the background, thus can effectively segment images, especially images with low contrast. The resulted two-phase constant approximation is the harmonic mean instead of the arithmetic mean. Based on this, we improve Zhang's active contour method by using the harmonic mean. We apply the proposed method on synthetic and real images and the segmentation results show that the proposed method is robust to noise and intensity contrast. Additionally, the proposed method is less sensitive than Zhang's method to parameter selection.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"341 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":"122838023","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
Second order-based image retrieval algorithm 基于二阶的图像检索算法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888213
Daguang Jiang, Junkai Yi
{"title":"Second order-based image retrieval algorithm","authors":"Daguang Jiang, Junkai Yi","doi":"10.1109/SIPROCESS.2016.7888213","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888213","url":null,"abstract":"Under the environment of big data, retrieval becomes a crucial technology and image retrieval is paid more attention and widely used. The paper proposes a second-order retrieval algorithm, of which can be used to retrieval the similar images. Firstly, extracting image sift features. Then, build frequency table of characteristic words by k-means clustering and bag of word algorithm. Finally, based on word frequency table, first retrieve the images that have similar distribution characteristics of the structure. The second-order retrieval implement accurate retrieval of images according to the proportion of the corresponding feature points that belonging to the same class. The experimental results show that this method has good recall factor and good effect on query efficiency. It's a kind of method can be used.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"7 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":"122889554","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
A multi-features based corner detection method 基于多特征的角点检测方法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888347
J. Teng, Jian Li, X. An, Hangen He
{"title":"A multi-features based corner detection method","authors":"J. Teng, Jian Li, X. An, Hangen He","doi":"10.1109/SIPROCESS.2016.7888347","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888347","url":null,"abstract":"To improve the accuracy of corner's detection in the traditional black and white chessboard, a new method based on multi-features is proposed. Three distinct local features of the corners have been analyzed, they are structural response, symmetric response and edge response. By selectively applying these features, initial selection and later screening of potential corners have been done. Non-maximum suppression (NMS) has been used to generate original potential corner candidates, which could be scored by the combination of feature responses mentioned above. With all scores reasonably thresholded, false corners could be removed. Meanwhile, sub-pixel level of corner coordinates is achieved using the orthogonality of potential corners and adjacent pixels. Experimentally, final results prove the effectiveness and robustness of the proposed method with high sub-pixel accuracy.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"os-14 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":"127764431","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
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