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

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High sensitive acquisition of signals for inter-satellite links of navigation constellation based on two-dimension partitioned FFTs 基于二维分割fft的导航星座星间链路高灵敏度信号采集
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888327
Yinyin Tang, Yueke Wang, Jianyun Chen
{"title":"High sensitive acquisition of signals for inter-satellite links of navigation constellation based on two-dimension partitioned FFTs","authors":"Yinyin Tang, Yueke Wang, Jianyun Chen","doi":"10.1109/SIPROCESS.2016.7888327","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888327","url":null,"abstract":"A direct sequence spread spectrum (DSSS) signal is commonly used in inter-satellite link (ISL) in navigation constellation; however, acquisition is a challenging task because of large-scale relative movement between satellites. In general, a signal search (carrier frequency and code phase) is implemented using two-dimensional partitioned fast Fourier transforms (FFTs), which is limited by the memory of the processor. Nevertheless, the carrier's continuity is damaged by the circular shift. Noncoherent integration is performed to circumvent this problem. However, the acquisition sensitivity will be reduced because of the square loss. In this paper, we analyze the processing flow of traditional noncoherent integration method and the new presented coherent integration method with compensation. Besides, the comparison of computation cost between the new approach and the conventional method is done. Numerical results show that the coherent method can improve the sensitivity of acquiring weak signals and save about 60% computation cost as well.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115595784","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
Infrared pedestrian detection utilizing entropy-edge weighted local gradient orientation descriptor 基于熵边加权局部梯度方向描述符的红外行人检测
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888279
Yuhao Yue, Qing Chang, Moufa Hu
{"title":"Infrared pedestrian detection utilizing entropy-edge weighted local gradient orientation descriptor","authors":"Yuhao Yue, Qing Chang, Moufa Hu","doi":"10.1109/SIPROCESS.2016.7888279","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888279","url":null,"abstract":"Detecting infrared pedestrian in outdoor smart video surveillance is always a challenging and difficult problem. Although there have been many methods based on histograms of oriented gradients (HOG) to solve this problem, they would probably fail because of shelter and poor quality of image. To overcome this problem, we propose a robust feature to describe pedestrian which is called entropy-edge weighted local gradient orientation (EEWLGO) descriptor. This feature firstly extracts the “orientation image” to depict pedestrian. Then “pixels” of “orientation image” is reshaped to a vector and it is combined with edge histogram to generate the final proposed EEWLGO descriptor. The descriptor outperforms other methods in not only some kinds of shelters but also robustness to noisy clutters. What's more, the processing time is also approximately identical to others, which fulfils the general real time property of surveillance. Cross validation and test on other datasets demonstrate the high accuracy and good robustness of our algorithm.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116407919","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
Multi-scale correlation tracking with convolutional features 基于卷积特征的多尺度相关跟踪
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888274
Yulong Xu, Yang Li, Jiabao Wang, Shan Zou, Zhuang Miao, Yafei Zhang
{"title":"Multi-scale correlation tracking with convolutional features","authors":"Yulong Xu, Yang Li, Jiabao Wang, Shan Zou, Zhuang Miao, Yafei Zhang","doi":"10.1109/SIPROCESS.2016.7888274","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888274","url":null,"abstract":"Feature extractor plays an important role in visual tracking due to the changing appearance of the object. In this paper, we propose a novel approach in correlation filter framework, which decomposes the task of tracking into translation and scale estimation. We employ two correlation filters with hierarchical convolutional features to estimate the translation. Furthermore, we use a discriminative correlation filter with histogram of oriented gradient features to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art tracking methods in accuracy and robustness.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129330557","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 novel image encryption algorithm based on bit-level improved Arnold transform and hyper chaotic map 一种基于比特级改进阿诺德变换和超混沌映射的图像加密算法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888243
Zhengchao Ni, Xuejing Kang, Lei Wang
{"title":"A novel image encryption algorithm based on bit-level improved Arnold transform and hyper chaotic map","authors":"Zhengchao Ni, Xuejing Kang, Lei Wang","doi":"10.1109/SIPROCESS.2016.7888243","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888243","url":null,"abstract":"In this paper, a novel image encryption algorithm based on bit-level Arnold transform and hyper chaotic maps is proposed. To commence, the scheme decomposes the original grayscale image into 8 binary images. Then we use chaos to generate sequences to shift the images before manipulating Arnold transform. Finally, the hyper chaotic map is adopted to produce pseudorandom sequences to diffuse the binary images. The computational simulations have proved that the proposed algorithm is effective for image encryption.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129172291","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: 16
Robust color demosaicking via vectorial hessian frobenius norm regularization 通过向量hessian frobenius范数正则化实现鲁棒色彩去马赛克
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888244
Xuan Wu, Songze Tang, Lili Huang, W. Shao, Pengfei Liu, Zhihui Wei
{"title":"Robust color demosaicking via vectorial hessian frobenius norm regularization","authors":"Xuan Wu, Songze Tang, Lili Huang, W. Shao, Pengfei Liu, Zhihui Wei","doi":"10.1109/SIPROCESS.2016.7888244","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888244","url":null,"abstract":"Single sensor camera captures scenes using a color filter array, such that each pixel samples only one of the three primary colors. A process called color demosaicking (CDM) is used to produce full color image. In this paper, we present a new variational model for high quality CDM. The robust data term is measured by Z1-norm to repress the heavy tailed artifacts. The regularization term is measured by vectorial Hessian Frobenius norm (VHFN) to capture the higher order edges as well as the intra-correlations across different channels simultaneously. To solve the proposed model, an efficient algorithm is designed using alternating direction method of multiplier (ADMM). Experimental results demonstrate that the proposed CDM method outperforms many state-of-the-art methods in reducing color artifacts, preserving the sharp edges and reconstructing fine details.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114799162","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
Approximate compressor based multiplier design methodology for error-resilient digital signal processing 基于近似压缩器的抗误差数字信号处理乘法器设计方法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888362
Zhixi Yang, Jun Yang, Kefei Xing, Guang Yang
{"title":"Approximate compressor based multiplier design methodology for error-resilient digital signal processing","authors":"Zhixi Yang, Jun Yang, Kefei Xing, Guang Yang","doi":"10.1109/SIPROCESS.2016.7888362","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888362","url":null,"abstract":"Multiplier is a fundamental component for digital signal processing (DSP) applications and takes up the most part of the resource utilization, namely power and area. Approximate circuitry architectures have been studied as innovative paradigm for reducing resource utilization for DSP systems. In this paper, the 4:2 compressor based approximate multiplier architecture which uses both truncation and approximation of compressor is studied. A greedy selection algorithm is then proposed to identify the Pareto frontier to give the optimal accuracy-power tradeoff. A finite impulse response (FIR) filter is used as an assessment. The architecture proposed in this paper has achieved up to 21.03% and 27.72% saving on power and area for FIR filter case compared to conventional multiplier designs with a decrease of 0.3dB in output SNR.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127748877","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
A fall detection method based on acceleration data and hidden Markov model 基于加速度数据和隐马尔可夫模型的跌倒检测方法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888350
Huiqiang Cao, Shuicai Wu, Zhuhuang Zhou, Chung-Chih Lin, Chih-Yu Yang, S. Lee, Chieh-Tsai Wu
{"title":"A fall detection method based on acceleration data and hidden Markov model","authors":"Huiqiang Cao, Shuicai Wu, Zhuhuang Zhou, Chung-Chih Lin, Chih-Yu Yang, S. Lee, Chieh-Tsai Wu","doi":"10.1109/SIPROCESS.2016.7888350","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888350","url":null,"abstract":"Falls have been a major health risk that diminishes the quality of life among the elderly. In this paper, we propose a new method using acceleration data and hidden Markov model (HMM) to detect fall events. A wearable device integrating a tri-axial accelerometer was used to collect acceleration data of human chest. Feature sequences (FSs) were extracted from the acceleration data and used as sequence of observations to train an HMM of fall detection. The probability of the input FS generated by the model was calculated as the detection standard. Experimental results showed that the accuracy of the proposed method was 97.2%, the sensitivity was 91.7%, and the specificity was 100%, demonstrating desired performance of our method in detecting fall events.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121467715","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: 15
A method of matching strokes based on genetic algorithm 基于遗传算法的笔画匹配方法
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888284
Hao Bai, Xiwen Zhang
{"title":"A method of matching strokes based on genetic algorithm","authors":"Hao Bai, Xiwen Zhang","doi":"10.1109/SIPROCESS.2016.7888284","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888284","url":null,"abstract":"It is natural way to write Chinese characters by digital pen for foreign students, whose handwriting information is much richer than digital image. Stroke matching is the prerequisite to analyze handwriting errors of Chinese character. Present research hardly delivers the optimal solution of the problem on the growing sizes and complexity because of wide differences among learners' writing qualities and features. This paper proposes an approach based on genetic algorithm to match strokes. Construction of fitness function considers structural and writing features of Chinese characters. The method can achieve correct matching stroke rate 90.17% at least in the experiments, which indicates that our proposed approach Is effective In next steps of handwriting errors analysis.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131915644","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
Micro-motion dynamic and geometric parameters estimation of exo-atmospheric infrared targets 大气外红外目标的微动动力学及几何参数估计
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888336
Junliang Liu, Shangfeng Chen, Huan-zhang Lu, Bendong Zhao
{"title":"Micro-motion dynamic and geometric parameters estimation of exo-atmospheric infrared targets","authors":"Junliang Liu, Shangfeng Chen, Huan-zhang Lu, Bendong Zhao","doi":"10.1109/SIPROCESS.2016.7888336","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888336","url":null,"abstract":"The motion dynamics and geometric information are considered to be one of the most useful features for infrared (IR) targets recognition. Especially for the exo-atmospheric target, when a target undergoes micro-motion dynamics in the outer space, such as mechanical vibrations or rotations, it would induce amplitude modulations on signature of target projected area along the Line-of-Sight (LOS) of IR detection. The aim of this article is to estimate micro-motion dynamics and geometric parameters from the amplitude signature of target projected area. For that, we introduce a projection model of exo-atmospheric targets, derive formulas of signature induced by targets with spinning, tumbling and coning motion, and estimate related target parameters with heuristic optimization techniques. By analyzing the estimated results, we confirmed the effective-ness of our estimation procedures.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132611617","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
Blind noisy deblurring via hyper laplacian prior and spectral properties of convolution kernel 利用超拉普拉斯先验和卷积核的频谱特性进行盲噪声去模糊
2016 IEEE International Conference on Signal and Image Processing (ICSIP) Pub Date : 2016-08-01 DOI: 10.1109/SIPROCESS.2016.7888289
Yibin Yu, Yinxing Chen, Pengfei Guo, Peng Chen, N. Peng
{"title":"Blind noisy deblurring via hyper laplacian prior and spectral properties of convolution kernel","authors":"Yibin Yu, Yinxing Chen, Pengfei Guo, Peng Chen, N. Peng","doi":"10.1109/SIPROCESS.2016.7888289","DOIUrl":"https://doi.org/10.1109/SIPROCESS.2016.7888289","url":null,"abstract":"Blind deblurring attempts to recover the latent sharp image from a blurred one. Such task is a well-known ill-posed inverse problem and is therefore usually solved as a posteriori probability estimation, incorporating prior information on natural images. In this paper, we propose a general blind noisy deblurring model based on hyper Laplacian (HL) in gradient domain and kernel spectra prior. This model includes the non-convex HL prior term, so we first separate variables and then utilize general soft threshold (GST) and closed-form threshold formulas (CFTF) to solve the proposed model, respectively. Simulation results verify the efficiency and feasibility of the proposed method. The proposed model can be used to solve other problems, such as machine learning and sparse coding.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133392091","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
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