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2016 IEEE International Conference on Digital Signal Processing (DSP)最新文献

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Deep learning-based learning to rank with ties for image re-ranking 基于深度学习的基于关系的学习排序，用于图像重新排序
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868598
Piplong Zhao, Ou Wu, L. Guo, Weiming Hu, Jinfeng Yang
{"title":"Deep learning-based learning to rank with ties for image re-ranking","authors":"Piplong Zhao, Ou Wu, L. Guo, Weiming Hu, Jinfeng Yang","doi":"10.1109/ICDSP.2016.7868598","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868598","url":null,"abstract":"In existing learning to rank problems, the learned ranking function sorts objects according to their predicted scores. Therefore, a full-ordering object list is obtained even if two or more objects have almost identical degrees of relevance (or called objects with ties). For objects containing ties, a more reasonable ranking approach is to learn a ranking function which can judge both the preference and ties relationships among objects. In this paper, we propose a new pairwise ranking algorithm and apply it to image re-ranking. Specifically, we utilize deep learning to re-rank images based on a new loss function. The ties-relationship is considered in both training and testing process. As a result, the learned ranking function can be used to rank objects containing ties. The experimental results demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124141782","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 multiplierless cosine modulated filterbank using hybrid technique in sub-expression space 基于子表达式空间混合技术的无乘法器余弦调制滤波器组设计
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868579
I. Sharma, Anil Kumar, G. Singh, Heung-no Lee
{"title":"Design of multiplierless cosine modulated filterbank using hybrid technique in sub-expression space","authors":"I. Sharma, Anil Kumar, G. Singh, Heung-no Lee","doi":"10.1109/ICDSP.2016.7868579","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868579","url":null,"abstract":"In this paper, an optimal design technique for M-channel multiplierless cosine modulated filterbank (CMFB) is proposed using common sub-expression technique (CSE) and hybrid method with given roll-off factor (RF) and stopband attenuation (AS). The key feature of the proposed method is utilization of single optimization algorithm to generate optimal quantized and canonic signed digit (CSD) converted coefficients that satisfy the magnitude response of 0.7071 at frequency ω= π /2M. CSE is employed to reduce the hardware requirement (adders) of a designed filter. Hybrid technique is based on the concept of particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm. A comparative analysis of different CSE algorithms has been made, and performance of the proposed method is evaluated in term of adders.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115922368","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
Detection & classification of imperceptible motion using video decomposition 基于视频分解的微小运动检测与分类
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868565
Saumik Bhattacharya, K. S. Venkatsh, Sumana Gupta
{"title":"Detection & classification of imperceptible motion using video decomposition","authors":"Saumik Bhattacharya, K. S. Venkatsh, Sumana Gupta","doi":"10.1109/ICDSP.2016.7868565","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868565","url":null,"abstract":"As human vision system (HVS) is highly sensitive to motion, motion saliency is an important field of research in video signal processing. But, HVS is particularly insensitive to subtle motions with low amplitude. Though, in many practical fields, e.g., biomedical science, earth science, plasma science etc., these low amplitude motions are significant for predicting certain crucial events, most of the signal processing methods fail to analyze them as they are difficult to detect in natural scenes. Thus, a specialized manual intervention is generally required to analyze these data. The situation worsens in presence of noise, inherent to any imaging system, as it is difficult to distinguish imperceptible motions in noisy environment. In this paper we propose a robust method to detect and classify imperceptible motion in a video sequence. The proposed algorithm exploits a total variation (TV) based video decomposition to detect the motion in a scene and detected motion is classified by training a support vector machines (SVM) after the detection. This classification of subtle motion can be used in several areas for diagnosing abnormalities.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130685913","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
Path voting based pavement crack detection from laser range images 基于路径投票的激光测距路面裂缝检测
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868594
Qin Zou, Qingquan Li, Fan Zhang, Zhimin Xiong, Qian Wang
{"title":"Path voting based pavement crack detection from laser range images","authors":"Qin Zou, Qingquan Li, Fan Zhang, Zhimin Xiong, Qian Wang","doi":"10.1109/ICDSP.2016.7868594","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868594","url":null,"abstract":"Due to illumination variations, cast shadows, and pavement stains, etc., traditional optical imaging has limitations in capturing and representing pavement cracks. In this work, laser imaging techniques are used to model the pavement surface with point clouds, where crack points hold relatively lower range values than their non-crack neighbors. To extract cracks from laser range images, a two-level grouping approach is proposed. First, local grouping is performed by a novel segmentation-based path voting algorithm. The proposed path voting is equipped with an adapted normalized-cut algorithm which purposely bi-partitions an image patch along the potential crack path. Then in a global grouping, crack seeds are sampled and fed into a graph representation, in which spanning tree and tree pruning algorithms are employed to extract the final cracks in a global view. Experimental results demonstrate the effectiveness of the proposed approach.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127070027","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: 12
Hybrid framework for image denoising with patch prior estimation 基于补丁先验估计的图像去噪混合框架
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868597
Ying Chen, Yibin Tang, Lin Zhou, A. Jiang, N. Xu
{"title":"Hybrid framework for image denoising with patch prior estimation","authors":"Ying Chen, Yibin Tang, Lin Zhou, A. Jiang, N. Xu","doi":"10.1109/ICDSP.2016.7868597","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868597","url":null,"abstract":"In this paper, a hybrid framework is proposed for image denoising, in which several state-of-the-art denoising methods are efficiently incorporated with a well trade-off by using the prior of patches. In detail, unlike modeling patches with the prior in existed denoising methods, the prior estimation here is presented only to detect the attributes of patches. Then, noisy patches are clustered into several categories according to their patch attributes. Sequentially, different denoising methods are adopted on patches of different categories. The restored image is finally synthesized with the denoised patches of all categories. Experiments show that, by using the hybrid framework, the proposed algorithm is insensitive to the variation of the attributes of images, and can robustly restore images with a remarkable denoising performance.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123304009","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
Energy-efficient massive MIMO system analysis: From a circuit power perspective 节能大规模MIMO系统分析:从电路功率的角度
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868577
Zhenbing Zhang, Jienan Chen, Jianhao Hu
{"title":"Energy-efficient massive MIMO system analysis: From a circuit power perspective","authors":"Zhenbing Zhang, Jienan Chen, Jianhao Hu","doi":"10.1109/ICDSP.2016.7868577","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868577","url":null,"abstract":"In this paper, we present an analysis of the energy-efficiency (EE) of massive multiple-input multiple-output (MIMO) systems from a circuit power perspective. We first study the power consumption of a CMOS technology related to processing cycles and logic area. Based on the power consumption model, we formulate the power equation of an Orthogonal Frequency Division Multiplexing (OFDM)-MIMO system related to design parameters. The close form of an EE equation is obtained by the ratio between total transmission bits and the energy consumption with unit Mbits/Joule. Compared with existing analysis work, the proposed model is related directly to the CMOS circuit model which is believed more accurate.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126626642","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
Adaptive shrinkage cascades for blind image deconvolution 盲图像反卷积的自适应收缩级联
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868622
Xuejian Rong, Yingli Tian
{"title":"Adaptive shrinkage cascades for blind image deconvolution","authors":"Xuejian Rong, Yingli Tian","doi":"10.1109/ICDSP.2016.7868622","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868622","url":null,"abstract":"Recently emerged discriminative non-blind deconvolution methods achieve excellent performance with only a fraction of computation cost w.r.t. generative competitors, but their extension to blind deconvolution field was seldom addressed in a practical manner, albeit equally vital in image restoration area. We propose a novel framework for effective blind image deblurring by patch-wise prior based adaptive shrinkage cascades, which introduces the powerful internal patch-based image statistics to the non-blind shrinkage field formulations. The rich expressiveness of internal patch prior brings instance-specific adaptivity to alternating kernel refinement between neighboring shrinkage cascades, while shrinkage model trained from varieties of natural image collections benefits internal patch-wise prior inference with external information and superior efficiency.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881570","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
Fast iterative reweighted least squares algorithm for sparse signals recovery 稀疏信号恢复的快速迭代重加权最小二乘算法
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868547
Xinyue Zhang, Xudong Zhang, Bin Zhou
{"title":"Fast iterative reweighted least squares algorithm for sparse signals recovery","authors":"Xinyue Zhang, Xudong Zhang, Bin Zhou","doi":"10.1109/ICDSP.2016.7868547","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868547","url":null,"abstract":"Iterative Re-weighted Least Squares (IRLS) is an effective recovery algorithm for compressed sensing (CS). However, it suffers from a large computational load for the recovery of high dimensional sparse signals due to the repeated multiplication and inversion of large matrices. This paper proposes a fast IRLS algorithm. In this algorithm, the signal weights in each iteration are computed based on the result from the current iteration, simplifying the calculation of weights and avoiding repeated multiplication and inversion of large matrices in each iteration. The fast IRLS algorithm is more efficient than the original IRLS, especially for the high dimensional sparse signals recovery. Finally, some experiments are provided to illustrate the effectiveness of the proposed algorithm.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633650","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
Localization of sensor networks via low rank approximation 基于低秩逼近的传感器网络定位
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868567
Yanping Zhu, A. Jiang, H. Kwan
{"title":"Localization of sensor networks via low rank approximation","authors":"Yanping Zhu, A. Jiang, H. Kwan","doi":"10.1109/ICDSP.2016.7868567","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868567","url":null,"abstract":"In this paper, a novel algorithm is proposed for locating sensor networks. In general, Euclidean distance matrices are incomplete due to their limited communication power. Furthermore, distance measurements are contaminated by noise. For the purpose of localization, unknown distances are first estimated via low rank approximation. Relative coordinates of sensors are then obtained by eigenvalue decomposition of the Gram matrix, which is constructed by the Euclidean distance matrix estimated. To improve the localization accuracy, subnetworks are constructed by each sensor and its neighbors. Since neighboring sensors of each sensor are more prone to communicate with each other, the local Euclidean distance matrix could be denser than the global one, leading to a more accurate estimate. Another advantage of the proposed algorithm is that it can be implemented in a distributed manner, which is desirable for sensor networks without central computational unit. Two sets of simulations demonstrate that the proposed algorithm can achieve better localization accuracy than other localization algorithms using global Euclidean distance matrices.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121973012","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
Iteratively reweighted optimum linear regression in the presence of generalized Gaussian noise 广义高斯噪声下的迭代重加权最优线性回归
2016 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2016-10-01 DOI: 10.1109/ICDSP.2016.7868640
Fuxi Wen, W. Liu
{"title":"Iteratively reweighted optimum linear regression in the presence of generalized Gaussian noise","authors":"Fuxi Wen, W. Liu","doi":"10.1109/ICDSP.2016.7868640","DOIUrl":"https://doi.org/10.1109/ICDSP.2016.7868640","url":null,"abstract":"Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. In this paper, we consider the linear regression problem in the presence of GGD noise employing the iteratively reweighted least squares (IRLS) algorithm. For the standard IRLS algorithm, an ℓp-norm minimization problem is solved iteratively. However, its performance depends on a properly chosen norm parameter p. To solve this problem, we propose a modified IRLS algorithm with a variable p, which is noise distribution dependent and can be updated online. Numerical studies show that the proposed method can normally converge within a few iterations. Furthermore, optimal performance is achieved in terms of normalized mean square error for different GGD noise models.","PeriodicalId":206199,"journal":{"name":"2016 IEEE International Conference on Digital Signal Processing (DSP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124399640","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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