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Ship detection using SAR and AIS raw data for maritime surveillance 利用SAR和AIS原始数据进行海上监视的船舶探测
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760615
F. M. Vieira, F. Vincent, J. Tourneret, D. Bonacci, M. Spigai, M. Ansart, J. Richard
{"title":"Ship detection using SAR and AIS raw data for maritime surveillance","authors":"F. M. Vieira, F. Vincent, J. Tourneret, D. Bonacci, M. Spigai, M. Ansart, J. Richard","doi":"10.1109/EUSIPCO.2016.7760615","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760615","url":null,"abstract":"This paper studies a maritime vessel detection method based on the fusion of data obtained from two different sensors, namely a synthetic aperture radar (SAR) and an automatic identification system (AIS) embedded in a satellite. Contrary to most methods widely used in the literature, the present work proposes to jointly exploit information from SAR and AIS raw data in order to detect the absence or presence of a ship using a binary hypothesis testing problem. This detection problem is handled by a generalized likelihood ratio detector whose test statistics has a simple closed form expression. The distribution of the test statistics is derived under both hypotheses, allowing the corresponding receiver operational characteristics (ROCs) to be computed. The ROCs are then used to compare the detection performance obtained with different sensors showing the interest of combining information from AIS and radar.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411172","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: 17
Real-time UHD scalable multi-layer HEVC encoder architecture 实时超高清可扩展多层HEVC编码器架构
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760458
Ronan Parois, W. Hamidouche, E. Mora, M. Raulet, O. Déforges
{"title":"Real-time UHD scalable multi-layer HEVC encoder architecture","authors":"Ronan Parois, W. Hamidouche, E. Mora, M. Raulet, O. Déforges","doi":"10.1109/EUSIPCO.2016.7760458","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760458","url":null,"abstract":"The High Efficiency Video Coding (HEVC) standard enables meeting new video quality demands such as Ultra High Definition (UHD). Its scalable extension (SHVC) allows encoding simultaneously different versions of a video, organised in layers. Thanks to inter-layer predictions, SHVC provides bit-rate savings over an equivalent HEVC simulcast encoding. Therefore, SHVC seems a promising solution for both broadcast and storage purposes. This paper proposes a multi-layer architecture of a pipeline of software HEVC encoder to perform real-time UHD spatially-scalable SHVC encoding. Inter-layer predictions are furthermore implemented to provide bit-rate savings with a minimum impact on complexity. The proposed architecture provides a good trade-off between coding gains and coding speed achieving real-time performance for 1080p60 and 1600p30 sequences in 2× spatial scalability. Moreover, experimental results show more than a 1000× speed-up compared to the SHVC reference software (SHM) and an introduced delay only reaching 14% of the equivalent HEVC coding speed.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324608","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
Rectified binaural ratio: A complex T-distributed feature for robust sound localization 校正双耳比:用于稳健声音定位的复杂t分布特征
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760450
Antoine Deleforge, F. Forbes
{"title":"Rectified binaural ratio: A complex T-distributed feature for robust sound localization","authors":"Antoine Deleforge, F. Forbes","doi":"10.1109/EUSIPCO.2016.7760450","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760450","url":null,"abstract":"Most existing methods in binaural sound source localization rely on some kind of aggregation of phase- and level-difference cues in the time-frequency plane. While different aggregation schemes exist, they are often heuristic and suffer in adverse noise conditions. In this paper, we introduce the rectified binaural ratio as a new feature for sound source localization. We show that for Gaussian-process point source signals corrupted by stationary Gaussian noise, this ratio follows a complex t-distribution with explicit parameters. This new formulation provides a principled and statistically sound way to aggregate binaural features in the presence of noise. We subsequently derive two simple and efficient methods for robust relative transfer function and time-delay estimation. Experiments on heavily corrupted simulated and speech signals demonstrate the robustness of the proposed scheme.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127404910","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
Human expert supervised selection of time-frequency intervals in EEG signals for brain-Computer interfacing 人类专家监督选择脑电信号的时频区间用于脑机接口
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760545
Alban Duprès, F. Cabestaing, J. Rouillard
{"title":"Human expert supervised selection of time-frequency intervals in EEG signals for brain-Computer interfacing","authors":"Alban Duprès, F. Cabestaing, J. Rouillard","doi":"10.1109/EUSIPCO.2016.7760545","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760545","url":null,"abstract":"In the context of brain-computer interfacing based on motor imagery, we propose a method allowing a human expert to supervise the selection of user-specific time-frequency features computed from EEG signals. Indeed, in the current state of BCI research, there is always at least one expert involved in the first stages of any experimentation. On one hand, such experts really appreciate keeping a certain level of control on the tuning of user-specific parameters. On the other hand, we will show that their knowledge is extremely valuable for selecting a sparse set of significant time-frequency features. The expert selects these features through a visual analysis of curves highlighting differences between electroencephalographic activities recorded during the execution of various motor imagery tasks. We compare our method to the basic common spatial patterns approach and to two fully-automatic feature extraction methods, using dataset 2A of BCI competition IV. Our method (mean accuracy m = 83.71 ± 14.6 std) outperforms the best competing method (m = 79.48 ± 12.41 std) for 6 of the 9 subjects.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130890717","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
Mining the bilinear structure of data with approximate joint diagonalization 用近似联合对角化方法挖掘数据的双线性结构
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760332
Louis Korczowski, Florent Bouchard, C. Jutten, M. Congedo
{"title":"Mining the bilinear structure of data with approximate joint diagonalization","authors":"Louis Korczowski, Florent Bouchard, C. Jutten, M. Congedo","doi":"10.1109/EUSIPCO.2016.7760332","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760332","url":null,"abstract":"Approximate Joint Diagonalization of a matrix set can solve the linear Blind Source Separation problem. If the data possesses a bilinear structure, for example a spatio-temporal structure, transformations such as tensor decomposition can be applied. In this paper we show how the linear and bilinear joint diagonalization can be applied for extracting sources according to a composite model where some of the sources have a linear structure and other a bilinear structure. This is the case of Event Related Potentials (ERPs). The proposed model achieves higher performance in term of shape and robustness for the estimation of ERP sources in a Brain Computer Interface experiment.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131781303","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
TDOA-based self-calibration of dual-microphone arrays 基于tdoa的双传声器阵列自校准
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760322
M. Farmani, R. Heusdens, M. Pedersen, J. Jensen
{"title":"TDOA-based self-calibration of dual-microphone arrays","authors":"M. Farmani, R. Heusdens, M. Pedersen, J. Jensen","doi":"10.1109/EUSIPCO.2016.7760322","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760322","url":null,"abstract":"We consider the problem of determining the relative position of dual-microphone sub-arrays. The proposed solution is mainly developed for binaural hearing aid systems (HASs), where each hearing aid (HA) in the HAS has two microphones at a known distance from each other. However, the proposed algorithm can effortlessly be applied to acoustic sensor network applications. In contrast to most state-of-the-art calibration algorithms, which model the calibration problem as a non-linear problem resulting in high computational complexity, we model the calibration problem as a simple linear system of equations by utilizing a far-field assumption. The proposed model is based on target signals time-difference-of-arrivals (TDOAs) between the HAS microphones. Working with TDOAs avoids clock synchronization between sound sources and microphones, and target signals need not be known beforehand. To solve the calibration problem, we propose a least squares estimator which is simple and does not need any probabilistic assumptions about the observed signals.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127308433","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
Nonlinear blind source separation for sparse sources 稀疏源的非线性盲源分离
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760515
Bahram Ehsandoust, B. Rivet, C. Jutten, M. Babaie-zadeh
{"title":"Nonlinear blind source separation for sparse sources","authors":"Bahram Ehsandoust, B. Rivet, C. Jutten, M. Babaie-zadeh","doi":"10.1109/EUSIPCO.2016.7760515","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760515","url":null,"abstract":"Blind Source Separation (BSS) is the problem of separating signals which are mixed through an unknown function from a number of observations, without any information about the mixing model. Although it has been mathematically proven that the separation can be done when the mixture is linear, there is not any proof for the separability of nonlinearly mixed signals. Our contribution in this paper is performing nonlinear BSS for sparse sources. It is shown in this case, sources are separable even if the problem is under-determined (the number of observations is less than the number of source signals). However in the most general case (when the nonlinear mixing model can be of any kind and there is no side-information about that), an unknown nonlinear transformation of each source is reconstructed. It is shown why the problem reconstructing the exact sources is severely ill-posed and impossible to do without any other information.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123066491","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: 9
An exemplar-based hidden Markov model framework for nonlinear state-space models 非线性状态空间模型的基于示例的隐马尔可夫模型框架
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760667
Redouane Lguensat, Ronan Fablet, P. Ailliot, P. Tandeo
{"title":"An exemplar-based hidden Markov model framework for nonlinear state-space models","authors":"Redouane Lguensat, Ronan Fablet, P. Ailliot, P. Tandeo","doi":"10.1109/EUSIPCO.2016.7760667","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760667","url":null,"abstract":"In this work we present a data-driven method for the reconstruction of dynamical systems from noisy and incomplete observation sequences. The key idea is to benefit from the availability of representative datasets of trajectories of the system of interest. These datasets provide an implicit representation of the dynamics of this system, in contrast to the explicit knowledge of the dynamical model. This data-driven strategy is of particular interest in a large variety of situations, e.g., modeling uncertainties and inconsistencies, unknown explicit models, computationally demanding models, etc. We address this exemplar-based reconstruction issue using a Hidden Markov Model (HMM) and we illustrate the relevance of the method for missing data interpolation issues in multivariate time series. As such, our contribution opens new research avenues for a variety of application domains to exploit the wealth of archived observation and simulation data, aiming a better analysis and reconstruction of dynamical systems using past and future observation sequences.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123295359","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
Sequential stack decoder for multichannel image restoration 顺序堆栈解码器的多通道图像恢复
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760457
Fouad Boudjenouia, R. Jennane, K. Abed-Meraim, A. Chetouani
{"title":"Sequential stack decoder for multichannel image restoration","authors":"Fouad Boudjenouia, R. Jennane, K. Abed-Meraim, A. Chetouani","doi":"10.1109/EUSIPCO.2016.7760457","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760457","url":null,"abstract":"In this paper, we propose a novel scheme for image restoration (IR) employing a sequential decoding technique based on a tree search, known as Stack algorithm. The latter is a well-known method used for 1D signal decoding in wireless communication systems. The main idea is to extend the Stack algorithm for image restoration (2D) and to exploit the information diversity conveyed by the channels (Multichannel) in order to restore the original image. To deal with the noisy case, a regularization term is introduced using the total variation and the wavelet transform. This method was tested on artificially degraded images (blurred and noisy). Obtained results confirm the relevance of the proposed approach.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132588090","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
Fast and robust detection of a known pattern in an image 快速和鲁棒检测图像中的已知模式
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-29 DOI: 10.1109/EUSIPCO.2016.7760640
L. Denis, A. Ferrari, D. Mary, L. Mugnier, É. Thiébaut
{"title":"Fast and robust detection of a known pattern in an image","authors":"L. Denis, A. Ferrari, D. Mary, L. Mugnier, É. Thiébaut","doi":"10.1109/EUSIPCO.2016.7760640","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760640","url":null,"abstract":"Many image processing applications require to detect a known pattern buried under noise. While maximum correlation can be implemented efficiently using fast Fourier transforms, detection criteria that are robust to the presence of outliers are typically slower by several orders of magnitude. We derive the general expression of a robust detection criterion based on the theory of locally optimal detectors. The expression of the criterion is attractive because it offers a fast implementation based on correlations. Application of this criterion to Cauchy likelihood gives good detection performance in the presence of outliers, as shown in our numerical experiments. Special attention is given to proper normalization of the criterion in order to account for truncation at the image borders and noise with a non-stationary dispersion.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121216814","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
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