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2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献

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Maritime anomaly detection in ferry tracks 渡轮航道的海上异常探测
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-09 DOI: 10.1109/ICASSP.2017.7952636
Cemre Zor, J. Kittler
{"title":"Maritime anomaly detection in ferry tracks","authors":"Cemre Zor, J. Kittler","doi":"10.1109/ICASSP.2017.7952636","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952636","url":null,"abstract":"This paper proposes a methodology for the automatic detection of anomalous shipping tracks traced by ferries. The approach comprises a set of models as a basis for outlier detection: A Gaussian process (GP) model regresses displacement information collected over time, and a Markov chain based detector makes use of the direction (heading) information. GP regression is performed together with Median Absolute Deviation to account for contaminated training data. The methodology utilizes the coordinates of a given ferry recorded on a second by second basis via Automatic Identification System. Its effectiveness is demonstrated on a dataset collected in the Solent area.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015681","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
Simultaneous coded plane wave imaging in ultrasound: Problem formulation and constraints 超声中的同步编码平面波成像:问题的表述和约束
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-09 DOI: 10.1109/ICASSP.2017.7953359
Bujoreanu Denis, L. Hervé, Nicolas Barbara
{"title":"Simultaneous coded plane wave imaging in ultrasound: Problem formulation and constraints","authors":"Bujoreanu Denis, L. Hervé, Nicolas Barbara","doi":"10.1109/ICASSP.2017.7953359","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7953359","url":null,"abstract":"In this paper, we propose a new emission strategy in the context of plane wave imaging. Plane wave imaging indeed implies compounding in order to preserve a good image quality. Such compounding is usually obtained using multiple, successive emissions, which in turn yields a decrease of the frame rate. As opposed to this approach, our method is based on the simultaneous emission of several coded plane waves. This allows the reconstruction of all the images corresponding to the different plane waves, by using an inverse problem approach. The proposed method is closely related to channel estimation in telecommunications, and coded excitation in synthetic aperture ultrasound imaging. In this paper, we extend these lines of work to the case of ultrasound plane wave imaging and evaluate the obtained performance from various numerical simulations.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124854559","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
Color channel-wise recurrent learning for facial expression recognition 基于颜色通道的面部表情识别循环学习
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-09 DOI: 10.1109/ICASSP.2017.7952353
Jinhyeok Jang, Dae Hoe Kim, Hyungil Kim, Yong Man Ro
{"title":"Color channel-wise recurrent learning for facial expression recognition","authors":"Jinhyeok Jang, Dae Hoe Kim, Hyungil Kim, Yong Man Ro","doi":"10.1109/ICASSP.2017.7952353","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952353","url":null,"abstract":"Facial expression recognition is increasingly gaining importance in emerging affective computing applications. In practice, achieving accurate facial expression recognition is still challenging due to environmental variations. In this paper, we propose a color channel-wise recurrent facial feature learning. The proposed method adopts recurrent neural network to learn expression features sequentially along color channels. The proposed network preserves discriminative expression feature through a long short-term memory for the sequence of color spatial features. Comprehensive experiments have been conducted on the publically available CMU Multi-PIE dataset under illumination variations. Experimental results showed that the proposed method achieved higher recognition rates compared to the state-of-the-art methods.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122225614","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: 7
Distance metric learning for posteriorgram based keyword search 基于后验图的关键字搜索的距离度量学习
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-09 DOI: 10.1109/ICASSP.2017.7953240
Batuhan Gündogdu, M. Saraçlar
{"title":"Distance metric learning for posteriorgram based keyword search","authors":"Batuhan Gündogdu, M. Saraçlar","doi":"10.1109/ICASSP.2017.7953240","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7953240","url":null,"abstract":"In this paper, we propose a neural network based distance metric learning method for a better discrimination in the sequence-matching based keyword search (KWS). In this technique, we conduct a version of Dynamic Time Warping (DTW) based similarity search on the speaker independent posteriorgram space. With this, we aim to compensate for the scarcity of the resources and overcome the out-of-vocabulary (OOV) term problem, which is one of the main issues for KWS on low-resource languages. This distance measure is then used in the DTW-based similarity search, as an alternative and in comparison to the widely and generally used distance metrics. The experiments ran on IARPA Babel Program's Turkish search data show that, the proposed system outperforms the baseline by 6.3% and when combined with the baseline system, the improvement reaches 44.9%.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133663849","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
Scale selective extended local binary pattern for texture classification 用于纹理分类的尺度选择性扩展局部二值模式
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-09 DOI: 10.1109/ICASSP.2017.7952389
Yuting Hu, Z. Long, G. Al-Regib
{"title":"Scale selective extended local binary pattern for texture classification","authors":"Yuting Hu, Z. Long, G. Al-Regib","doi":"10.1109/ICASSP.2017.7952389","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952389","url":null,"abstract":"In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local microand macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126802097","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
Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities 存在稀疏多重散射非线性的快速高光谱解混
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-09 DOI: 10.1109/ICASSP.2017.7952729
Abderrahim Halimi, J. Bioucas-Dias, N. Dobigeon, G. Buller, S. Mclaughlin
{"title":"Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities","authors":"Abderrahim Halimi, J. Bioucas-Dias, N. Dobigeon, G. Buller, S. Mclaughlin","doi":"10.1109/ICASSP.2017.7952729","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952729","url":null,"abstract":"This paper presents a novel nonlinear hyperspectral mixture model and its associated supervised unmixing algorithm. The model assumes a linear mixing model corrupted by an additive term which accounts for multiple scattering nonlinearities (NL). The proposed model generalizes bilinear models by taking into account higher order interaction terms. The inference of the abundances and nonlinearity coefficients of this model is formulated as a convex optimization problem suitable for fast estimation algorithms. This formulation accounts for constraints such as the sum-to-one and nonnegativity of the abundances, the non-negativity of the nonlinearity coefficients, and the spatial sparseness of the residuals. The resulting convex problem is solved using the alternating direction method of multipliers (ADMM) whose convergence is ensured theoretically. The proposed mixture model and its unmixing algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inference and the computational complexity when compared to the state-of-the-art algorithms.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131418834","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
Secure genomic susceptibility testing based on lattice encryption 基于点阵加密的安全基因组易感性检测
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-08 DOI: 10.1109/ICASSP.2017.7952520
J. Troncoso-Pastoriza, A. Pedrouzo-Ulloa, F. Pérez-González
{"title":"Secure genomic susceptibility testing based on lattice encryption","authors":"J. Troncoso-Pastoriza, A. Pedrouzo-Ulloa, F. Pérez-González","doi":"10.1109/ICASSP.2017.7952520","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952520","url":null,"abstract":"Recent advances in Next Generation Sequencing have increased the availability of genomic data for more accurate analyses, like testing for the genetic susceptibility to a disease. Current laboratories' facilities cannot cope with this data growth, and genomic processing needs to be outsourced, comprising serious privacy risks. This work proposes an encrypted genomic susceptibility test protocol based on lattice homomorphic cryptosystems, and introduces optimizations like data packing and transformed processing to achieve considerable gains in performance, bandwidth and storage needs.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124574350","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
Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features 基于深度模型迁移学习和混合特征的超声图像甲状腺结节分类
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-08 DOI: 10.1109/ICASSP.2017.7952290
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun
{"title":"Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features","authors":"Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun","doi":"10.1109/ICASSP.2017.7952290","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952290","url":null,"abstract":"Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification. Firstly, a CNN model trained with a massive natural dataset is transferred to the ultrasound image domain, to generate semantic deep features and handle the small sample problem. Then, we combine those deep features with conventional features such as Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) together, to form a hybrid feature space. Finally, a positive-samplefirst majority voting and a feature-selected based strategy are employed for the hybrid classification. Experimental results on 1037 images show that the accuracy of our proposed method is 0.931, which outperformed other relative methods by over 10%.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124219428","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: 86
Blind image deblurring based on sparse representation and structural self-similarity 基于稀疏表示和结构自相似性的盲图像去模糊
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-08 DOI: 10.1109/ICASSP.2017.7952372
Jing Yu, Zhenchun Chang, Chuangbai Xiao, Weidong Sun
{"title":"Blind image deblurring based on sparse representation and structural self-similarity","authors":"Jing Yu, Zhenchun Chang, Chuangbai Xiao, Weidong Sun","doi":"10.1109/ICASSP.2017.7952372","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952372","url":null,"abstract":"In this paper, we propose a blind motion deblurring method based on sparse representation and structural self-similarity from a single image. The priors for sparse representation and structural self-similarity are explicitly added into the recovery of the latent image by means of sparse and multi-scale nonlocal regularizations, and the down-sampled version of the observed blurry image is used as training samples in the dictionary learning for sparse representation so that the sparsity of the latent image over this dictionary can be guaranteed, which implicitly makes use of multi-scale similar structures. Experimental results on both simulated and real blurry images demonstrate that our method outperforms existing state-of-the-art blind deblurring methods.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128605782","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
Part-level fully convolutional networks for pedestrian detection 行人检测的部分级全卷积网络
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2017-03-08 DOI: 10.1109/ICASSP.2017.7952560
Xinran Wang, Cheolkon Jung, A. Hero
{"title":"Part-level fully convolutional networks for pedestrian detection","authors":"Xinran Wang, Cheolkon Jung, A. Hero","doi":"10.1109/ICASSP.2017.7952560","DOIUrl":"https://doi.org/10.1109/ICASSP.2017.7952560","url":null,"abstract":"Since pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, pedestrian detection is a challengeable task. In this paper, we propose part-level fully convolutional networks (FCN) for pedestrian detection. We adopt deep learning to deal with the proposal shifting problem in pedestrian detection. First, we combine convolutional neural networks (CNN) and FCN to align bounding boxes for pedestrians. Then, we perform part-level pedestrian detection based on CNN to recall the lost body parts. Experimental results demonstrate that the proposed method achieves 6.83% performance improvement in log-average miss rate over CifarNet.","PeriodicalId":118243,"journal":{"name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114841968","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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