2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第4页

Single Image 3D Vehicle Pose Estimation for Augmented Reality 增强现实单图像三维车辆姿态估计

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969201

Yawen Lu, Sophia Kourian, C. Salvaggio, Chenliang Xu, G. Lu

{"title":"Single Image 3D Vehicle Pose Estimation for Augmented Reality","authors":"Yawen Lu, Sophia Kourian, C. Salvaggio, Chenliang Xu, G. Lu","doi":"10.1109/GlobalSIP45357.2019.8969201","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969201","url":null,"abstract":"The intent of this paper is to introduce a novel method for 3D vehicle pose estimation, a critical component of augmented reality. The proposed method is able to recover the location of a specific object from a single image by combining pretrained reliable semantic segmentation and improved single image depth estimation. Our method exploits a novel pose estimation technique by generating new 2D images created from the projections of rotated point clouds. The rotation of the specific object is able to be predicted. Augmented objects can be shifted, rotated and scaled correctly based on the recovered orientation and localization. Through accurate vehicle pose estimation, virtual vehicles are able to be augmented accurately in place of real vehicles. The effectiveness of our method is verified by comparison with other recent pose estimation methods on the challenging KITTI 3D benchmark. Further experiments on the Cityscapes dataset also demonstrates good robustness in the method. Without requiring ground truth 3D vehicle pose labels for training, our model is able to produce competitive and robust performance in 3D vehicle pose estimation.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714131","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

Radar as a Security Measure - Real Time Neural Model based Human Detection and Behaviour Classification 雷达作为一种安全措施——基于实时神经模型的人体检测和行为分类

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969161

Prakhar Kaushik

引用次数: 3

3-D MIMO-SAR Imaging Using Multi-Chip Cascaded Millimeter-Wave Sensors 基于多芯片级联毫米波传感器的三维MIMO-SAR成像

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969133

Muhammet Emin Yanik, Dan Wang, M. Torlak

引用次数: 32

A Divide-and-Conquer Framework for Attention-based Combination of Multiple Investment Strategies 基于注意的多投资策略组合的分而治之框架

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969091

Xiao Yang, Weiqing Liu, Lewen Wang, Cheng Qu, Jiang Bian

{"title":"A Divide-and-Conquer Framework for Attention-based Combination of Multiple Investment Strategies","authors":"Xiao Yang, Weiqing Liu, Lewen Wang, Cheng Qu, Jiang Bian","doi":"10.1109/GlobalSIP45357.2019.8969091","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969091","url":null,"abstract":"In order to maximize the profit, investors usually examine diverse investment strategies based on various information when constructing their portfolios. However, they can hardly always construct a profitable portfolio due to the dynamic performance of the strategies and the dynamics of the market state along with the time. To address this challenge, we propose a 2D-attention framework to capture the dynamics of the above two factors in this paper. To capture the dynamic of the first factor, we design a strategy-wise attention model to dynamically combine multiple strategies according to their respective effectiveness. To deal with the second factor, we design a divide-and-conquer framework to learn multiple strategy-wise attention models, which categorizes the whole market periods into a few stable states and jointly learn respective models for each state and then build a state-wise attention model to combine them dynamically for the final task. Extensive experiments on real-world data demonstrate that our 2D-attention framework can significantly outperform several widely-used baselines.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121669811","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

On Theoretical Optimization of the Sensing Matrix for Sparse-Dictionary Signal Recovery 稀疏字典信号恢复传感矩阵的理论优化

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969228

Jianchen Zhu, Shengjie Zhao, Xu Ma, G. Arce

引用次数: 1

Power System Dynamic State Estimation Using Smooth Variable Structure Filter 基于光滑变结构滤波器的电力系统动态估计

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969306

Ibrahim Al-Omari, A. Rahimnejad, S. Gadsden, M. Moussa, H. Karimipour

引用次数: 5

Bottom-Up Unsupervised Word Discovery via Acoustic Units 基于声学单元的自下而上无监督词发现

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969225

Saurabhchand Bhati, Chunxi Liu, J. Villalba, J. Trmal, S. Khudanpur, N. Dehak

{"title":"Bottom-Up Unsupervised Word Discovery via Acoustic Units","authors":"Saurabhchand Bhati, Chunxi Liu, J. Villalba, J. Trmal, S. Khudanpur, N. Dehak","doi":"10.1109/GlobalSIP45357.2019.8969225","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969225","url":null,"abstract":"Unsupervised term discovery is the task of identifying and grouping reoccurring word-like patterns from the untranscribed audio data. It facilitates unsupervised acoustic model training in zero resource setting where no or minimal transcribed speech is available. In this paper, we investigate two-step bottom-up approaches for unsupervised discovery of word-like units. The first step discovers phone-like acoustic units from data and the second step combines the basic acoustic blocks to identify word-like units. We investigated Embedded Segmental K-means and Nested Hierarchical Pitman-Yor (PYR) model as bottom-up strategies. ESK-Means iteratively selects boundaries from an initial set to arrive at the word boundaries. The final performance critically depends on the quality of the initial boundaries. We used a segmentation method that discovers boundaries much closer to actual boundaries. PYR model has been used for word segmentation from space removed text data, and here we use it for word discovery from unsupervised acoustic units. The term discovery performance is evaluated on the Zero Resource 2017 challenge dataset, which consists of around 70 hours of unlabelled data. Our systems outperformed the baseline systems on all the languages without language-specific parameter tuning. We performed comprehensive experiments of the system parameters on the system performance.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283155","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

Classifying Melanoma and Seborrheic Keratosis Automatically with Polarization Speckle Imaging 偏振散斑成像自动分类黑色素瘤和脂溢性角化病

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969331

Yuheng Wang, Jiayue Cai, Daniel C. Louie, H. Lui, Tim K. Lee, Z. J. Wang

{"title":"Classifying Melanoma and Seborrheic Keratosis Automatically with Polarization Speckle Imaging","authors":"Yuheng Wang, Jiayue Cai, Daniel C. Louie, H. Lui, Tim K. Lee, Z. J. Wang","doi":"10.1109/GlobalSIP45357.2019.8969331","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969331","url":null,"abstract":"Skin cancer is the most common cancer in western countries with a high incidence rate. Among all different types of skin cancers, malignant melanoma is the most fatal but has a promising prognosis if it is detected and treated at the early stages. However, melanoma often resemble to seborrheic keratosis (SK), a benign skin condition, and cause mis-diagnosis. Therefore, it is important to develop a framework with computer aided system and non-invasive techniques to assist in the clinical diagnosis of melanoma. In this study, we extend a recent polarization speckle imaging method based on depolarization rate and achieved automatic detection of melanoma by leveraging the power of machine learning strategies. We collected 143 malignant melanoma and seborrheic keratosis lesions. Different machine learning methods, including support vector machine, random forest and k-nearest neighbor, were employed for the classification between melanoma and seborrheic keratosis. In order to explore the impact of different light sources, we further compared the classification performance of depolarization rate with blue and red light sources using different classifiers. The results suggested that the most reliable classification performance was achieved by support vector machine, yielding a high accuracy of 86.31% and the most balanced performance between sensitivity and specificity. In addition, the depolarization rate with the blue light source demonstrated a consistently better performance than that with the red light source across different methods. Our promising classification performance shows evidence for the potentials of computer aided diagnosis of melanoma with polarization speckle imaging, providing an additional non-invasive in vivo tool for skin cancer detection which could benefit future clinical dermatology research.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127681804","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

Deep Learning Methods for Image Segmentation Containing Translucent Overlapped Objects 包含半透明重叠对象的图像分割的深度学习方法

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969558

Tayebeh Lotfi Mahyari, R. Dansereau

引用次数: 2

Robust Minimum Variance Distortionless Response Beamformer based on Target Activity Detection in Binaural Hearing Aid Applications 基于目标活动检测的鲁棒最小方差无失真响应波束形成器在双耳助听器中的应用

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969387

Hala As’ad, M. Bouchard, Homayoun Kamkar-Parsi

{"title":"Robust Minimum Variance Distortionless Response Beamformer based on Target Activity Detection in Binaural Hearing Aid Applications","authors":"Hala As’ad, M. Bouchard, Homayoun Kamkar-Parsi","doi":"10.1109/GlobalSIP45357.2019.8969387","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969387","url":null,"abstract":"In this work, a new beamforming algorithm for binaural hearing aids is introduced, with a good robustness to errors in the estimated target direction of arrival. The proposed design is based on a selection between a monaural MVDR beamformer and a binaural MVDR beamformer at each time-frequency bin. The decision of using either the monaural MVDR or the binaural MVDR at each time-frequency bin depends on a proposed target activity detection system, over an a priori determined target zone. The proposed target activity detection system uses a binaural beamforming method in target-cancelling mode, in order to get an estimate of the dominant speaker direction at each time-frequency bin. Simulations are performed using signals and propagation models obtained from real-life multichannel binaural hearing aids, to evaluate the resulting noise reduction and target distortion. Simulation results show the robustness of the proposed design to errors in the estimated target direction of arrival, in comparison with both the monaural MVDR and binaural MVDR algorithms.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133353327","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