2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第3页

LEARNED MIXED MATERIAL MODELS FOR EFFICIENT CLUSTERING BASED DUAL-ENERGY CT IMAGE DECOMPOSITION 学习了基于双能ct图像高效聚类的混合材料模型

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646635

Zhipeng Li, S. Ravishankar, Y. Long, J. Fessler

{"title":"LEARNED MIXED MATERIAL MODELS FOR EFFICIENT CLUSTERING BASED DUAL-ENERGY CT IMAGE DECOMPOSITION","authors":"Zhipeng Li, S. Ravishankar, Y. Long, J. Fessler","doi":"10.1109/GlobalSIP.2018.8646635","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646635","url":null,"abstract":"Penalized weight-least squares (PWLS) with basis material priors is a promising way to achieve high quality material decompositions for Dual-energy CT (DECT). This paper proposes a new method dubbed DECT-MULTRA for image domain DECT material decomposition that combines conventional PWLS estimation with regular-ization based on a mixed union of learned transforms (MULTRA) model. Our approach pre-learns from training data a common union of unitary transforms for all the basis materials’ patches, as well as a cross-material union of unitary transforms that captures relationships between the different basis material images. The proposed DECT-MULTRA algorithm efficiently obtains material decompositions by alternating between updating the material images and performing clustering of patches in the MULTRA model. Both these steps of the alternating algorithm have closed-form updates. Numerical experiments with the XCAT phantom show that the proposed method significantly improves image quality compared to the recent DECT-ST method that learns different sparsifying transforms for different basis materials and the DECT-EP approach that uses a non-adaptive edge-preserving hyperbola regularizer.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134400714","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

Coarse-To-Fine Segmentation Refinement and Missing Shape Recovery for Halibut Fish 大比目鱼粗-细分割细化及缺失形状恢复

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646442

Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose

{"title":"Coarse-To-Fine Segmentation Refinement and Missing Shape Recovery for Halibut Fish","authors":"Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose","doi":"10.1109/GlobalSIP.2018.8646442","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646442","url":null,"abstract":"Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to fisheries survey. To measure the fish size and length accurately, a reliable segmentation result is required. However, there are two major challenges about the segmentation. One is that images may be blurred due to the spray of water on the camera lens, and the other is that some part of the fish body is out of the camera view. In this paper, we present an innovative and effective contour-based segmentation refinement and missing shape recovery method from an arbitrary initial segmentation. The refinement is processed from coarse level to fine level. At the coarse level, a weighted affine transform is estimated to align the entire fish contour of the initial segmentation with trained representative contours. At the finer and finest levels, we iteratively refine the contour segments to represent the poorly segmented or shape missing image. The proposed method shows promising results on segmentation and length measurement for both water drop images and images with part of the fish out of the camera view.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131562811","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

A LARGE-SCALE EXTENSION OF SPARSE-CODE MULTIPLE-ACCESS SYSTEM 稀疏码多址系统的大规模扩展

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646492

Chao Yang, Shusen Jing, X. Liang, Zaichen Zhang, X. You, Chuan Zhang

引用次数: 1

ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS 绿色蜂窝网络中受无线电覆盖约束的天线参数梯度下降优化

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646463

Yaxi Liu, W. Huangfu, Haijun Zhang, Keping Long

{"title":"ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS","authors":"Yaxi Liu, W. Huangfu, Haijun Zhang, Keping Long","doi":"10.1109/GlobalSIP.2018.8646463","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646463","url":null,"abstract":"The power consumption of the base stations is the major part of the total consumption of mobile communications, which implies that it is of great significance to reduce the radio transmit power consumption of base stations under the constraint of quality of service. The dynamic of the traffic leads to the adaptive adjustment of the antenna parameters of the base stations or even turning off some base stations serving low user traffic. We propose a penalty method to convert the power consumption optimization problem which constrained by the coverage condition into a simple form with only lower and upper bound condition. We also transform the discrete-valued coverage index into a continuous one and utilize the sub-gradient to conduct the gradient descent algorithm. Moreover, an elite scheme is adopted to preserve the best solution of the optimization problem. The proposed method is applicable to various optimization conditions, either to adjust the transmit power or to adjust the transmit power and the antenna tilt jointly. Experiment results show that our proposed algorithm performs well in BS ON/OFF switching networks. Besides, the effect of jointly adjusting the antenna tilt and the transmit power is better than that of adjusting the transmit power alone.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793596","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 CONTEXT-INTEGRATING SIGNAL CLASSIFICATION MODEL FOR RESOLVING AMBIGUOUS STIMULI 一种解决模糊刺激的上下文整合信号分类模型

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646628

Rajesh Amerineni, L. Gupta, Resh S. Gupta

引用次数: 0

CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS 基于cnn的自适应多尺度深度运动图和稳定关节距离图的动作识别

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646404

Junyou He, Hailun Xia, Chunyan Feng, Yunfei Chu

引用次数: 1

COMPRESSING UNSTRUCTURED MESH DATA USING SPLINE FITS, COMPRESSED SENSING, AND REGRESSION METHODS 使用样条拟合、压缩感知和回归方法压缩非结构化网格数据

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GLOBALSIP.2018.8646678

C. Kamath, Y. Fan

引用次数: 2

SPECTRAL CLUSTERING FOR BEAM-FREE SATELLITE COMMUNICATIONS 无波束卫星通信的频谱聚类

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646351

M. Vázquez, A. Pérez-Neira

引用次数: 6

A Smartphone-based Wellness Assessment Using Mobile Sensors 使用移动传感器的基于智能手机的健康评估

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646590

Katherine McLeod, L. Girchenko, Peter Spenler, P. Spachos

引用次数: 3

PATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION 用于点云压缩缩放的补丁感知平均滤波器

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646392

Keming Cao, Yi Xu, P. Cosman

引用次数: 2