2016 IEEE International Conference on Image Processing (ICIP)最新文献_第8页

Model based image reconstruction with physics based priors 基于物理先验的模型图像重建

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532945

M. U. Sadiq, J. Simmons, C. Bouman

{"title":"Model based image reconstruction with physics based priors","authors":"M. U. Sadiq, J. Simmons, C. Bouman","doi":"10.1109/ICIP.2016.7532945","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532945","url":null,"abstract":"Computed tomography is increasingly enabling scientists to study physical processes of materials at micron scales. The MBIR framework provides a powerful method for CT reconstruction by incorporating both a measurement model and prior model. Classically, the choice of prior has been limited to models enforcing local similarity in the image data. In some material science problems, however, much more may be known about the underlying physical process being imaged. Moreover, recent work in Plug-And-Play decoupling of the MBIR problem has enabled researchers to look beyond classical prior models, and innovations in methods of data acquisition such as interlaced view sampling have also shown promise for imaging of dynamic physical processes. In this paper, we propose an MBIR framework with a physics based prior model - namely the Cahn-Hilliard equation. The Cahn-Hilliard equation can be used to describe the spatiotemporal evolution of binary alloys. After formulating the MBIR cost with Cahn-Hilliard prior, we use Plug-And-Play algorithm with ICD optimization to minimize this cost. We apply this method to simulated data using the interlaced-view sampling method of data acquisition. Results show superior reconstruction quality compared to the Filtered Back Projection. Though we use Cahn-Hilliard equation as one instance, the method can be easily extended to use any other physics-based prior model for a different set of applications.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"153 1","pages":"3176-3179"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86155506","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

Trilateral filtering-based hybrid up-sampling in dual domains for single video frame super resolution 基于三边滤波的双域混合上采样单帧超分辨率

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532540

Zhenyu Wu, H. Hu

{"title":"Trilateral filtering-based hybrid up-sampling in dual domains for single video frame super resolution","authors":"Zhenyu Wu, H. Hu","doi":"10.1109/ICIP.2016.7532540","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532540","url":null,"abstract":"Up-sampling has been one of the key techniques for multimedia processing. Higher resolution videos are always the pursuit goal of major customers. Pervasive applications of multimedia processing starve for this technique to solve mismatch problems in display resolutions between senders and receivers. The hybrid DCT-Wiener-based interpolation method designs a powerful framework to interpolate video frames by mining the information in both spatial and DCT domain briefly. It can provide better objective as well as visual qualities with low complexity than many existing well studied interpolation methods. This paper presents an analysis about the bottleneck of hybrid DCT-Wiener-based interpolation method firstly. And then, proposes a trilateral filtering-based hybrid up-sampling algorithm in dual domains, which has dug the information in spatial and frequency domains more deeply. The proposed spatial domain interpolation scheme is an adaptive Wiener filter with a trilateral filtering enhancement, which possess capability to overcome the quarter-pixel shift mismatch of hybrid DCT-Wiener-based interpolation method and achieve much more accurate detail information estimation. Furthermore, flexible block size chosen mechanism in frequency domain enables the whole proposed up-sampling algorithm achieve further advantages in high frequency coefficients retaining. Experiments have been carried out to demonstrate that the proposed algorithm is able to achieve noticeable gains over state of art methods in both objective and visual qualities measurements.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"15 1","pages":"1160-1164"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84737057","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

Pedestrian detection via a leg-driven physiology framework 通过腿驱动的生理学框架行人检测

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532895

Gongbo Liang, Qi Li, Xiangui Kang

引用次数: 0

HEVC still image coding and high efficiency image file format HEVC静态图像编码和高效的图像文件格式

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532321

J. Lainema, M. Hannuksela, V. Vadakital, Emre B. Aksu

引用次数: 23

Localized region context and object feature fusion for people head detection 基于局部区域上下文和目标特征融合的人头部检测

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532426

Yule Li, Y. Dou, Xinwang Liu, Teng Li

{"title":"Localized region context and object feature fusion for people head detection","authors":"Yule Li, Y. Dou, Xinwang Liu, Teng Li","doi":"10.1109/ICIP.2016.7532426","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532426","url":null,"abstract":"People head detection in crowded scenes is challenging due to the large variability in clothing and appearance, small scales of people, and strong partial occlusions. Traditional bottom-up proposal methods and existing region proposal network approaches suffer from either poor recall or low precision. In this paper, we propose to improve both the recall and precision of head detection of region proposal models by integrating the local head information. In specific, we first use a region proposal network to predict the bounding boxes and corresponding scores of multiple instances in the region. A local head classifier network is then trained to score the bounding box generated from the region proposal model. After that, we propose an adaptive fusion method by optimally combining both the region and local scores to obtain the final score of each candidate bounding box. Furthermore, our fusion models can automatically learn the optimal hyper-parameters from data. Our algorithm achieves superior people head detection performance on the crowded scenes data set, which significantly outperforms several recent state-of-the-art baselines in the literature.","PeriodicalId":6521,"journal":{"name":"2016 IEEE International Conference on Image Processing (ICIP)","volume":"35 1","pages":"594-598"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88260530","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: 11

Characterizing distortions in first-person videos 描述第一人称视频中的扭曲

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532797

Chen Bai, A. Reibman

引用次数: 5

H.264 intra coding with transforms based on prediction inaccuracy modeling 基于预测误差建模的H.264帧内编码

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532785

Xun Cai, J. Lim

引用次数: 2

Fisher-selective search for object detection 目标检测的费雪选择性搜索

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7533037

Ilker Buzcu, Aydin Alatan

引用次数: 12

Structured Discriminative Nonnegative Matrix Factorization for hyperspectral unmixing 高光谱分解的结构判别非负矩阵分解

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532678

Xue Li, J. Zhou, Lei Tong, Xun Yu, Jianhui Guo, Chunxia Zhao

引用次数: 6

Membrane segmentation via active learning with deep networks 基于深度网络主动学习的膜分割

2016 IEEE International Conference on Image Processing (ICIP) Pub Date : 2016-09-01 DOI: 10.1109/ICIP.2016.7532697

Utkarsh Gaur, M. Kourakis, E. Newman-Smith, William C. Smith, B. S. Manjunath

引用次数: 10