2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)最新文献

Video Tracking of Insect Flight Path: Towards Behavioral Assessment 昆虫飞行轨迹的视频跟踪:面向行为评估

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608167

Yufang Bao, H. Krim

引用次数: 0

Image Super-Resolution based on multi-pairs of dictionaries via Patch Prior Guided Clustering 基于多对字典的图像超分辨率补丁先验引导聚类

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608128

Dongfeng Mei, Xuan Zhu, Cheng Yue, Qingwen Cao, Lei Wang, Longfei Zhang, Q. Song

引用次数: 0

Interval-valued JPEG decompression for artifact suppression 用于伪影抑制的间隔值JPEG解压缩

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608122

V. Itier, Florentin Kucharczak, O. Strauss, W. Puech

引用次数: 5

3D lymphoma detection in PET-CT images with supervoxel and CRFs 超体素和CRFs在PET-CT图像中的三维淋巴瘤检测

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608129

Jierui Zha, P. Decazes, Jérôme Lapuyade, A. Elmoataz, S. Ruan

{"title":"3D lymphoma detection in PET-CT images with supervoxel and CRFs","authors":"Jierui Zha, P. Decazes, Jérôme Lapuyade, A. Elmoataz, S. Ruan","doi":"10.1109/IPTA.2018.8608129","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608129","url":null,"abstract":"In this paper we present a lymphoma detection method on image PET-CT by combining supervoxel and conditional random fields(CRFs). Positron-emission tomography(PET) is often used to analysis diseases like cancer. And it is usually combined with computed tomography scan (CT), which provides accurate anatomical location of lesions. Most lymphoma detection in PET are based on machine learning technique which requires a large learning database. However, it is difficult to acquire such a large standard database in medical field. In our previous work, a new approach which combines an anatomical atlas obtained in CT with CRFs (Conditional Random Fields) in PET is proposed and is proved to have good results, however it is very time consuming due to the fully connection of each voxel in 3D. To cope with this problem, we proposed a method that combines supervoxel and CRFs to accelerate the progress. Our method consists of 3 steps. First, we apply the supervoxel on the PET image to group the voxels into supervoxels. Then, an anatomic atlas is applied on CT to remove the organs having hyper-fixation in PET. Finally, CRFs will detect lymphoma regions in PET. The obtained results show good performance in terms of speed and lymphoma detection.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","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":"127262445","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

An Image Compression Scheme Based on Block Truncation Coding Using Real-time Block Classification and Modified Threshold for Pixels Grouping 一种基于实时块分类和改进阈值的块截断编码图像压缩方案

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608124

Zheng Hui, Quan Zhou

引用次数: 1

A Method for Automatic Tracking of Cell Nuclei in 2D Epifluorescence Microscopy Image Sequences 一种二维荧光显微镜图像序列中细胞核自动跟踪的方法

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608156

Alexandr Yu. Kondrati'ev, H. Yaginuma, Y. Okada, D. Sorokin

{"title":"A Method for Automatic Tracking of Cell Nuclei in 2D Epifluorescence Microscopy Image Sequences","authors":"Alexandr Yu. Kondrati'ev, H. Yaginuma, Y. Okada, D. Sorokin","doi":"10.1109/IPTA.2018.8608156","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608156","url":null,"abstract":"The automated segmentation and tracking of cells in live cell microscopy image sequences is an actual problem in many biological research areas. Despite the existence of different cell tracking approaches, a universal solution for this problem still does not exist due to high variety of fluorescent microscopy image data obtained using different techniques, where cells have completely different visual appearance. Moreover, the cells can significantly change their shape even within a single image sequence. In this work, we propose a cell tracking algorithm designed for detecting and tracking cell nuclei in 2D image sequences obtained by epifluorescence microscopy, where the cell appearance drastically changes during cell mitosis. We used marker controlled watershed algorithm combined with blob detection for nuclei segmentation followed by a generalized nearest neighbor approach for nuclei tracking. We also employed a special mitosis detection algorithm to process cell division events. Our approach was quantitatively evaluated for its segmentation and tracking accuracy using the real image data annotated by human experts. The evaluation procedure was performed based on the protocol used in the Cell Tracking Challenge. It was shown that the proposed approach outperforms an existing semiautomatic method in both segmentation and tracking accuracy.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"11 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":"132556765","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

[Copyright notice] (版权)

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/ipta.2018.8608135

引用次数: 0

Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs 视觉显著性图与深度cnn在建筑图像分类问题中的比较研究

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608125

A. M. Obeso, J. Benois-Pineau, Kamel Guissous, V. Gouet-Brunet, M. García-Vázquez, A. A. Ramírez-Acosta

{"title":"Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs","authors":"A. M. Obeso, J. Benois-Pineau, Kamel Guissous, V. Gouet-Brunet, M. García-Vázquez, A. A. Ramírez-Acosta","doi":"10.1109/IPTA.2018.8608125","DOIUrl":"https://doi.org/10.1109/IPTA.2018.8608125","url":null,"abstract":"Incorporating Human Visual System (HVS) models into building of classifiers has become an intensively researched field in visual content mining. In the variety of models of HVS we are interested in so-called visual saliency maps. Contrarily to scan-paths they model instantaneous attention assigning the degree of interestingness/saliency for humans to each pixel in the image plane. In various tasks of visual content understanding, these maps proved to be efficient stressing contribution of the areas of interest in image plane to classifiers models. In previous works saliency layers have been introduced in Deep CNNs, showing that they allow reducing training time getting similar accuracy and loss values in optimal models. In case of large image collections efficient building of saliency maps is based on predictive models of visual attention. They are generally bottom-up and are not adapted to specific visual tasks. Unless they are built for specific content, such as \"urban images\"-targeted saliency maps we also compare in this paper. In present research we propose a \"bootstrap\" strategy of building visual saliency maps for particular tasks of visual data mining. A small collection of images relevant to the visual understanding problem is annotated with gaze fixations. Then the propagation to a large training dataset is ensured and compared with the classical GBVS model and a recent method of saliency for urban image content. The classification results within Deep CNN framework are promising compared to the purely automatic visual saliency prediction.","PeriodicalId":272294,"journal":{"name":"2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"1 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":"129402091","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: 10

A new enhancement algorithm for the low illumination image based on fog-degraded model 一种基于雾退化模型的低照度图像增强算法

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608164

Feiyan Cheng, Junsheng Shi, Lijun Yun, Zhenhua Du, Zhijian Xu, Xiaoqiao Huang, Zaiqing Chen

引用次数: 0

Image Registration Algorithm Based on Super pixel Segmentation and SURF Feature Points 基于超像素分割和SURF特征点的图像配准算法

2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2018-11-01 DOI: 10.1109/IPTA.2018.8608151

Weiyi Wei, Chengfeng A, Yufei Zhao, Guicang Zhang

引用次数: 4