2021 IEEE International Conference on Image Processing (ICIP)最新文献_第6页

UTR: Unsupervised Learning of Thickness-Insensitive Representations for Electron Microscope Image 电子显微镜图像厚度不敏感表征的无监督学习

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506597

Tong Xin, Bohao Chen, Xi Chen, Hua Han

{"title":"UTR: Unsupervised Learning of Thickness-Insensitive Representations for Electron Microscope Image","authors":"Tong Xin, Bohao Chen, Xi Chen, Hua Han","doi":"10.1109/ICIP42928.2021.9506597","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506597","url":null,"abstract":"Registration of serial section electron microscopy (ssEM) images is essential for neural circuit reconstruction. Morphologies of neurite structure in adjacent sections are different. Thus, it is challenging to extract valid features in ssEM image registration. Convolutional neural networks (CNN) have made unprecedented progress in feature extraction of natural images. However, morphological differences need not be considered in the registration of natural images. Directly applying these methods will result in matching failure or over-registration. This paper proposes an unsupervised learning-based representation taking the morphological differences of ssEM images into account. CNN architecture was used to extract the feature. To train the network, the focused ion beam scanning electron microscope (FIB-SEM) images are used. The FIB-SEM images are in situ, so they are naturally registered. Sampling those images with a certain thickness can teach CNN to learn changes in neurite structure. The learned feature can be directly applied to existing ssEM image registration methods and reduce the negative effect of section thickness on registration accuracy. The experimental results show that the proposed feature outperforms the state-of-the-art method in matching accuracy and significantly improves the registration outcome when used in ssEM images.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127324935","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

Multi-Scale Background Suppression Anomaly Detection In Surveillance Videos 监控视频中的多尺度背景抑制异常检测

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506580

Yang Zhen, Yuanfan Guo, Jinjie Wei, Xiuguo Bao, Di Huang

引用次数: 2

Federated Trace: A Node Selection Method for More Efficient Federated Learning 联邦跟踪:一种更有效的联邦学习的节点选择方法

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506725

Zirui Zhu, Lifeng Sun

引用次数: 3

2DTPCA: A New Framework for Multilinear Principal Component Analysis 多线性主成分分析的新框架

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506729

Cagri Ozdemir, R. Hoover, Kyle A. Caudle

引用次数: 4

Semantic Role Aware Correlation Transformer For Text To Video Retrieval 文本到视频检索的语义角色感知相关转换器

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506267

Burak Satar, Hongyuan Zhu, X. Bresson, J. Lim

引用次数: 8

Hierarchical Region Proposal Refinement Network for Weakly Supervised Object Detection 弱监督目标检测的层次区域建议改进网络

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506087

Ming Zhang, Shuaicheng Liu, Bing Zeng

{"title":"Hierarchical Region Proposal Refinement Network for Weakly Supervised Object Detection","authors":"Ming Zhang, Shuaicheng Liu, Bing Zeng","doi":"10.1109/ICIP42928.2021.9506087","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506087","url":null,"abstract":"Weakly supervised object detection (WSOD) has attracted more attention because it only requires image-level annotations to indicate whether a certain class exists. Most WSOD methods utilize multiple instance learning (MIL) to train an object detector where an image is treated as a bag of candidate proposals. Unlike fully supervised object detection (FSOD) that uses the object-aware region proposal network (RPN) to generate effective candidate proposals, WSOD only utilizes region proposal methods (e.g., selective search or edge boxes) due to the lack of instance-level annotations (i.e., bounding boxes). However, the quality of proposals can influence the training of the detector. To solve this problem, we propose a hierarchical region proposal refinement network (HRPRN) to refine these proposals gradually. Specifically, our network contains multiple weakly supervised detectors that are trained stage by stage. In addition, we propose an instance regression refinement model to generate object-aware coordinate offsets to refine proposals at each stage. In order to demonstrate the effectiveness of our method, we conduct experiments on PASCAL VOC 2007 dataset that is the widely used benchmark. Compared with our baseline method, online instance classifier refinement (OICR), our method achieves 9% and 5.6% improvements in terms of mAP and CorLoc, respectively.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124533258","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

Adversarial Attack on Fake-Faces Detectors Under White and Black Box Scenarios 白盒和黑盒场景下假脸检测器的对抗性攻击

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506273

Xiying Wang, R. Ni, Wenjie Li, Yao Zhao

{"title":"Adversarial Attack on Fake-Faces Detectors Under White and Black Box Scenarios","authors":"Xiying Wang, R. Ni, Wenjie Li, Yao Zhao","doi":"10.1109/ICIP42928.2021.9506273","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506273","url":null,"abstract":"Generative Adversarial Network (GAN) models have been widely used in various fields. More recently, styleGAN and styleGAN2 have been developed to synthesize faces that are indistinguishable to the human eyes, which could pose a threat to public security. But latest work has shown that it is possible to identify fakes using powerful CNN networks as classifiers. However, the reliability of these techniques is unknown. Therefore, in this paper we focus on the generation of content-preserving images from fake faces to spoof classifiers. Two GAN-based frameworks are proposed to achieve the goal in the white-box and black-box. For the white-box, a network without up/down sampling is proposed to generate face images to confuse the classifier. In the black-box scenario (where the classifier is unknown), real data is introduced as a guidance for GAN structure to make it adversarial, and a Real Extractor as an auxiliary network to constrain the feature distance between the generated images and the real data to enhance the adversarial capability. Experimental results show that the proposed method effectively reduces the detection accuracy of forensic models with good transferability.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114366351","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

Nuclear Density Distribution Feature for Improving Cervical Histopathological Images Recognition 核密度分布特征提高宫颈组织病理图像识别

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506093

Zhuangzhuang Wang, Mengning Yang, Yangfan Lyu, Kairun Chen, Qicheng Tang

{"title":"Nuclear Density Distribution Feature for Improving Cervical Histopathological Images Recognition","authors":"Zhuangzhuang Wang, Mengning Yang, Yangfan Lyu, Kairun Chen, Qicheng Tang","doi":"10.1109/ICIP42928.2021.9506093","DOIUrl":"https://doi.org/10.1109/ICIP42928.2021.9506093","url":null,"abstract":"Cervical carcinoma is a common type of cancer in the female reproductive system. Early detection and diagnosis can facilitate immediate treatment and prevent progression of the disease. However, in order to achieve better performance, DL-based algorithms just stack various layers with low interpretability. In this paper, a robust and reliable Nuclear Density Distribution Feature (NDDF) based on priors of the pathologists to promote the Cervical Histopathological Image Classification (CHIC) is proposed. Our proposed method combines the nucleus mask segmented by U-Net with the segmentation grid-lines generated from pathology images utilizing SLIC to obtain the NDDF map, which contains information about the morphology, size, number, and spatial distribution of nuclei. The result shows that the proposed model trained with NDDF maps has better performance and accuracy than that trained on RGB images (patch-level histopathological images). More significantly, the accuracy of the two-stream network trained with RGB images and NDDF maps is steadily improved over the corresponding baselines of different complexity.","PeriodicalId":314429,"journal":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505107","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

Segmentation-Aware Text-Guided Image Manipulation 分割感知文本引导图像处理

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506601

T. Haruyama, Ren Togo, Keisuke Maeda, Takahiro Ogawa, M. Haseyama

引用次数: 6

Partitioned Centerpose Network for Bottom-Up Multi-Person Pose Estimation 自底向上多人姿态估计的分区中心姿态网络

2021 IEEE International Conference on Image Processing (ICIP) Pub Date : 2021-09-19 DOI: 10.1109/ICIP42928.2021.9506555

Jiahua Wu, H. Lee

引用次数: 0