2022 IEEE International Conference on Image Processing (ICIP)最新文献_第6页

Recurrent Attentive Decomposition Network for Low-Light Image Enhancement 用于弱光图像增强的递归关注分解网络

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897342

Haoyu Gao, Lin Zhang, Shunli Zhang

引用次数: 0

Yolo-SG: Salience-Guided Detection Of Small Objects In Medical Images Yolo-SG:医学图像中小目标的显著性引导检测

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898077

Rong Han, Xiaohong Liu, Ting Chen

{"title":"Yolo-SG: Salience-Guided Detection Of Small Objects In Medical Images","authors":"Rong Han, Xiaohong Liu, Ting Chen","doi":"10.1109/ICIP46576.2022.9898077","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9898077","url":null,"abstract":"Object detection, a crucial component of medical image analysis, provides physicians with an interpretable auxiliary diagnostic basis. Although existing object detection models have had great success with natural images, the growing resolution of medical images makes the problem especially challenging because of the increased expectations to exploit the image details and discover small targets in images. For instance, lesions are occasionally diminutive relative to high-resolution medical images. To address this problem, we present YOLO-SG, a salience-guided (SG) deep learning model that improves small object detection by attending to detailed regions via a generated salience map. YOLO-SG performs two rounds of detection: coarse detection and salience-guided detection. In the first round of coarse detection, YOLO-SG detects objects using a deep convolutional detection model and proposes a salience map utilizing the context surrounding objects to guide the subsequent round of detection. In the second round, YOLO-SG extracts salient regions from the original input image based on the generated salience map and combines local detail with global context information to improve the object detection performance. The experimental results demonstrate that YOLO-SG outperforms the state-of-the-art models, especially when detecting small objects.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132791364","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

Coronary Artery Centerline Tracking with the Morphological Skeleton Loss 冠状动脉中心线追踪与形态学骨架丢失

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897385

Mario Viti, H. Talbot, B. Abdallah, E. Perot, N. Gogin

引用次数: 1

Human-Centric Image Retrieval with Gaze-Based Image Captioning 基于注视的图像字幕的以人为中心的图像检索

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897949

Yuhu Feng, Keisuke Maeda, Takahiro Ogawa, M. Haseyama

{"title":"Human-Centric Image Retrieval with Gaze-Based Image Captioning","authors":"Yuhu Feng, Keisuke Maeda, Takahiro Ogawa, M. Haseyama","doi":"10.1109/ICIP46576.2022.9897949","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897949","url":null,"abstract":"This paper presents human-centric image retrieval with gaze-based image captioning. Although the development of cross-modal embedding techniques has enabled advanced image retrieval, many methods have focused only on the information obtained from the contents such as image and text. For further extending the image retrieval, it is necessary to construct retrieval techniques that directly reflect human intentions. In this paper, we propose a new retrieval approach via image captioning based on gaze information by focusing on the fact that the gaze information obtained from humans contains semantic information. Specifically, we construct a transformer, connect caption and gaze trace (CGT) model that learns the relationship among images, captioning provided by humans and gaze traces. Our CGT model enables transformer-based learning by dividing the gaze traces into several bounding boxes, and thus, gaze-based image captioning becomes feasible. By using the obtained captioning for cross-modal retrieval, we can achieve human-centric image retrieval. The technical contribution of this paper is transforming the gaze trace into the captioning via the transformer-based encoder. In the experiments, by comparing the cross-modal embedding method, the effectiveness of the proposed method is proved.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131005523","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

Hierarchical Training for Distributed Deep Learning Based on Multimedia Data over Band-Limited Networks 基于频带限制网络上多媒体数据的分布式深度学习分层训练

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897383

Siyu Qi, Lahiru D. Chamain, Zhi Ding

引用次数: 1

Subjective Quality Evaluation of Point Clouds with 3D Stereoscopic Visualization 基于三维立体可视化的点云主观质量评价

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897937

João Prazeres, Manuela Pereira, A. Pinheiro

引用次数: 4

Towards Model Quantization on the Resilience Against Membership Inference Attacks 抗隶属推理攻击弹性模型量化研究

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897681

C. Kowalski, Azadeh Famili, Yingjie Lao

引用次数: 2

Face Reconstruction from Deep Facial Embeddings using a Convolutional Neural Network 基于卷积神经网络的深度面部嵌入人脸重建

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897535

Hatef Otroshi-Shahreza, Vedrana Krivokuća Hahn, S. Marcel

引用次数: 10

Deep Neural Network-Based Noisy Pixel Estimation for Breast Ultrasound Segmentation 基于深度神经网络的乳腺超声分割噪声像素估计

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898006

Songbai Jin, Wen-kai Lu, P. Monkam

{"title":"Deep Neural Network-Based Noisy Pixel Estimation for Breast Ultrasound Segmentation","authors":"Songbai Jin, Wen-kai Lu, P. Monkam","doi":"10.1109/ICIP46576.2022.9898006","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9898006","url":null,"abstract":"The success of modern deep learning algorithms for image segmentation heavily relies on the availability of high-quality labels for training. However, obtaining accurate labels is time-consuming and tedious, and requires expertise. If directly trained with dataset with noisy annotations, networks can easily overfit to noisy labels and result in poor performance, which might lead to serious misinterpretation. To this end, we propose a noisy pixel estimation approach based on deep neural network, which helps correct the noisy annotations resulting in better prediction performance. First, a deep neural network is trained to detect noisy pixels from image annotations. Then, the estimated noisy pixels are used to correct the noisy annotations. Finally, the corrected annotations are used to train the deep learning model. Our proposed framework is validated on the breast tumor segmentation task. The obtained experimental results show that our proposed method can improve the robustness of deep learning model under noisy annotations while achieving favorable performance against existing noisy label correction methods.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132429326","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

Downsampling Based Light Field Video Coding with Restoration Network Using Joint Spatio-Angular and Epipolar Information 基于空间角和极极联合信息的下采样恢复网络光场视频编码

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897948

V. V. Duong, T. N. Huu, Jonghoon Yim, B. Jeon

引用次数: 5