2022 IEEE International Conference on Image Processing (ICIP)最新文献_第10页

MLS-GAN: Multi-Level Semantic Guided Image Colorization MLS-GAN:多层次语义引导图像着色

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897614

Xinning Chai, Xibei Liu, Hengsheng Zhang, Han Wang, Li Song, Liean Cao

{"title":"MLS-GAN: Multi-Level Semantic Guided Image Colorization","authors":"Xinning Chai, Xibei Liu, Hengsheng Zhang, Han Wang, Li Song, Liean Cao","doi":"10.1109/ICIP46576.2022.9897614","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897614","url":null,"abstract":"Image colorization predicts plausible color versions of given grayscale images. Recently, several methods incorporate image semantics to assist image colorization and have shown impressive performance. To further exploit and take full advantage of more semantic information, in this paper, we propose a Multi-Level Semantic guided Generative Adversarial Network (MLS-GAN) for image colorization. Specifically, we utilize three different levels of semantics to guide the colorization process: image level, segmentation level and contextual level. Image-level classification semantics is used to learn category and high-level semantics, ensuring the reasonability of color results. At the segmentation level, multi-scale saliency map semantics is extracted to provide figure-background separation information, which can efficiently alleviate semantic confusion, especially for images with complex backgrounds. Furthermore, we novelly use non-local blocks to capture long-range semantic dependencies at the contextual level. Experiments show that our method enhances color consistency and can produce more vivid color in visually important regions, outperforming state-of-the-art methods qualitatively and quantitatively.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"7 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":"125521641","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

Hidden Conditional Adversarial Attacks 隐藏条件对抗性攻击

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898075

Junyoung Byun, Kyujin Shim, Hyojun Go, Changick Kim

引用次数: 0

Predicting Path Loss Distributions of a Wireless Communication System for Multiple Base Station Altitudes from Satellite Images 从卫星图像预测多基站高度无线通信系统的路径损耗分布

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897467

Ibrahim Shoer, B. Gunturk, H. Ateş, T. Baykaş

引用次数: 1

Prototype Queue Learning for Multi-Class Few-Shot Semantic Segmentation 多类少镜头语义分割的原型队列学习

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897698

Zichao Wang, Zhiyu Jiang, Yuan Yuan

{"title":"Prototype Queue Learning for Multi-Class Few-Shot Semantic Segmentation","authors":"Zichao Wang, Zhiyu Jiang, Yuan Yuan","doi":"10.1109/ICIP46576.2022.9897698","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897698","url":null,"abstract":"Few-shot semantic segmentation aims to undertake the segmentation task of novel classes with only a few annotated images. However, most existing methods tend to segment the foreground and background in the image, which limits practical application. In this paper, we present a Prototype Queue Network, which performs few-shot segmentation on multiclass in the images by aggregating binary classes into multiple classes. A prototype queue learning module is proposed to achieve multi-class segmentation by mining the relationship among features of different classes with queue and pseudo labels. In addition, a background latent class distribution refinement module is proposed to prevent the latent novel class in the background from being incorrectly predicted, which refines the boundary among different classes. Furthermore, we propose a two-steps segmentation module to optimize the process of extracting feature representation by adding progressive constraints, which can further improve the accuracy of segmentation. Experiments on the UDD and Vaihingen datasets demonstrate that our method achieves state-of-the-art performance.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"112 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":"128483048","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

Dispense Mode for Inference to Accelerate Branchynet 加速分支网络的推理分配模式

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897574

Zhiwei Liang, Yuezhi Zhou

引用次数: 2

3D Head Pose Estimation Based on Graph Convolutional Network from A Single RGB Image 基于图卷积网络的单幅RGB图像三维头部姿态估计

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897519

W. Lie, Monyneath Yim, Lee Aing, J. Chiang

引用次数: 0

Multifractal Anomaly Detection in Images via Space-Scale Surrogates 基于空间尺度替代的图像多重分形异常检测

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897659

H. Wendt, Lorena Leon, J. Tourneret, P. Abry

{"title":"Multifractal Anomaly Detection in Images via Space-Scale Surrogates","authors":"H. Wendt, Lorena Leon, J. Tourneret, P. Abry","doi":"10.1109/ICIP46576.2022.9897659","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897659","url":null,"abstract":"Multifractal analysis provides a global description for the spatial fluctuations of the strengths of the pointwise regularity of image amplitudes. A global image characterization leads to robust estimation, but is blind to and corrupted by small regions in the image whose multifractality differs from that of the rest of the image. Prior detection of such zones with anomalous multifractality is thus crucial for relevant analysis, and their delineation of central interest in applications, yet has never been achieved so far. The goal of this work is to devise and study such a multifractal anomaly detection scheme. Our approach combines three original key ingredients: i) a recently proposed generic model for the statistics of the multiresolution coefficients used in multifractal estimation (wavelet leaders), ii) an original surrogate data generation procedure for simulating a hypothesized global multifractality and iii) a combination of multiple hypothesis tests to achieve pixel-wise detection. Numerical simulations using synthetic multifractal images show that our procedure is operational and leads to good multifractal anomaly detection results for a range of target sizes and parameter values of practical relevance.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"13 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":"130597668","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

Memory Reduction Of Cgh Calculation Based On Integrating Point Light Sources 基于积分点光源的Cgh计算内存缩减

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897936

Ryota Koiso, R. Watanabe, Keisuke Nonaka, Tatsuya Kobayashi

引用次数: 0

Predicting Radiologist Attention During Mammogram Reading with Deep and Shallow High-Resolution Encoding 用深、浅高分辨率编码预测乳房x光片阅读时放射科医生的注意力

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897723

Jianxun Lou, Hanhe Lin, David Marshall, Richard White, Young Yang, S. Shelmerdine, Hantao Liu

{"title":"Predicting Radiologist Attention During Mammogram Reading with Deep and Shallow High-Resolution Encoding","authors":"Jianxun Lou, Hanhe Lin, David Marshall, Richard White, Young Yang, S. Shelmerdine, Hantao Liu","doi":"10.1109/ICIP46576.2022.9897723","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897723","url":null,"abstract":"Radiologists’ eye-movement during diagnostic image reading reflects their personal training and experience, which means that their diagnostic decisions are related to their perceptual processes. For training, monitoring, and performance evaluation of radiologists, it would be beneficial to be able to automatically predict the spatial distribution of the radiologist’s visual attention on the diagnostic images. The measurement of visual saliency is a well-studied area that allows for prediction of a person’s gaze attention. However, compared with the extensively studied natural image visual saliency (in free viewing tasks), the saliency for diagnostic images is less studied; there could be fundamental differences in eye-movement behaviours between these two domains. Most current saliency prediction models have been optimally developed for natural images, which could lead them to be less adept at predicting the visual attention of radiologists during the diagnosis. In this paper, we propose a method specifically for automatically capturing the visual attention of radiologists during mammogram reading. By adopting high-resolution image representations from both deep and shallow encoders, the proposed method avoids potential detail losses and achieves superior results on multiple evaluation metrics in a large mammogram eye-movement dataset.","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":"129692325","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

Representation Learning Optimization for 3D Point Cloud Quality Assessment Without Reference 无参考的三维点云质量评估的表示学习优化

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897689

M. Tliba, A. Chetouani, G. Valenzise, F. Dufaux

引用次数: 6