2022 IEEE International Conference on Image Processing (ICIP)最新文献_第8页

Deep Learning Classification of Large-Scale Point Clouds: A Case Study on Cuneiform Tablets 大规模点云的深度学习分类——以楔形文字片为例

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898032

Frederik Hagelskjær

引用次数: 1

Variational Depth Estimation on Hypersphere for Panorama 全景超球的变分深度估计

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897914

Jingbo Miao, Yanwei Liu, Kan Wang, Jinxia Liu, Zhen Xu

引用次数: 0

A New Regularization for Retinex Decomposition of Low-Light Images 一种新的弱光图像视网膜分解正则化方法

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897893

Arthur Lecert, R. Fraisse, A. Roumy, C. Guillemot

{"title":"A New Regularization for Retinex Decomposition of Low-Light Images","authors":"Arthur Lecert, R. Fraisse, A. Roumy, C. Guillemot","doi":"10.1109/ICIP46576.2022.9897893","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897893","url":null,"abstract":"We study unsupervised Retinex decomposition for low light image enhancement. Being an underdetermined problem with infinite solutions, well-suited priors are required to reduce the solution space. In this paper, we analyze the characteristics of low-light images and their illumination component and identify a trivial solution not taken into consideration by the previous unsupervised state-of-the-art methods. The challenge comes from the fact that the trivial solution cannot be completely eliminated from the feasible set as it corresponds to the true solution when the low-light image contains a light source or an overexposed area. To address this issue, we propose a new regularization term which only remove absurd solutions and keep plausible ones in the set. To demonstrate the efficiency of the proposed prior, we conduct our experiments using deep image priors in a framework similar to the recent work RetinexDIP and an in-depth ablation study. Finally, we observe no more halo artefacts in the restored image. For all-but-one metrics, our unsupervised approach gives results as good as the supervised state-of-the-art indicating the potential of this framework for low-light image enhancement.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"10 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":"115883934","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

Masked Face Recognition via Self-Attention Based Local Consistency Regularization 基于自注意的局部一致性正则化蒙面人脸识别

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898076

Dongyun Lin, Yiqun Li, Yi Cheng, S. Prasad, Aiyuan Guo

{"title":"Masked Face Recognition via Self-Attention Based Local Consistency Regularization","authors":"Dongyun Lin, Yiqun Li, Yi Cheng, S. Prasad, Aiyuan Guo","doi":"10.1109/ICIP46576.2022.9898076","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9898076","url":null,"abstract":"With the COVID-19 pandemic, one critical measure against infection is wearing masks. This measure poses a huge challenge to the existing face recognition systems by introducing heavy occlusions. In this paper, we propose an effective masked face recognition system. To alleviate the challenge of mask occlusion, we first exploit RetinaFace to achieve robust masked face detection and alignment. Secondly, we propose a deep CNN network for masked face recognition trained by minimizing ArcFace loss together with a local consistency regularization (LCR) loss. This facilitates the network to simultaneously learn globally discriminative face representations of different identities together with locally consistent representations between the non-occluded faces and their counterparts wearing synthesized facial masks. The experiments on the masked LFW dataset demonstrate that the proposed system can produce superior masked face recognition performance over multiple state-of-the-art methods. The proposed method is implemented in a portable Jetson Nano device which can achieve real-time masked face recognition.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"28 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":"124284531","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

A Multi-Source Image Matching Network for UAV Visual Location 无人机视觉定位的多源图像匹配网络

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897631

C. Li, Ganchao Liu, Yuan Yuan

引用次数: 0

A Novel Visual Feature and Gaze Driven Egocentric Video Retargeting 一种新的视觉特征和凝视驱动的自我中心视频重定位

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897908

Aneesh Bhattacharya, S. Malladi, J. Mukhopadhyay

{"title":"A Novel Visual Feature and Gaze Driven Egocentric Video Retargeting","authors":"Aneesh Bhattacharya, S. Malladi, J. Mukhopadhyay","doi":"10.1109/ICIP46576.2022.9897908","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897908","url":null,"abstract":"Egocentric vision data has become popular due to its unique way of capturing first-person perspective. However they are lengthy, contain redundant information and visual noise caused by head movements which disrupt the story being expressed through them. This paper proposes a novel visual feature and gaze driven approach to retarget egocentric videos following the principles of cinematography. This approach is divided into two parts: activity based scene detection and performing panning and zooming to produce visually immersive videos. Firstly, visually similar frames are grouped using DCT feature matching followed by SURF descriptor matching. These groups are further refined using the gaze data to generate different scenes and transitions occurring within an activity. Secondly, the mean 2D gaze positions of scenes are used for generating panning windows enclosing 75% of the frame content. This is done for performing zoom-in and zoom-out operations in the detected scenes and transitions respectively. Our approach has been tested on the GTEA and EGTEA gaze plus datasets witnessing an average accuracy of 88.1% and 72% for sub-activity identification and obtaining an average aspect ratio similarity (ARS) score of 0.967 and 0.73; 60% and 42% SIFT similarity index (SSI) respectively. Code available on Github.1","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"4 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":"114368584","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

Early Pedestrian Intent Prediction via Features Estimation 基于特征估计的早期行人意图预测

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897636

Nada Osman, Enrico Cancelli, Guglielmo Camporese, Pasquale Coscia, Lamberto Ballan

引用次数: 4

Defining Point Cloud Boundaries Using Pseudopotential Scalar Field Implicit Surfaces 用伪势标量场隐式曲面定义点云边界

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897175

Ethan Payne, Amanda Fernandez

引用次数: 0

Towards Zero-Latency Video Transmission Through Frame Extrapolation 通过帧外推实现零延迟视频传输

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897958

Melan Vijayaratnam, Marco Cagnazzo, G. Valenzise, Anthony Trioux, M. Kieffer

{"title":"Towards Zero-Latency Video Transmission Through Frame Extrapolation","authors":"Melan Vijayaratnam, Marco Cagnazzo, G. Valenzise, Anthony Trioux, M. Kieffer","doi":"10.1109/ICIP46576.2022.9897958","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897958","url":null,"abstract":"In the past few years, several efforts have been devoted to reduce individual sources of latency in video delivery, including acquisition, coding and network transmission. The goal is to improve the quality of experience in applications requiring real-time interaction. Nevertheless, these efforts are fundamentally constrained by technological and physical limits. In this paper, we investigate a radically different approach that can arbitrarily reduce the overall latency by means of video extrapolation. We propose two latency compensation schemes where video extrapolation is performed either at the encoder or at the decoder side. Since a loss of fidelity is the price to pay for compensating latency arbitrarily, we study the latency-fidelity compromise using three recent video prediction schemes. Our preliminary results show that by accepting a quality loss, we can compensate a typical latency of 100 ms with a loss of 8 dB in PSNR with the best extrapolator. This approach is promising but also suggests that further work should be done in video prediction to pursue zero-latency video transmission.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"95 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":"116267149","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

Unsupervised Domain-Adaptive Person Re-Identification with Multi-Camera Constraints 多相机约束下的无监督域自适应人再识别

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897377

S. Takeuchi, Fei Li, Sho Iwasaki, Jiaqi Ning, Genta Suzuki

引用次数: 1