2022 IEEE International Conference on Image Processing (ICIP)最新文献

Graph Autoencoder-Based Embedded Learning in Dynamic Brain Networks for Autism Spectrum Disorder Identification 基于图自编码器的动态脑网络嵌入式学习用于自闭症谱系障碍识别

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898034

Fuad M. Noman, S. Yap, R. Phan, H. Ombao, C. Ting

{"title":"Graph Autoencoder-Based Embedded Learning in Dynamic Brain Networks for Autism Spectrum Disorder Identification","authors":"Fuad M. Noman, S. Yap, R. Phan, H. Ombao, C. Ting","doi":"10.1109/ICIP46576.2022.9898034","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9898034","url":null,"abstract":"Recent applications of pattern recognition techniques to brain connectome-based classification focus on static functional connectivity (FC) neglecting the dynamics of FC over time, and use input connectivity matrices on a regular Euclidean grid. We exploit the graph convolutional networks (GCNs) to learn irregular structural patterns in brain FC networks and propose extensions to capture dynamic changes in network topology. We develop a dynamic graph autoencoder (DyGAE)-based framework to leverage the time-varying topological structures of dynamic brain networks for identification of autism spectrum disorder (ASD). The framework combines a GCN-based DyGAE to encode individual-level dynamic networks into time-varying low-dimensional network embeddings, and classifiers based on weighted fully-connected neural network (FCNN) and long short-term memory (LSTM) to facilitate dynamic graph classification via the learned spatial-temporal information. Evaluation on a large ABIDE resting-state functional magnetic resonance imaging (rs-fMRI) dataset shows that our method outperformed state-of-the-art methods in detecting altered FC in ASD. Dynamic FC analyses with DyGAE learned embeddings also reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"101 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":"115155125","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: 3

Aggregated Context Network For Semantic Segmentation Of Aerial Images 基于聚合上下文网络的航空图像语义分割

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9898016

A. Chouhan, A. Sur, D. Chutia

引用次数: 0

Automatic Illumination of Flat-Colored Drawings by 3D Augmentation of 2D Silhouettes 二维轮廓的三维增强实现平面彩色绘图的自动照明

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897386

D. Tschumperlé, C. Porquet, A. Mahboubi

引用次数: 0

Deformable Alignment And Scale-Adaptive Feature Extraction Network For Continuous-Scale Satellite Video Super-Resolution 面向连续尺度卫星视频超分辨率的形变对齐与尺度自适应特征提取网络

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897998

Ning Ni, Hanlin Wu, Li-bao Zhang

{"title":"Deformable Alignment And Scale-Adaptive Feature Extraction Network For Continuous-Scale Satellite Video Super-Resolution","authors":"Ning Ni, Hanlin Wu, Li-bao Zhang","doi":"10.1109/ICIP46576.2022.9897998","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897998","url":null,"abstract":"Video super-resolution (VSR), especially continuous-scale VSR, plays a crucial role in improving the quality of satellite video. Continuous-scale VSR aims to use a single model to process arbitrary (integer or non-integer) scale factors, which is conducive to meeting the needs of video images transmission with different compression ratios and arbitrarily zooming by rolling the mouse wheel. In this article, we propose a novel network to achieve continuous-scale satellite VSR (CAVSR). Specifically, first, we propose a time-series-aware dynamic routing deformable alignment module (TDAM) for feature alignment. Second, we develop a scale-adaptive feature extraction module (SFEM), which uses the proposed scale-adaptive convolution (SA-Conv) to dynamically generate different filters based on the input scale information. Finally, we design a global implicit function feature-adaptive walk continuous-scale upsampling module (GFCUM), which can perform feature-adaptive walks according to the input features with different scale information and finally complete the continuous-scale mapping from coordinates to pixel values. Experimental results have demonstrated the CAVSR has superior reconstruction performance.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"41 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":"120921372","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: 3

Multi-Stage Feature Alignment Network for Video Super-Resolution 视频超分辨率多阶段特征对齐网络

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897627

Keito Suzuki, M. Ikehara

引用次数: 0

Linear Discriminant Analysis Metric Learning Using Siamese Neural Networks 基于暹罗神经网络的线性判别分析度量学习

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897925

Abin Jose, Qinglin Mei, D. Eschweiler, Ina Laube, J. Stegmaier

{"title":"Linear Discriminant Analysis Metric Learning Using Siamese Neural Networks","authors":"Abin Jose, Qinglin Mei, D. Eschweiler, Ina Laube, J. Stegmaier","doi":"10.1109/ICIP46576.2022.9897925","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897925","url":null,"abstract":"We propose a method for learning the Linear Discriminant Analysis (LDA) using a Siamese Neural Network (SNN) architecture for learning a low dimensional image descriptor. The novelty of our work is that we learn the LDA projection matrix between the final fully-connected layers of an SNN. An SNN architecture is used since the proposed loss maximizes the Kullback-Leibler divergence between the feature distributions from the two branches of an SNN. The network learns an optimized feature space having inherent properties pertaining to the learning of LDA. The learned image descriptors are a) low-dimensional, b) have small intra-class variance, c) large inter-class variance, and d) can distinguish the classes with linear decision hyperplanes. The proposed method has the advantage that LDA learning happens end-to-end. We measured the classification accuracy in the three datasets MNIST, CIFAR-10, and STL-10 and compared the performance with other state-of-the-art methods. We also measured the KL divergence between the class pairs and visualized the projections of feature vectors along the learned discriminant directions.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"38 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":"127088933","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

Revisiting Artistic Style Transfer for Data Augmentation in A Real-Case Scenario 重新审视真实场景中数据增强的艺术风格转移

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897728

Stefano D'Angelo, F. Precioso, F. Gandon

引用次数: 0

High-Resolution NIR Prediction from RGB Images: Application to Plant Phenotyping 基于RGB图像的高分辨率近红外预测:在植物表型分析中的应用

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897670

Ankit Shukla, Avinash Upadhyay, Manoj Sharma, V. Chinnusamy, Sudhir Kumar

引用次数: 1

AI4EO Hyperview: A Spectralnet3d and Rnnplus Approach for Sustainable Soil Parameter Estimation on Hyperspectral Image Data AI4EO Hyperview:基于高光谱影像数据的可持续土壤参数估计的Spectralnet3d和rnplus方法

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897889

Claudius Zelenka, Andreas Lohrer, Mirjam Bayer, Peer Kröger

引用次数: 0

Strong-Weak Integrated Semi-Supervision for Unsupervised Domain Adaptation 无监督域自适应的强弱综合半监督

2022 IEEE International Conference on Image Processing (ICIP) Pub Date : 2022-10-16 DOI: 10.1109/ICIP46576.2022.9897242

Xiaohu Lu, H. Radha

{"title":"Strong-Weak Integrated Semi-Supervision for Unsupervised Domain Adaptation","authors":"Xiaohu Lu, H. Radha","doi":"10.1109/ICIP46576.2022.9897242","DOIUrl":"https://doi.org/10.1109/ICIP46576.2022.9897242","url":null,"abstract":"Unsupervised domain adaptation (UDA) focuses on transferring knowledge learned in the labeled source domain to the unlabeled target domain. Semi-supervised learning is a proven strategy for improving UDA performance. In this paper, we propose a novel strong-weak integrated semi-supervision (SWISS) learning strategy for unsupervised domain adaptation. Under the proposed SWISSUDA framework, a strong representative set with high confidence but low diversity target domain samples and a weak representative set with low confidence but high diversity target domain samples are updated constantly during the training process. Both sets are fused randomly to generate an augmented strong-weak training batch with pseudo-labels to train the network during every iteration. Moreover, a novel adversarial logit loss is proposed to reduce the intra-class divergence between source and target domains, which is back-propagated adversarially with a gradient reverse layer between the classifier and the rest of the network. Experimental results based on two popular benchmarks, Office-Home, and DomainNet, show the effectiveness of the proposed SWISS framework with our method achieving the best performance in both Office-Home and DomainNet.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"30 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":"126010714","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