International Conference on Digital Image Processing最新文献_第10页

Feature attention network (FA-Net): a deep-learning based approach for underwater single image enhancement 特征注意网络(FA-Net):一种基于深度学习的水下单幅图像增强方法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644516

M. Hamza, Ammar Hawbani, Sami Ul Rehmana, Xingfu Wang, Liang Zhao

{"title":"Feature attention network (FA-Net): a deep-learning based approach for underwater single image enhancement","authors":"M. Hamza, Ammar Hawbani, Sami Ul Rehmana, Xingfu Wang, Liang Zhao","doi":"10.1117/12.2644516","DOIUrl":"https://doi.org/10.1117/12.2644516","url":null,"abstract":"Underwater image processing and analysis have been a hotspot of study in recent years, as more emphasis has been focused to underwater monitoring and usage of marine resources. Compared with the open environment, underwater image encountered with more complicated conditions such as light abortion, scattering, turbulence, nonuniform illumination and color diffusion. Although considerable advances and enhancement techniques achieved in resolving these issues, they treat low-frequency information equally across the entire channel, which results in limiting the network's representativeness. We propose a deep learning and feature-attention-based end-to-end network (FA-Net) to solve this problem. In particular, we propose a Residual Feature Attention Block (RFAB), containing the channel attention, pixel attention, and residual learning mechanism with long and short skip connections. RFAB allows the network to focus on learning high-frequency information while skipping low-frequency information on multi-hop connections. The channel and pixel attention mechanism considers each channel's different features and the uneven distribution of haze over different pixels in the image. The experimental results shows that the FA-Net propose by us provides higher accuracy, quantitatively and qualitatively and superiority to previous state-of-the-art methods.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133625964","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

DIRA: disjoint-identity resolution adaptation for low-resolution face recognition 低分辨率人脸识别的分离身份分辨率适应

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644258

Jacky Chen Long Chai, C. Low, A. Teoh

引用次数: 0

Detecting roads from high-resolution aerial images: a position iteration algorithm for linear target detection 高分辨率航拍图像道路检测:一种线性目标检测的位置迭代算法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644721

Hao He, Shuyang Wang, Qi Yang, Xu Huang, Qian Zhao

{"title":"Detecting roads from high-resolution aerial images: a position iteration algorithm for linear target detection","authors":"Hao He, Shuyang Wang, Qi Yang, Xu Huang, Qian Zhao","doi":"10.1117/12.2644721","DOIUrl":"https://doi.org/10.1117/12.2644721","url":null,"abstract":"Detecting roads from high-resolution photographs can serve forestry, agriculture, traffic and even military areas, and produce significant social and economic value. In this paper, we present a novel method that utilizes the flatness and the connectivity to detect the road in high-resolution aerial images. The method iterates the probable locations of the roads by using the flatness and connects the roads by using the connectivity. Firstly, we introduce a concept of ‘footprint’, which reveals the probable location and extension direction of a road. Given an initial footprint, we assess the flatness between locations to search the resulting footprint. By iterating and connecting the footprints, our approach produces a set of connected line segments that reflect the road to be detected. In addition, a footprints initialization algorithm is introduced to make our method totally automatic, and a road network pruning algorithm is designed to make the result clearer and more accurate. Tested under three high-resolution aerial photographs, our method achieved an accuracy of more than 80%. The algorithm is adapted for road detection and still linear target detection in high-resolution aerial photographs. Since the algorithm does not require artificial features or training data, it can be quickly deployed in application.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134066161","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

Grad-CAM based visualization of 3D CNNs in classifying fMRI 基于Grad-CAM的三维cnn在fMRI分类中的可视化

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643867

Jiajun Fu, Meili Lu, Yifan Cao, Zhaohua Guo, Zicheng Gao

引用次数: 0

Comparison of different classification methods for autism spectrum diagnosis 自闭症谱系诊断中不同分类方法的比较

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644418

Shumin Liu, Zhaohui Wang, Linmao Tian, Y. Zhan

引用次数: 0

A NSST-based infrared and visible image fusion method focusing on luminance effect 一种基于nst的聚焦亮度效应的红外与可见光图像融合方法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644228

Meng Cai, Xinlong Liu

{"title":"A NSST-based infrared and visible image fusion method focusing on luminance effect","authors":"Meng Cai, Xinlong Liu","doi":"10.1117/12.2644228","DOIUrl":"https://doi.org/10.1117/12.2644228","url":null,"abstract":"Generally, the fused image shows fully the actual situation of the scene and contains more detailed information. However, most fusion methods miss the details of the fusion image and confuses the contrast of the raw scene. To solve this problem, we propose a fusion algorithm based on non-subsampled shearlet transform (NSST) that particularly pays attention to the influence of light intensity when calculating the fusion coefficient. The method first decomposes the input images into high- and low-frequency coefficients through NSST. Then regarding the high-frequency coefficients, we calculate the phase consistency (PC) of the decomposed images, and the results are combined with the adaptive simplified pulse coupled neural network (SPCNN) to compose parameter. Meanwhile, for the low-frequency coefficient, the optimal brightness entropy (OBE) of the input images is obtained as the fusion basis. The next step is to fuse the high- and low-frequency sub-band coefficients by the designed fusion rule, and obtain final image through NSST inverse transformation. Experiments show that our method not only keeps well the image details and maintains the overall image luminance while taking care of the overall effect of the image, but also gets a leading position in some evaluation indicators.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278353","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

Surface defect sample generation method based on GAN 基于GAN的表面缺陷样本生成方法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644555

Fangyi Ni, Xiaojun Wu, Jinghui Zhou, Zhichang Liu

引用次数: 0

Multi-task model for human pose estimation and person detection 基于多任务模型的人体姿态估计与检测

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644479

Daiwei Yu, Jun Zhang, Zhao Jin, Guanqun Li, Wenjin Zhang

{"title":"Multi-task model for human pose estimation and person detection","authors":"Daiwei Yu, Jun Zhang, Zhao Jin, Guanqun Li, Wenjin Zhang","doi":"10.1117/12.2644479","DOIUrl":"https://doi.org/10.1117/12.2644479","url":null,"abstract":"Human pose estimation and person detection are two fundamental tasks of human behavior analysis. There has been remarkable progress in these two tasks separately since the development of convolutional neural network. Recently, researchers have paid more attention to one-stage human pose estimation and person detection for the needs of practical application. However, few researches have been reported on completing these two tasks in a single network simultaneously. There are two main reasons: (1) designing an effective mechanism that makes full use of their relevance and complementation to achieve common progress, especially the pose estimation accuracy is really challenging, (2) evaluation bias caused by scale sensitivity difference remains unsolved. To address these problems, we propose a multi-task model for human pose estimation and person detection simultaneously, named PersonPD (person pose and person detection). It predicts keypoint heatmaps and regresses a 4D relative displacement vector (l,t,r,b) which actually encodes the person bounding box and also acts as keypoints' grouping clues. A maximum IOU matching algorithm, named IOU-grouping, is presented to group body joints into individual persons. At the same time, it generates accurate person detection results. In this simple but effective method, our model get competitive person detection and pose estimation performance on COCO datasets1.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126144736","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

Compressing CNN by alternating constraint optimization framework 交替约束优化框架压缩CNN

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643734

Peidong Liu, Weirong Liu, Changhong Shi, Zhiqiang Zhang, Zhijun Li, Jie Liu

{"title":"Compressing CNN by alternating constraint optimization framework","authors":"Peidong Liu, Weirong Liu, Changhong Shi, Zhiqiang Zhang, Zhijun Li, Jie Liu","doi":"10.1117/12.2643734","DOIUrl":"https://doi.org/10.1117/12.2643734","url":null,"abstract":"Tensor decomposition has been extensively studied for convolutional neural networks (CNN) model compression. However, the direct decomposition of an uncompressed model into low-rank form causes unavoidable approximation error due to the lack of low-rank property of a pre-trained model. In this manuscript, a CNN model compression method using alternating constraint optimization framework (ACOF) is proposed. Firstly, ACOF formulates tensor decomposition-based model compression as a constraint optimization problem with low tensor rank constraints. This optimization problem is then solved systematically in an iterative manner using alternating direction method of multipliers (ADMM). During the alternating process, the uncompressed model gradually exhibits low-rank tensor property, and then the approximation error in low-rank tensor decomposition can be negligible. Finally, a high-performance CNN compression network can be effectively obtained by SGD-based fine-tuning. Extensive experimental results on image classification show that ACOF produces the optimal compressed model with high performance and low computational complexity. Notably, ACOF compresses Resnet56 to 28% without accuracy drop, and the compressed model have 1.14% higher accuracy than learning-compression (LC) method.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115606069","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

Comparative analysis of feature-based methods and direct methods for unmanned system’s vision navigation 基于特征的无人系统视觉导航方法与直接导航方法的比较分析

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643020

Chuanqi Cheng, Haiyan Wang, Jie Huang, Jiaxin Lu, Rui Si

引用次数: 0