International Conference on Video and Image Processing最新文献

A multi-focus image fusion method based on nested U-Net 一种基于嵌套U-Net的多焦点图像融合方法

International Conference on Video and Image Processing Pub Date : 2021-12-22 DOI: 10.1145/3511176.3511188

Wangping Zhou, Yuanqing Wu, Hao Wu

引用次数: 1

High-Frequency Feature Learning in Image Super-Resolution with Sub-Pixel Convolutional Neural Network 基于亚像素卷积神经网络的图像超分辨率高频特征学习

International Conference on Video and Image Processing Pub Date : 1900-01-01 DOI: 10.1145/3376067.3376099

Xiao-Yuan Jiang, Xi-Hai Chen

{"title":"High-Frequency Feature Learning in Image Super-Resolution with Sub-Pixel Convolutional Neural Network","authors":"Xiao-Yuan Jiang, Xi-Hai Chen","doi":"10.1145/3376067.3376099","DOIUrl":"https://doi.org/10.1145/3376067.3376099","url":null,"abstract":"Sub-pixel convolutional neural network is efficient for image super-resolution. However, the images generated are relatively smooth. Improving the learning ability of high-frequency features is of great significance for sub-pixel convolutional neural network to get better performance. In the paper, we propose an improved algorithm of sub-pixel convolutional neural network based on high-frequency feature learning for image super-resolution, it optimizes the traditional sub-pixel convolutional structure. Firstly we introduce a residual convolutional layer in the generation net. it assigns the residual factor to each sub-pixel feature map and forces each pixel feature map to adaptively use the input information. Furthermore, a method for high frequency feature mapping is proposed. During image super-resolution training stage, the multi-task learning function, combining the pixel-level loss function with high-frequency contrast loss function, make the generation images getting closer to the target super-resolution images in high-frequency domain. The experiments on CelebA dataset show that our proposed method can effectively improve the quality of super-resolution images by contrast to the traditional sub-pixel convolutional neural network.","PeriodicalId":120826,"journal":{"name":"International Conference on Video and Image Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116358734","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

An Efficient Non-convex Mixture Method for Low-rank Tensor Completion 一种高效的低秩张量补全的非凸混合方法

International Conference on Video and Image Processing Pub Date : 1900-01-01 DOI: 10.1145/3301506.3301516

Chengfei Shi, Li Wan, Zhengdong Huang, Tifan Xiong

引用次数: 0

Vision-Based Analysis for Queue Characteristics and Lane Identification 基于视觉的队列特征分析与车道识别

International Conference on Video and Image Processing Pub Date : 1900-01-01 DOI: 10.1145/3447450.3447474

C. G. V. Ya-On, Jonathan Paul C. Cempron, J. Ilao

引用次数: 1

Vehicle Counting Using Detecting-Tracking Combinations: A Comparative Analysis 使用检测-跟踪组合的车辆计数:比较分析

International Conference on Video and Image Processing Pub Date : 1900-01-01 DOI: 10.1145/3447450.3447458

Ala Alsanabani, Mohammed A. Ahmed, Ahmad Al Smadi

{"title":"Vehicle Counting Using Detecting-Tracking Combinations: A Comparative Analysis","authors":"Ala Alsanabani, Mohammed A. Ahmed, Ahmad Al Smadi","doi":"10.1145/3447450.3447458","DOIUrl":"https://doi.org/10.1145/3447450.3447458","url":null,"abstract":"In light of the rapid progress in building smart cities and smart traffic systems, the need for an accurate and real-time counting vehicles system has become a very urgent need. Finding a robust and accurate counting system is a challenge, as the system must detect, classify and track multi vehicles in complex and dynamic scene situations, different models and classes, and various traffic densities. Several hardware and software systems have emerged for this purpose and their results have varied. In recent years, and due to the great growth in computational capacities and deep learning techniques, deep learning based vehicle counting systems have delivered an impressive performance at low costs. In this study, several state-of-the-art detection and tracking algorithms are studied and combined with each other to render different models. These models are applied in automatic vehicle counting frameworks in traffic videos to assess how accurate are their results against the ground truth. Experiments on these models present the existing challenges that hinder their ability to extract the distinctive object features and thus undermine their efficiency such as problems of occlusion, large scale objects detection, illumination, and various weather conditions. The study revealed that the detectors coupled with the Deep Sort tracker, such as YOLOv4, Detectron2 and CenterNet, achieved the best results compared to the rest of the models.","PeriodicalId":120826,"journal":{"name":"International Conference on Video and Image Processing","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127665499","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: 9