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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

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Deep Learning-Based Multiple Detection Techniques of Covid-19 Disease From Chest X-Ray images Using Advanced image processing Methods and Transfer Learning 1st

Deep Learning-Based Multiple Detection Techniques of Covid-1...

引用

1st Analytics Global conference, AGC 2023

作者： Hussain, Arif Basak, Rohini Mandal, Sourav Department of Informtion Technology Jadavpur University West Bengal Kolkata700098 India School of Computer Scinece and Engineering XIM University Odisha Bhubaneswar752050 India

ISBN: (纸本)9783031508141

The 2019 new coronavirus (COVID-19), which originally surfaced in the Chinese city of Wuhan in December 2019, expanded quickly, and caused a pandemic. It has created a terrible influence on daily lives, public health, and the global economy. To stop the pandemic from spreading further and to treat the affected individuals as soon as feasible, it is essential to identify positive cases as soon as possible. One of the primary issues in the present COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease. This study compares the usage of the most recent Convolutional Neural Network (CNN), a deep learning methodology employing networks, with a transfer learning technique using VGG and ResNet. With their assistance, we created models that can recognize COVID-19 in X-Ray photos. We have applied techniques of image preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE). We built the model by applying CNN. We have also applied transfer learning techniques using visual Geometry Group (VGG16) and Residual Network (ResNet50) after image preprocessing using CLAHE and AHE. We have compared the accuracy of all models. The CLAHE technique along with ResNet50 works well as we achieve an accuracy of 0.99 It can be stated that ResNet50 with CLAHE played a very significant role in determining the covid-19 in X-ray images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Security and Forensics Exploration of Learning-based image Coding

Security and Forensics Exploration of Learning-based Image C...

引用

IEEE visual communications and image processing (VCIP)

作者： Deepayan Bhowmik Mohamed Elawady Keiller Nogueira Division of Computing Science & Mathematics University of Stirling Stirling UK

ISBN: (纸本)9781728173221

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.

关键词： Deep learning Video coding image coding Codecs visual communication Forensics Transform coding

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A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology 10th

A 3D MRI Brain Image Segmentation and Reconstruction System ...

引用

10th China Health Information processing conference, CHIP 2024

作者： Zhao, Wang Lu, Peixin Hu, LianTing Lu, Long School of Management Engineering Henan University of Engineering Zhengzhou China School of Safety Science and Emergency Management Wuhan University of Technology Wuhan China Guangdong Provincial People’s Hospital Guangzhou China School of Information Management Wuhan University Wuhan China The Center for Healthcare Big Data Research The Big Data Institute Wuhan University Wuhan China Institute of Pediatrics Guangzhou Women and Children’s Medical Center Guangzhou Medical University Guangzhou China School of Public Health Wuhan University Wuhan China

ISBN: (纸本)9789819637546

Background With the rapid development of information technology and the digitization of medical devices, various diseases require the use of medical imaging equipment for diagnosis. At present, various medical imaging diagnostic equipment such as CT and nuclear magnetic resonance can provide two-dimensional planar images of diseases. Doctors urgently need to accurately determine the spatial location, size, geometry, and spatial relationship with the surrounding tissue. Therefore, it is very important to use computer technology to segment 3D MRI images, determine the location of lesions, and then perform 3D reconstruction. Method At present, automatic recognition and marking of brain images are displayed in two dimensions. Therefore, it is necessary to use 3D visualization technology for reconstruction. In addition, it can be combined with virtual and real, and some additional information is superimposed on the brain image for integrated display. In addition, a combination of virtual and real needs to be superimposed, and some additional information is superimposed on the brain image for integrated display. The research focus of this paper includes two main parts: disease segmentation and 3D reconstruction visualization. Firstly, the disease segmentation method based on 3D MRI brain image files was designed, and then the feature extraction and 3D reconstruction functions were designed. Thereby forming a complete process of disease region segmentation and three-dimensional reconstruction. Results This study is based on a three-dimensional MRI brain image segmentation algorithm. The algorithm is advanced in technology, high in accuracy, and can effectively identify the location of the disease. Then, this study used the Unity tool to implement a three-dimensional reconstruction and visual display program for brain image disease segmentation. Therefore, the doctor can quickly and intuitively grasp the spatial information inside the brain and the information of the lesion

关键词： image segmentation

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Multicomponent Secondary Transform

Multicomponent Secondary Transform

引用

IEEE visual communications and image processing (VCIP)

作者： Madhu P Krishnan Xin Zhao Shan Liu Tencent America Palo Alto CA United States

ISBN: (纸本)9781728173221

The Alliance for Open Media has recently initiated coding tool exploration activities towards the next-generation video coding beyond AV1. In this regard, a frequency-domain coding tool, which is designed to leverage the cross-component correlation existing between collocated chroma blocks, is explored in this paper. The tool, henceforth known as multi-component secondary transform (MCST), is implemented as a low complexity secondary transform with primary transform coefficients of multiple color components as input. The proposed tool is implemented and tested on top of libaom. Experimental results show that, compared to libaom, the proposed method achieves an average 0.34% to 0.44% overall coding efficiency for All Intra (AI) coding configuration for a wide range of video content.

关键词： Video coding image coding Correlation image color analysis visual communication Frequency-domain analysis Transforms

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AnomalyHop: An SSL-based image Anomaly Localization Method

AnomalyHop: An SSL-based Image Anomaly Localization Method

引用

IEEE visual communications and image processing (VCIP)

作者： Kaitai Zhang Bin Wang Wei Wang Fahad Sohrab Moncef Gabbouj C.-C. Jay Kuo University of Southern California Los Angeles USA Tampere University Tampere Finland

ISBN: (纸本)9781728173221

An image anomaly localization method based on the successive subspace learning (SSL) framework, called Anomaly-Hop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.

关键词： Location awareness Deep learning visual communication Neural networks Benchmark testing Feature extraction Mathematical models

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Deformable Convolution Based No-Reference Stereoscopic image Quality Assessment Considering visual Feedback Mechanism

Deformable Convolution Based No-Reference Stereoscopic Image...

引用

IEEE visual communications and image processing (VCIP)

作者： Mingyue Zhou Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

Simulation of human visual system (HVS) is very crucial for fitting human perception and improving assessment performance in stereoscopic image quality assessment (SIQA). In this paper, a no-reference SIQA method considering feedback mechanism and orientation selectivity of HVS is proposed. In HVS, feedback connections are indispensable during the process of human perception, which has not been studied in the existing SIQA models. Therefore, we design a new feedback module (FBM) to realize the guidance of the high-level region of visual cortex to the low-level region. In addition, given the orientation selectivity of primary visual cortex cells, a deformable feature extraction block is explored to simulate it, and the block can adaptively select the regions of interest. Meanwhile, retinal ganglion cells (RGCs) with different receptive fields have different sensitivities to objects of different sizes in the image. So a new multi receptive fields information extraction and fusion manner is realized in the network structure. Experimental results show that the proposed model is superior to the state-of-the-art no-reference SIQA methods and has excellent generalization ability.

关键词： image quality Deformable models visualization Adaptation models Sensitivity Convolution visual communication

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Urban Planter: A Web App for Automatic Classification of Urban Plants

Urban Planter: A Web App for Automatic Classification of Urb...

引用

IEEE visual communications and image processing (VCIP)

作者： Sarit Divekar Irina Rabaev Marina Litvak Shamoon College of Engineering Israel

ISBN: (纸本)9781728173221

Plant classification requires an expert because subtle differences in leaves or petal forms might differentiate between different species. On the contrary, some species are characterized by high variability in appearance. This paper introduces a web app for assisting people in identifying plants for discovering the best growing methods. The uploaded picture is submitted to the back-end server, and a pre-trained neural network classifies it to one of the predefined classes. The classification label and confidence are displayed to the end user on the front-end page. The application focuses on the house and garden plant species that can be grown mainly in a desert climate and are not covered by existing datasets. For training a model, we collected the Urban Planter dataset. The installation code of the alpha version and the demo video of the app can be found on https://***/UrbanPlanter/urbanplanterapp.

关键词： Training Codes visual communication image processing Neural networks Servers Meteorology

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Portable Congenital Glaucoma Detection System

Portable Congenital Glaucoma Detection System

引用

IEEE visual communications and image processing (VCIP)

作者： Chunjun Hua Menghan Hu Yue Wu East China Normal University Shanghai China Ninth People's Hospital Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

Congenital glaucoma is an eye disease caused by embryonic developmental disorders, which damages the optic nerve. In this demo paper, we proposed a portable non-contact congenital glaucoma detection system, which can evaluate the condition of children's eyes by measuring the cornea size using the developed mobile application. The system consists of two modules viz. cornea identification module and diagnosis module. This system can be utilized by everyone with a smartphone, which is of wider application. It can be used as a convenient home self-examination tool for children in the large-scale screening of congenital glaucoma. The demo video of the proposed detection system is available at: https://***/10.6084/***.14728854.v1.

关键词： Cornea visual communication image processing Size measurement Optical imaging Mobile applications Diseases

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Inter-Observer visual Congruency in Video-Viewing

Inter-Observer Visual Congruency in Video-Viewing

引用

IEEE visual communications and image processing (VCIP)

作者： Jiaomin Yue Qiang Lu Dandan Zhu Xiongkuo Min Xiao-Ping Zhang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Ryerson University Canada

ISBN: (纸本)9781728173221

There are individual differences in human visual attention between observers when viewing the same scene. Inter-observer visual congruency (IOVC) describes the dispersion between different people's visual attention areas when they observe the same stimulus. Research on the IOVC of video is interesting but lacking. In this paper, we first introduce the measurement to calculate the IOVC of video. And an eye-tracking experiment is conducted in a realistic movie-watching environment to establish a movie scene dataset. Then we propose a method to predict the IOVC of video, which employs a dual-channel network to extract and integrate content and optical flow features. The effectiveness of the proposed prediction model is validated on our dataset. And the correlation between inter-observer congruency and video emotion is analyzed.

关键词： visualization Correlation visual communication Predictive models Observers Motion pictures Feature extraction

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Video Compression based on Jointly Learned Down-Sampling and Super-Resolution Networks

Video Compression based on Jointly Learned Down-Sampling and...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuzhuo Wei Li Chen Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

With the blooming of deep learning technology in computer vision, the integration of deep learning and the traditional video coding has made significant improvements, especially applying the super-resolution neural network as the post-processing module in the down-sampling-based video compression framework. However, the pre-processing module lacks back-propagated gradients for jointly considering down-sampling and up-sampling due to the non-differentiability of the traditional video codec. In this paper, we propose an end- to-end down-sampling-based video compression framework applying convolutional neural networks both as down-sampling and upsampling. We use a virtual codec neural network to approximate the actual video codec so that the gradient can be effectively back-propagated for joint training. Experimental results show the superiority of our proposed framework compared with the predefined down-sampling-based video compression and various methods of joint training.

关键词： Training Deep learning Video coding image coding visual communication Superresolution Neural networks

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