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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是261-270 订阅

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CT image Recognition of COVID-19 Based on Improved Resnet Model

CT Image Recognition of COVID-19 Based on Improved Resnet Mo...

引用

image processing and computer applications (icipca), ieee international conference on

作者： Fuqiang Jiang East China University of Science and Technology Shanghai China

In order to detect COVID-19 positive cases more quickly, in this paper, a deep learning model is used and a CNN network is designed for model training. In addition, some classical networks such as Resnet, VGG and some lightweight networks are tried to train the model and analyze their advantages and disadvantages. Finally, a Resnet network is designed. The Fire module is introduced to reduce the network depth and parameter quantity and make the network pay attention to the global information. The performance of the model on the three loss functions of CrossEntropyLoss, FocalLoss and Poly1FocalLoss is compared, and the optimal loss function is CrossEntropyLoss. The network model has achieved good results in both the training set and the verification set. The accuracy in the training set is 95%, the average accuracy in the verification set is about 80%, and the accuracy in the test set is nearly 75%. Because the model can be trained under the CPU, compared with the 84% accuracy of the original author's DenseNet test set, the precision has slightly decreased, and the recall rate has increased by 4 percentage points. However, in comparison, the speed and accuracy of the improved model have been improved under the Resnet 34 model.

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Scaling Neural Face Synthesis to High FPS and Low Latency by Neural Caching 23

Scaling Neural Face Synthesis to High FPS and Low Latency by...

引用

23rd ieee/CVF Winter conference on applications of computer Vision (WACV)

作者： Yu, Frank Fels, Sid Rhodin, Helge Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781665493468

Recent neural rendering approaches greatly improve image quality, reaching near photorealism. However, the underlying neural networks have high runtime, precluding telepresence and virtual reality applications that require high resolution at low latency. The sequential dependency of layers in deep networks makes their optimization difficult. We break this dependency by caching information from the previous frame to speed up the processing of the current one with an implicit warp. The warping with a shallow network reduces latency and the caching operations can further be parallelized to improve the frame rate. In contrast to existing temporal neural networks, ours is tailored for the task of rendering novel views of faces by conditioning on the change of the underlying surface mesh. We test the approach on view-dependent rendering of 3D portrait avatars, as needed for telepresence, on established benchmark sequences. Warping reduces latency by 70% (from 49.4ms to 14.9ms on commodity GPUs) and scales frame rates accordingly over multiple GPUs while reducing image quality by only 1%, making it suitable as part of end-to-end view-dependent 3D teleconferencing applications.

关键词： image quality Teleconferencing Three-dimensional displays Telepresence Runtime Neural networks Rendering (computer graphics)

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image Defogging Based on Joint Contrast Enhancement and Multi-scale Fusion 3

Image Defogging Based on Joint Contrast Enhancement and Mult...

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3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Nie, Junjie Wu, Haotian Yao, Shuyang Zhejiang University of Science and Technology Zhejiang Hangzhou China Southwest Jiaotong University Chengdu China Harbin Engineering University Harbin China

ISBN: (纸本)9781665472784

Although the single image dehazing techniques now in use work well in certain circumstances, real-world scenarios' complex and ever-changing nature makes it impossible for them to function well. This research offers a contrast enhancement and exposure fusion-based efficient paradigm for picture dehazing. Three basic preprocessing techniques are utilized to create intermediate pictures for contrast enhancement. The first processing method is gamma correction (GC), employed to improve an image's local quality. The second technique is the newly developed color-preserving histogram equalization, which combines each channel to enhance the overall contrast. The third kind of processing is called ACE, which uses adaptability to deal with a complex and shifting environment. This paper fuses the input from GC, CPAHE, and ACE during the fusion stage using the adaptive kernel size fusion technique based on rapid structural patch decomposition. According to the experimental findings, it outperforms other cutting-edge visual and quantitative analysis techniques. The average FADE scores for the daylight and night-time scenes have increased by at least 0.09 and 0.08, respectively. This paper also describes how this strategy may be used in various contexts. © 2023 ieee.

关键词： image enhancement

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Shatter and Gather: Learning Referring image Segmentation with Text Supervision

Shatter and Gather: Learning Referring Image Segmentation wi...

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Kim, Dongwon Kim, Namyup Lan, Cuiling Kwak, Suha POSTECH Pohang South Korea Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350307184

Referring image segmentation, the task of segmenting any arbitrary entities described in free-form texts, opens up a variety of vision applications. However, manual labeling of training data for this task is prohibitively costly, leading to lack of labeled data for training. We address this issue by a weakly supervised learning approach using text descriptions of training images as the only source of supervision. To this end, we first present a new model that discovers semantic entities in input image and then combines such entities relevant to text query to predict the mask of the referent. We also present a new loss function that allows the model to be trained without any further supervision. Our method was evaluated on four public benchmarks for referring image segmentation, where it clearly outperformed the existing method for the same task and recent open-vocabulary segmentation models on all the benchmarks.

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Thorax Disease Classification Based on the Convolutional Network SqueezeNet 12

Thorax Disease Classification Based on the Convolutional Net...

引用

12th ieee international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications, IDAACS 2023

作者： Avanzato, Roberta Beritelli, Francesco University of Catania Dieei Catania95125 Italy

ISBN: (纸本)9798350358056

Chest radiography allows a detailed inspection of a patient's thorax via an imaging modality, but requires specialized training for proper interpretation. With the advent of high performance general purpose image analysis and computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. In this paper, we propose a classification algorithm distinguishing between chest radiographs performed on people with or without lung disease. The dataset used as the input to the SqueezeNet neural network is Chest X-ray. The only pre-processing phase applied to the dataset is the resize to 224×224 and the conversion to grayscale. The accuracy percentage obtained during the network testing phase is 94%. © 2023 ieee.

关键词： computer vision Convolutional neural network deep learning healthcare image processing

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DRAW: Defending Camera-shooted RAW against image Manipulation

DRAW: Defending Camera-shooted RAW against Image Manipulatio...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Hu, Xiaoxiao Ying, Qichao Qian, Zhenxing Li, Sheng Zhang, Xinpeng Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Key Lab Culture & Tourism Intelligent Comp Shanghai Peoples R China

ISBN: (纸本)9798350307184

RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through image Signal processing (ISP) pipelines. Nowadays, digital images are risky of being nefariously manipulated. Inspired by the fact that innate immunity is the first line of body defense, we propose DRAW, a novel scheme of defending images against manipulation by protecting their sources, i.e., camera-shooted RAWs. Specifically, we design a lightweight Multi-frequency Partial Fusion Network (MPF-Net) friendly to devices with limited computing resources by frequency learning and partial feature fusion. It introduces invisible watermarks as protective signal into the RAW data. The protection capability can not only be transferred into the rendered RGB images regardless of the applied ISP pipeline, but also is resilient to post-processing operations such as blurring or compression. Once the image is manipulated, we can accurately identify the forged areas with a localization network. Extensive experiments on several famous RAW datasets, e.g., RAISE, FiveK and SIDD, indicate the effectiveness of our method. We hope that this technique can be used in future cameras as an option for image protection, which could effectively restrict image manipulation at the source.

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Biomedical image processing Workflow using Segmentation 11

Biomedical Image Processing Workflow using Segmentation

引用

11th international Scientific conference on computer Science, COMSCI 2023

作者： Vetova, Stella Lazarova, Milena Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

The following paper presents a workflow for biomedical image processing. On the base of accomplished analysis the software for biological and biomedical image processing Cell Profiler is used. The designed workflow is organized in eight phases. It has the options to perform segmentation of the objects in the images and to apply a mask on the inverted greyscale images of the test database. The experiments are graphically presented showing the final result of the image processing workflow. © 2023 ieee.

关键词： image segmentation

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Uncovering Water Research with Natural Language processing 47

Uncovering Water Research with Natural Language Processing

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47th ieee-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Ceh-Varela, Edgar Imhmed, Essa Eastern Nell Mexico Univ Dept Math Sci Portales NM USA

ISBN: (纸本)9798350326970

In order to address current water challenges, scientific research on water-related issues is crucial. However, traditional techniques for selecting research topics, such as literature reviews and expert opinions, can be time-consuming and may not provide a comprehensive overview of available information. We propose using Natural Language processing (NLP) techniques in this study to extract, align, and compare water research topics from different corpora. We apply these techniques to the research paper abstracts from the New Mexico Water Resources Research Institute (NMWRRI) and the U.S. Geological Survey (USGS) to assess these institutions' current research interests and identify potential new research directions. We use a Latent Dirichlet Allocation (LDA) model for topic extraction and a Word2Vec model for topic alignment. This study highlights the benefits of using NLP techniques to analyze trends and identify novel research directions in water studies.

关键词： water research natural language processing topic modeling LDA Word2Vec

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Multi-model image matching based on feature map matching method 8

Multi-model image matching based on feature map matching met...

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8th international conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Qi, Wei Lang, Wenhui School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9798350302455

In the field of remote sensing, multimodal data matching is a problem with great challenges, and the matching between SAR and visible light images is tremendously difficult, because there is obvious speckle noise in SAR images, and the imaging methods of SAR images and optical images are very different. At present, although lots of excellent results have been fulfilled in multimodal image matching, it is difficult to meet the rapid and even real-time processing requirements of massive remote sensing data such as disaster emergency and military applications due to the complexity of the matching task. In view of the above problems, this paper proposes a feature map matching method based on the deep convolution neural network model of the pseudo-Siam architecture, which can quickly accomplish the multimodal image matching. © 2023 ieee.

关键词： image matching

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A Review on 3D Convolutional Neural Network 3

A Review on 3D Convolutional Neural Network

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3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Wang, Chuqi McGill University Department of Mathematics and Statistics MontréalQCH3A 0B9 Canada

ISBN: (纸本)9781665472784

The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first proposed to solve image classification problems at first. With advancements in science and technology, as well as the growth of the internet, tasks involving video analysis have received increased attention in recent years. As a result, it was suggested that a 3-dimensions convolutional neural network (3D CNN) be applied to tackle 3D image processing or video analysis. 3D CNN is primarily used in the field of human activity recognition, video classification, medical imaging detection, and so on. This paper offers a succinct introduction to the general architecture and representative models of 3D CNN, contrasts the variations between 2D CNN and 3D CNN, explores the widely used models derived from 3D CNN, and displays the application outcomes of 3D CNN. © 2023 ieee.

关键词： image classification

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