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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 4813 条记录，以下是311-320 订阅

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Depth image Inpainting Algorithm Based on Improved Non-Local Means Filtering 5

Depth Image Inpainting Algorithm Based on Improved Non-Local...

引用

5th international conference on computer Engineering and Application (ICCEA)

作者： Chen, Jian Zhang, Jianfeng Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Shaanxi Engn Res Ctr Agr Informat Intelligent Per Yangling 712100 Shaanxi Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Depth images captured by depth cameras often suffer from issues such as holes and noise, which can have adverse effects on subsequent practical applications, including the accuracy of 3D reconstruction. To address these issues and enhance the quality of depth images, we propose depth image restoration algorithm based on improved non-local means (NLM) filtering. Initially, the algorithm utilizes fuzzy C-means (FCM) clustering to cluster the color image and generate a guidance image. Subsequently, neighborhoods are defined centered around the hole points in the depth image. A weight function for NLM filtering is then constructed using bilateral filtering. By calculating the weighted average of all non-hole points within the neighborhood, the holes are repaired. Finally, median filtering is utilized to smoothen and reduce noise in the depth image. Experimental findings show that the proposed algorithm efficiently repairs holes while preserving the edge details of image.

关键词： depth images image restoration non-local means filtering weight function

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Low-Light image Enhancement Based on Local Contrast Denoising and Fusion 5

Low-Light Image Enhancement Based on Local Contrast Denoisin...

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2023 5th international conference on Artificial Intelligence and computer applications, ICAICA 2023

作者： Zhang, Guiwen Kong, Qun Wang, Fuxiang Fang, Li Yu, Wenyue Xiao, Yingjie School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350323313

The purpose of low-light image enhancement is to improve the clarity of objects in low-light environments to facilitate the recognition and detection of targets later. Local Contrast Denoising and Fusion Network (LCDFN) is constructed to address the problem of poor contrast and severe noise in low light images. images obtained in low-light environments have low contrast in the image as a whole, but usually some of the edge regions of the image have larger contrast due to the presence of the light source, and enhancement processing of low-light images often results in over-enhancement of the region, generating noise. In order to suppress the noise, firstly, determines the region with larger contrast by local inspection of low-light/bright-light image pairs and suppresses the region's weighting value;second, multiple denoising is performed on the low-light image by changing the coefficients of the weight map;then, the multiple denoising results are fused;finally, the color loss function is improved, and the luminance loss is added to the color loss function, so that the LCDFN network can better inhibit the generation of noise. The experimental results show that LCDFN has a better effect on low-light image enhancement. © 2023 ieee.

关键词： image enhancement

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Point-Query Quadtree for Crowd Counting, Localization, and More

Point-Query Quadtree for Crowd Counting, Localization, and M...

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Liu, Chengxin Lu, Hao Cao, Zhiguo Liu, Tongliang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350307184

We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are crowd and where they locate. The querying processing, however, raises an underlying problem on the number of necessary querying points. Too few imply underestimation;too many increase computational overhead. To address this dilemma, we introduce a decomposable structure, i.e., the point-query quadtree, and propose a new counting model, termed Point quEry Transformer (PET). PET implements decomposable point querying via data-dependent quadtree splitting, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions. Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable. We demonstrate the applications of PET on a number of crowd-related tasks, including fully-supervised crowd counting and localization, partial annotation learning, and point annotation refinement, and also report state-of-the-art performance. For the first time, we show that a single counting model can address multiple crowd-related tasks across different learning paradigms. Code is available at https://***/cxliu0/PET.

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Deep Learning-Based image Style Migration Technique and Art Design 2

Deep Learning-Based Image Style Migration Technique and Art ...

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2nd international conference on Algorithm, image processing and Machine Vision, AIPMV 2024

作者： Deng, Kaida Institute of Information Engineering Huizhou Engineering Vocational College Guangdong Huizhou516000 China

ISBN: (纸本)9798350390254

In this paper, we adopt image style migration technique based on deep learning, use Vgg19 network for content and style feature extraction, combine an image with art design style, and realise the generation of Van Gogh style Eiffel Tower image. The style conversion effect is evaluated by content loss and style loss calculation, and the values of J_content and J_style show an overall decreasing trend, indicating that the model works well and the output image gradually completes the style migration. This technique is of great significance in the field of computer vision and artificial intelligence, and brings new creative possibilities to the field of art and design, expanding the boundaries of image processing and art and design. © 2024 ieee.

关键词： Contrastive Learning

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Compressor using Cadence 180nm for image processing applications 12

Compressor using Cadence 180nm for Image Processing Applicat...

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12th ieee international conference on Communication Systems and Network Technologies, CSNT 2023

作者： Reddy, P Tharun Kumar Subramanyam, Radha Kartheek, M Lakshman Melvin, C. Nagabushanam, P. Kiran, P Sri Department of Ece Karunya Institute of Technology and SciencesCbeIN India Department of Ece Chaitanya Bharathi Institute of Technology Telangana Hyd India

ISBN: (纸本)9781665462617

Magnetic Compressors play major role in image processing and microwave applications. AND-OR complex compound gates and XOR-XNOR modules are used to design compressor to achieve low power consumption and less hardware complexity using cadence 180nm technology. The waveforms of the compressor are analyzed for its better logical performance over conventional compressors. © 2023 ieee.

关键词： Compressors

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Separated Frequency Loss with Distinguished Weight for images Generation

Separated Frequency Loss with Distinguished Weight for Image...

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2023 international conference on computer applications Technology, CCAT 2023

作者： Wei, Jian Wang, Qinzhao Army Academy of Armored Force Department of Weaponry and Control Beijing China

ISBN: (纸本)9798350301540

Deep neural network models are more and more widely used in image reconstruction and generation tasks. By setting various loss functions, the model adaptively generates images that meet the corresponding constraints, which has a positive effect on improving image quality and enriching diversity. As an important image processing method, two-dimensional Fourier transform is widely used for image filtering and denoising, but its role has not been further explored. Based on the background of the fine-grained target image reconstruction task, this paper proposes a differentiated weight loss function based on frequency domain space in view of the problem that the global loss function of fixed constraints in the past brings great bias to model parameter update. The distinguishing regional weight coefficient is stetted by separating the high and low frequency regions, thus, to alleviate the high-frequency information introduced by the fine-grained target due to the diverse changes of individual morphology and environmental background. The model parameter update process gains much smoother with the guidance of separated frequency loss. Experimental results show that by setting the differentiated frequency domain region weight loss function, the model generates higher quality images in both image reconstruction and generation tasks. © 2023 ieee.

关键词： image reconstruction

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Blind image Separation Using Composite Criteria based on Statistical Information and Sparse Representation 12

Blind Image Separation Using Composite Criteria based on Sta...

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12th international conference on image processing Theory, Tools and applications, IPTA 2023

作者： Boudjenouia, Fouad Abed-Meraim, Karim Chetouani, Aladine Jennane, Rachid Polytechnic Military School Algiers Algeria University of Orléans Prisme Laboratory Orléans France University of Orléans Idp Institute UMR-CNRS 7013 Orléans France

ISBN: (纸本)9798350325416

This paper deals with blind image separation by exploiting the statistical characteristics of the mixtures (information related to the sources independence) with the sparsity of the signals. More precisely, we investigate and compare the gain that can be reached by considering two different signal properties, namely the statistical independence and the sparse representation. These properties are considered in a 'dual' as well as in a 'hybrid' mode. Both approaches are compared, simulation and experimental results demonstrate the efficiency of the proposed methods. © 2023 ieee.

关键词： Blind source separation

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LEARNING SPATIALLY-ADAPTIVE STYLE-MODULATION NETWORKS FOR SINGLE image SYNTHESIS 30

LEARNING SPATIALLY-ADAPTIVE STYLE-MODULATION NETWORKS FOR SI...

引用

30th ieee international conference on image processing (ICIP)

作者： Shen, Jianghao Wu, Tianfu North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781728198354

Recently there has been a growing interest in learning generative models from a single image. This task is important as in many real world applications, collecting large dataset is not feasible. Existing work like SinGAN is able to synthesize novel images that resemble the patch distribution of the training image. However, SinGAN cannot learn high level semantics of the image, and thus their synthesized samples tend to have unrealistic spatial layouts. To address this issue, this paper proposes a spatially adaptive style-modulation (SASM) module that learns to preserve realistic spatial configuration of images. Specifically, it extracts style vector (in the form of channel-wise attention) and latent spatial mask (in the form of spatial attention) from a coarse level feature separately. The style vector and spatial mask are then aggregated to modulate features of deeper layers. The disentangled modulation of spatial and style attributes enables the model to preserve the spatial structure of the image without overfitting. Experimental results show that the proposed module learns to generate samples with better fidelity than prior works.

关键词： single image generative learning spatially-adaptive

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Design and Realization of Intelligent image Recognition System Based on computer 1

Design and Realization of Intelligent Image Recognition Syst...

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1st international conference on computer Graphics and image processing, CGIP 2023

作者： Pan, Jiheng Liaoning Urban Construction Technical College Liaoning Shenyang China

ISBN: (纸本)9798350333183

The update of modern intelligent technology has driven the progress of computer related technology. The tide of technological revolution is impacting the economic system all over the world. The emergence of information technology has helped the intelligent information industry break through the bottleneck period of traditional development. computer image intelligent recognition technology is gradually mature. This form puts forward higher requirements for China's economic construction. On this basis, the design of image recognition system has gradually become the main research object of experts. This paper explains the relevant theories of image recognition technology. Finally, the design and implementation of image recognition system are proposed. © 2023 ieee.

关键词： image recognition

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Knowledge Graph Generation for Unstructured Data Using Data processing Pipeline 47

Knowledge Graph Generation for Unstructured Data Using Data ...

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

作者： Sukumar, Sushmi Thushara Lung, Chung-Horng Zaman, Marzia Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Cistel Technol R&D Nepean ON Canada

ISBN: (纸本)9798350326970

The proliferation of technologies and unstructured data on the internet poses a persistent challenge in extracting valuable information from diverse formats. To address this, research leverages Machine Learning (ML) and Natural Language processing (NLP) techniques. This study contributes to information extraction from unstructured text using a stateof-the-art pipeline, incorporating modules for coreference resolution (Neuralcoref), named entity linking (Wikifier API), and Relationship Extraction (RE) (OpenNRE and REBEL models). The resulting Knowledge Graph (KG) in Neo4j captures entity relationships. Experiments on a BBC news dataset analyzed the pipeline's performance, focusing on RE. Accuracies of 61.4% (OpenNRE) and 87% (REBEL) were achieved. The research demonstrates the efficacy of the proposed pipeline in extracting structured knowledge from unstructured data, facilitating the preservation and utilization of valuable information.

关键词： Machine Learning Data Analytics Unstructured Data Graph Database Coreference resolution Named Entity Linking Relation Extraction Knowledge Graph Neo4j

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