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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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A CNN-Based Framework for Underwater Image Noise Reduction and visibility Enhancement in Marine Applications

A CNN-Based Framework for Underwater Image Noise Reduction a...

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International conference on Computer and Communications Technologies (ICCCT)

作者： Ashok P Ragul Sabarish J vigneash R v Wasiq Rahman M A Antony vijay J S. Kaliappan Dept of Computer Science and Engineering Sri Sairam Institute of Technology Chennai Dept of Computer Science and Engineering Karpagam College of Engineering Coimbatore Division of Research and Development Lovely Professional University Jalandhar

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Underwater imaging technology serves critical functions in diverse fields that range from marine biological research to underwater archaeological investigations to oceans exploration endeavors. The underwater imaging process typically produces highly degraded visual output due to multiple factors including light absorption and scattering while also affecting clarity because of particulate matter. We propose a Convolutional Neural Network (CNN)-based technique for underwater image noise reduction which works to enhance image visibility while improving perceptual data. Our system applies deep learning principles to master noise patterns while recovering image clarity. The proposed model trained using underwater images with different levels of noise underwent evaluation on the same dataset. Subjective visual assessment together with objective metrics demonstrated that the CNN delivered noise reduction performance at an accuracy range between 85-90% which defeated conventional noise filtering methods. The developed methodology exhibits substantial progress potential for seabed image quality enhancement tools which subsequently results in better underwater cognitive interpretation processes.

关键词： Image quality Deep learning visualization Recurrent neural networks Image color analysis Noise reduction Scattering Biology Convolutional neural networks Image enhancement

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An In-Depth exploration of Image Captioning Training Approaches and Performance analysis

An In-Depth Exploration of Image Captioning Training Approac...

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International conference for Convergence of Technology (I2CT)

作者： K Kushal Madhav Manoj Kevansh Reddy Priyanka C Nair Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the rapidly evolving landscape of computer vision, image captioning has emerged as a challenging task. This report explores the techniques involved in image captioning using deep learning techniques, especially the encoder-decoder framework. Models such as CNN-LSTM, CNN-GRU,Xception – YOLO v4, GIT Based Model are used. Recognition is given to the significance of ample and well-annotated datasets in teaching the algorithms to understand the complex relationships between visual elements and textual descriptions. Along with traditional evaluation metrics like BLEU score, this study also employs metrics such as METEOR, ROUGE-L, and SPICE to compare performance between models. The findings highlight on the impact of deep learning in enabling computers to generate captions for diverse visual content.

关键词： Measurement Deep learning YOLO Training visualization Computational modeling SPICE

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USING REORDERABLE HEATMAPS AS A SUPPORT FOR data ASSIMILATION IN PETROLEUM RESERvOIR MANAGEMENT AND DEvELOPMENT PROCESSES 16

USING REORDERABLE HEATMAPS AS A SUPPORT FOR DATA ASSIMILATIO...

引用

16th International conference on Computer Graphics, visualization, Computer vision and Image Processing, CGvCvIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big data Analytics, data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Turato, Gabriel Paulo de Oliveira, João Gabriel Nogueira Duarte Santos, Antonio Alberto S. Schiozer, Denis José da Silva, Celmar Guimarães School of Technology University of Campinas São Paulo Limeira Brazil Center for Petroleum Studies University of Campinas São Paulo Campinas Brazil School of Mechanical Engineering University of Campinas São Paulo Campinas Brazil

ISBN: (纸本)9789898704429

Petroleum exploration activities demand the creation of lots of models to represent the uncertainties of a given reservoir, aiming to guide decision-making regarding exploration strategies. As part of these activities, a data assimilation process may refine the model set and discard models that do not honor the history data of the reservoir, given an error tolerance level. Reservoir engineers may use visualizations to analyze the partial results of iterative data assimilation processes and make decisions such as accepting the models selected in an iteration or refining the model set. Among a set of available visualizations for this process, we did not find in the literature the use of interactive heatmaps based on reorderable matrices. In this paper, we propose to add this kind of visualization to the analysis toolset for data assimilation, aiming to help engineers to better overview data assimilation datasets and how their models honor the history data of a reservoir. The proposed visual mappings use a set of heatmaps that enable the engineers to analyze a misfit measure (NQDS) for a given iteration, regarding models, wells, and their dynamic attributes. Users may reorder and filter these heatmaps according to their needs. We implemented our proposal in a web-based prototype that was submitted to a preliminary evaluation with real users in a synthetic scenario. This evaluation revealed positive opinions about the potential of the tool, which may be used to complement other approaches, and provided some opportunities for improvement. © MCCSIS *** rights reserved.

关键词： visualization

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exploration of Spatial and Temporal Modeling Alternatives for HOI

Exploration of Spatial and Temporal Modeling Alternatives fo...

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IEEE Winter conference on Applications of Computer vision (WACv)

作者： Dabral, Rishabh Sarkar, Srijon Reddy, Sai Praneeth Ramakrishnan, Ganesh Indian Inst Technol Dept Comp Sci Mumbai Maharashtra India

ISBN: (纸本)9780738142661

Human-Object Interaction detection from a video clip can be considered as a special case of video-based visual-Relationship Detection wherein the subject must be a human. Specifically, it involves detecting the humans and objects in the clip as well as the interactions between them. Conventionally, the problem has been formulated as a space-time graph inference problem over the video clip features. In this work, we explore alternate spatial approaches for detecting Human-Object Interactions. We consider a hierarchical setup that decouples spatial and temporal aspects of the problem and analyse the impacts of a variety of design choices for the spatial networks. Particularly, to capture spatial relationships in the scene, we analyze the effectiveness of the traditionally used Graph Convolutional Networks against Convolutional Networks and Capsule Networks. Unlike current approaches, we avoid using ground truth data like depth maps or 3D human pose during inference, thus increasing generalization across non-RGBD datasets as well. We demonstrate a comprehensive analysis of the exploration, both quantitatively and qualitatively, while achieving state-of-the-art results in human-object interaction detection (88.9% and 92.6%) and anticipation tasks of CAD-120 and competitive results on image based HOI detection in v-COCO dataset, setting a new benchmark for visual features based approaches.

关键词： visualization Computer vision Three-dimensional displays conferences Pipelines Benchmark testing Feature extraction

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exploration on 3D Geological Modeling Technology Based on BIM Secondary Development - Taking Liangshuijing Tunnel as an Example 22

Exploration on 3D Geological Modeling Technology Based on BI...

引用

2022 International conference on Computational Infrastructure and Urban Planning, CIUP 2022

作者： Wang, Yan Jun Li, Rui Yang, Zhu Tan, Zhuoyue Xu, Zhong College of Environment and Civil Engineering Chengdu University of Technology China

ISBN: (纸本)9781450396363

Although the existing 3D geological software has great advantages in spatial geographical analysis, it still has some weaknesses such as complex modeling program. On this basis, 3D geological modeling technology was explored herein. Firstly, the representation method of 3D geological model and the data processing method of geological modeling are discussed;secondly, aiming at the problems of low modeling accuracy and little information data, Delaunay Triangulation Algorithm is applied to establish the correlation between 3D geological model and data;the theoretical basis and application skills for supplementing the measurement data by Kriging Interpolation Method are explored;finally, by using Dynamo visual programming plug-in, the topographic curved surface, unstratified 3D geological entity and the stratified 3D geological entity have been generated successively, the 3D geological modeling platform based on Revit has been improved, and the 3D geological model of the stratified structure has been quickly created and has been applied to Liangshuijing Tunnel Engineering Project. © 2022 ACM.

关键词： Interpolation

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Hacking the Human vasculature 15

Hacking the Human Vasculature

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pudumalar, S. Balasoundarya, v. vathana, Padma S.T. vallimayil, M. Department of Information Technology Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331300579

The human vasculature is a complex network of blood vessels, tubules, and capillaries essential for transporting oxygen and nutrients to all parts of the *** the structure and function of the vasculature is critical for diagnosing and treating a wide range of diseases, including cancer, cardiovascular disease, and neurological *** study of cutting-edge technologies has been prompted by the search for accuracy and efficiency in the field of medical imaging and *** YOLOv8 model, a cutting-edge object recognition method, is used in this abstract to explore the novel strategy of hacking the human *** technology offers advances in comprehending and diagnosing vascular-related disorders by concentrating primarily on mapping blood tissues and glomerulus within the complex network of blood *** You Only Look Once (YOLO) architecture has evolved with the YOLOv8 model, which offers unmatched capabilities for real-time object *** mapping of blood tissues and glomerulus structures has been remarkably successful because to the model's adaptation to the complex human *** YOLOv8 is a perfect choice for dissecting the complicated web of blood arteries because of its capacity to analyze enormous amounts of visual data quickly and accurately, allowing for an in-depth analysis of vascular ***-resolution medical photos are obtained as part of the process and then fed into the YOLOv8 *** model has been trained to recognize and define particular blood tissues and glomerulus-related features in the *** utilizing convolution neural networks, YOLOv8 excels at identifying patterns and structures and offers an in-depth mapping of the *** method's potential impact on early disease identification is one of its main *** and abnormalities linked to vascular disorders can be found early on by carefully mapping blood tissues and *** skill for early identificat

关键词： Medical imaging

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AI and the Future of Work: Preparing the Workforce for Technological Shifts and Skill Evolution

AI and the Future of Work: Preparing the Workforce for Techn...

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2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Lokesh, G.R. Harish, K.S. Sangu, veera Shireesha Prabakar, S. Santhosh Kumar, v. vallabhaneni, Mounica Visvesvaraya Technological University Nagarjuna College of Engineering & Technology Dept of Mba Bangalore India Reva University School of Management Studies Bangalore India Isbr Business School Hrm and General Management Bengaluru India Isbr Business School Human Resources Bengaluru India Isbr Business School Marketing and International Business Bengaluru India Presidency University School of Commerce Bangalore India

ISBN: (纸本)9798350359688

The rapid integration of artificial intelligence (AI) into various industries has prompted a profound transformation in the nature of work. As AI technologies continue to evolve, the workforce faces unprecedented challenges and opportunities. This paper delves into the dynamic intersection of AI and the future of work, with a primary focus on preparing the workforce for imminent technological shifts and the evolution of requisite skills. We embark on a comprehensive exploration of existing literature, scrutinizing the current state of AI in the workforce and identifying gaps in knowledge and preparation. Our proposed work introduces a novel approach to addressing these gaps, offering a conceptual framework that emphasizes adaptive strategies for workforce development. The methodology involves a multifaceted research design, encompassing data collection, analysis, and implementation. Leveraging an advanced implementation model, our study aims to elucidate the nuanced impacts of AI on diverse professional domains. Preliminary results showcase the intricate interplay between AI adoption and skill demands, shedding light on critical insights for workforce adaptation. In conclusion, this paper contributes to the discourse on AI's transformative role in the workplace, offering practical implications for policymakers, educators, and industry leaders. The findings underscore the urgency of proactive measures in preparing the workforce for the evolving landscape of work shaped by AI technologies. © 2024 IEEE.

关键词： Artificial Intelligence Future of Work Skill Evolution Technological Shifts Workforce Preparation

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data prophecy: Exploring the efects of belief elicitation in visual analytics

Data prophecy: Exploring the efects of belief elicitation in...

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2021 CHI conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Koonchanok, Ratanond Baser, Parul Sikharam, Abhinav Indiana University-Purdue University Indianapolis United States

ISBN: (纸本)9781450380966

Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confrmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these afordances impact participants' cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design. © 2021 ACM.

关键词： visualization

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Question-guided Insights Generation for Automated Exploratory data analysis 39

Question-guided Insights Generation for Automated Explorator...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Manatkar, Abhijit Akella, Ashlesha Narayanam, Krishnasuri Mehta, Sameep IBM Research India India

ISBN: (纸本)157735897X

Exploratory data analysis (EDA) derives meaningful insights from extensive and complex datasets. This process typically involves a series of analytical operations to identify the patterns within the data. However, the effectiveness of EDA is often limited by the user's domain knowledge and proficiency in data exploration methods. To overcome these challenges, we developed QUIS, a fully automated EDA system that uncovers insights by generating data-related questions and exploring subspaces in the dataset without prior training. QUIS allows users to control key system parameters such as beam width, beam depth, and expansion factor for subspace selection, the interestingness score for filtering valuable insights, and parameters for managing the quality and quantity of generated questions. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： data reduction

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Multimodal Apparent Personality Traits analysis of Short video using Swin Transformer and Bi-directional Long Short-Term Memory Network 4

Multimodal Apparent Personality Traits Analysis of Short Vid...

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4th International conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Duan, Xiaodong Zhan, Qingguo Zhan, Shaohui Yu, Yingying Chang, Luning Wang, Yuangang Minzu University Seac Key Laboratory of Big Data Applied Technology Dalian Key Lab of Digital Technology for National Culture Dalian Dalian China

ISBN: (纸本)9798350321951

Due to potential applications in online education, human resources and personalized recommendation, apparent personality analysis has become a meaningful task in the field of computer vision and multimedia. According to characteristics of short video in time length, idea density and individual expression, we take full advantage of clues in visual and audio aspects to propose a novel framework of apparent personality analysis. From the viewpoint of visual modality, Swin- Transformer and Bi-directional Long Short-Term Memory (BiLSTM) network are introduced to discovery feature representation for personality assessment. Meanwhile, usual features including chromaticity characteristics, root mean square energy, spectrum, zero crossing rate, and MFCC are extracted to describe personality from the perspective of audio modality. Finally, feature leaned from visual and audio modalities are fused to make personality analysis that can be viewed as solving regression problem. For the purpose of evaluating the proposed algorithm's performance, we conduct comparative analysis between our approach and other methods on First Impression v1 and Short video vlogger Personality datasets. The experimental results show that our method is competitive in prediction accuracy. © 2022 IEEE.

关键词： Brain

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