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INTERACTIVE visual EXPLORATION AND ANALYSIS OF MULTIVARIATE SIMULATION DATA

computing IN SCIENCE & ENGINEERING 2012年第2期14卷 70-76页

作者： Doleisch, Helmut Hauser, Helwig Univ Bergen N-5020 Bergen Norway

The interactive visual exploration of large and complex simulation datasets has become an important methodology that improves data analysis for scientists and professional practitioners.

关键词： Interactive visualization visual computing Scientific computing

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Measuring Knowledge Acquisition in 3D Virtual Learning Environments

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2016年第2期36卷 58-67页

作者： dos Santos Nunes, Eunice P. Roque, Licinio G. dos Santos Nunes, Fatima de Lourdes Univ Fed Mato Grosso Curitiba Parana Brazil Univ Coimbra P-3000 Coimbra Portugal Univ Sao Paulo BR-05508 Sao Paulo Brazil Fed Univ Mato Grosso UFMT Inst Comp Curitiba Parana Brazil Univ Coimbra Software Engn Human Comp Interact Course Masters Program Engn P-3000 Coimbra Portugal Univ Coimbra Software Engn Game Design Course Masters Program Design P-3000 Coimbra Portugal Univ Sao Paulo Sch Arts Sci & Humanities BR-05508 Sao Paulo Brazil CNPq Brasilia DF Brazil

Virtual environments can contribute to the effective learning of various subjects for people of all ages. Consequently, they assist in reducing the cost of maintaining physical structures of teaching, such as laboratories and classrooms. However, the measurement of how learners acquire knowledge in such environments is still incipient in the literature. This article presents a method to evaluate the knowledge acquisition in 3D virtual learning environments (3D VLEs) by using the learner's interactions in the VLE. Three experiments were conducted that demonstrate the viability of using this method and its computational implementation. The results suggest that it is possible to automatically assess learning in predetermined contexts and that some types of user interactions in 3D VLEs are correlated with the user's learning differential.

关键词： 3D virtual environments automatic assessment tools Computational modeling computer graphics Knowledge acquisition Solid modeling Three-dimensional displays Virtual environments virtual learning environments virtual reality visual computing

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Graphics and Media Technologies for Operators in Industry 4.0

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2018年第5期38卷 119-132页

作者： Posada, Jorge Zorrilla, Mikel Dominguez, Ana Simoes, Bruno Eisert, Peter Stricker, Didier Rambach, Jason Doellner, Juergen Guevara, Miguel GraphicsMedia Net Kaiserslautern Germany Vicomtech San Sebastian Spain Fraunhofer HHI Berlin Germany DFKI Kaiserslautern Germany Hasso Plattner Inst Potsdam Germany CCG Cologne Germany

visual computing technologies have an important role in manufacturing and production, particularly in new Industry 4.0 scenarios with intelligent machines, human-robot collaboration and learning factories. In this article, we explore challenges and examples on how the fusion of graphics, vision and media technologies can enhance the role of operators in this new context.

关键词： computer graphics computer vision industry 4.0 media smart factory visual computing

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An improved saliency detection method based on non-uniform quantification and channel-weighted color distance

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第8期76卷 11037-11050页

作者： Han, Aili Han, Feilin Hao, Jing Yuan, Yahui Shandong Univ Dept Comp Sci & Technol Weihai 264209 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310007 Zhejiang Peoples R China

We propose a non-uniform quantification method for RGB channels based on the visual sensitivities of human eyes to the red, green and blue colors, and give weights to RGB channels which are used to compute the channel-weighted color distances. Furthermore, we design a saliency detection method using the non-uniform quantification method and the channel-weighted color distances measurement. We first quantify the values in RGB channels in different step-lengths and determine the weights w (r) , w (g) and w (b) based on the visual sensitivities of human eyes to the three-primary colors. And then we convert the quantified image into that in Lab color space, which are segmented into some regions, and compute the channel-weighted color distance using the weights w (L) , w (a) and w (b) , which are computed from the weights w (r) , w (g) and w (b) and the transformation from RGB color space to Lab color space. The saliency of each region is computed using the weighted distances and spatial weights. The proposed non-uniform quantification method and the channel-weighted color distance measurement can be used for the applications based on color features in the field of image processing and computer vision. Experimental results show that our methods can improve the efficiency and performance of saliency detection to some extent.

关键词： Saliency detection visual computing Non-uniform quantification Channel weight

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Semantic binary coding for visual recognition via joint concept-attribute modelling

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第17期77卷 22185-22198页

作者： Xu, Xing Wu, Haiping Yang, Yang Shen, Fumin Xie, Ning Ji, Yanli Guizhou Univ Guizhou Prov Key Lab Publ Big Data Guiyang Guizhou Peoples R China Univ Elect Sci & Technol China Ctr Future Media Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China

Recent years have witnessed the unprecedented efforts of visual representation for enabling various efficient and effective multimedia applications. In this paper, we propose a novel visual representation learning framework, which generates efficient semantic hash codes for visual samples by substantially exploring concepts, semantic attributes as well as their inter-correlations. Specifically, we construct a conceptual space, where the semantic knowledge of concepts and attributes is embedded. Then, we develop an effective on-line feature coding scheme for visual objects by leveraging the inter-concept relationships through the intermediate representative power of attributes. The code process is formulated as an overlapping group lasso problem, which can be efficiently solved. Finally, we may binarize the visual representation to generate efficient hash codes. Extensive experiments have been conducted to illustrate the superiority of our proposed framework on visual retrieval task as compared to state-of-the-art methods.

关键词： visual computing Binary representation Semantics Attributes

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MOSBIE: a tool for comparison and analysis of rule-based biochemical models

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BMC BIOINFORMATICS 2014年第1期15卷 1-16页

作者： Wenskovitch, John E., Jr. Harris, Leonard A. Tapia, Jose-Juan Faeder, James R. Marai, G. Elisabeta Allegheny Coll Dept Comp Sci Meadville PA 16335 USA Vanderbilt Univ Sch Med Dept Canc Biol Nashville TN 37232 USA Univ Pittsburgh Dept Computat & Syst Biol Pittsburgh PA 15260 USA Univ Illinois Dept Comp Sci Elect Visualizat Lab Chicago IL 60607 USA

Background: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. Results: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. Conclusions: We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.

关键词： visualization visual computing Rule-based modeling Cell signaling

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Machine learning based small bowel video capsule endoscopy analysis: Challenges and opportunities

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年 143卷 191-214页

作者： Wahab, Haroon Mehmood, Irfan Ugail, Hassan Sangaiah, Arun Kumar Muhammad, Khan Univ Bradford Ctr Visual Comp Bradford England Natl Yunlin Univ Sci & Technol Int Grad Inst Artificial Intelligence Touliu 64002 Taiwan Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Sungkyunkwan Univ Coll Comp & Informat Sch Convergence Dept Appl Artificial IntelligenceVisual Analyt Kn Seoul 03063 South Korea

Video capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to the high redundancy and subtle manifestation of anomalies among thousands of frames, the manual construal of VCE videos requires considerable patience, focus, and time. The automatic analysis of these videos using computational methods is a challenge as the capsule is un-tamed in motion and captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential of improving the VCE analysis. However, the clinical impact of these methods is yet to be investigated. This survey aimed to highlight the gaps between existing ML-based research methodologies and clinically significant rules recently established by gastroenterologists based on VCE. A framework for interpreting raw frames into contextually relevant frame-level findings and subsequently merging these findings with meta-data to obtain a disease-level diagnosis was formulated. Frame-level findings can be more intelligible for discriminative learning when organized in a taxonomical hierarchy. The proposed taxonomical hierarchy, which is formulated based on pathological and visual similarities, may yield better classification metrics by setting inference classes at a higher level than training classes. Mapping from the frame level to the disease level was structured in the form of a graph based on clinical relevance inspired by the recent international consensus developed by domain experts. Furthermore, existing methods for VCE summarization, classification, segmentation, detection, and localization were critically evaluated and compared based on aspects deemed significant by clinicians. Numerous studies pertain to single anomaly detection instead of a pragmatic approach in a clinical setting. The challenges and opportunities associated with VCE analysis were delineated. A focus on maxim

关键词： Machine learning visual computing Deep learning Capsule endoscopy Small bowel Precision diagnostic

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Toward Human-centered XAIin Practice:A survey

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Machine Intelligence Research 2024年第4期21卷 740-770页

作者： Xiangwei Kong Shujie Liu Luhao Zhu Department of Data Science and Management Engineering School of ManagementZhejiang UniversityHangzhou 310058China

Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI *** AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human ***,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI *** first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of *** addition,a taxonomy of XAI methods is developed for visual computing with analysis of method *** clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in *** we check the availability of open-source XAI tools for stakeholders'*** survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

关键词： Artificial intelligence(AI)application explainable AI(XAI) human-centered design visual computing medical diagnosis.

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Computer Vision and Machine Learning for Viticulture Technology

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IEEE ACCESS 2018年 6卷 67494-67510页

作者： Seng, Kah Phooi Ang, Li-Minn Schmidtke, Leigh M. Rogiers, Suzy Y. Charles Sturt Univ Ringgold Stand Inst Sch Comp & Math Wagga Wagga Campus Wagga Wagga NSW 2650 Australia Ringgold Stand Inst Natl Wine & Grape Ind Ctr Wagga Wagga NSW 2678 Australia NSW Dept Primary Ind Wagga Wagga NSW 2678 Australia

This paper gives two contributions to the state-of-the-art for viticulture technology research. First, we present a comprehensive review of computer vision, image processing, and machine learning techniques in viticulture. We summarize the latest developments in vision systems and techniques with examples from various representative studies, including, harvest yield estimation, vineyard management and monitoring, grape disease detection, quality evaluation, and grape phenology. We focus on how computer vision and machine learning techniques can be integrated into current vineyard management and vinification processes to achieve industry relevant outcomes. The second component of the paper presents the new GrapeCS-ML database which consists of images of grape varieties at different stages of development together with the corresponding ground truth data (e.g., pH and Brix) obtained from chemical analysis. One of the objectives of this database is to motivate computer vision and machine learning researchers to develop practical solutions for deployment in smart vineyards. We illustrate the usefulness of the database for a color-based berry detection application for white and red cultivars and give baseline comparisons using various machine learning approaches and color spaces. This paper concludes by highlighting future challenges that need to be addressed prior to successful implementation of this technology in the viticulture industry.

关键词： Viticulture computer vision machine vision visual computing image processing machine learning

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Underwater Depth Estimation and Image Restoration Based on Single Images

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2016年第2期36卷 24-35页

作者： Drews, Paulo L. J., Jr. Nascimento, Erickson R. Botelho, Silvia S. C. Montenegro Campos, Mario Fernando Univ Fed Rio Grande Rio De Janeiro Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil Fed Univ Rio Grande FURG Rio De Janeiro Brazil Fed Univ Minas Gerais UFMG Dept Comp Sci Belo Horizonte MG Brazil Ctr Ciencias Computacionais C3 FURG Belo Horizonte MG Brazil FURG Robot & Intelligent Automat NAUTEC Rio De Janeiro Brazil Fed Univ Minas Gerais UFMG Dept Comp Sci Comp Vis & Robot Belo Horizonte MG Brazil Univ Fed Minas Gerais Vis & Robot Lab VeRLab Belo Horizonte MG Brazil

In underwater environments, the scattering and absorption phenomena affect the propagation of light, degrading the quality of captured images. In this work, the authors present a method based on a physical model of light propagation that takes into account the most significant effects to image degradation: absorption, scattering, and backscattering. The proposed method uses statistical priors to restore the visual quality of the images acquired in typical underwater scenarios. [ABSTRACT FROM AUTHOR]

关键词： Absorption Attenuation backscattering computer graphics Dark Channel Prior Image color analysis image restoration Scattering Underwater Dark Channel Prior underwater images visual computing

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