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Related Concept Videos

Visual System01:26

Visual System

554
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
554
Levels of Use of a GIS01:29

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Visualizing Visual Adaptation
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Development model based on visual image big data applied to art management.

Jiehui Ju1, Yanghui Ma1, Ting Gong1

  • 1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China.

Heliyon
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new art management model using visual image big data (BD) and image processing (IP) technology. The model enhances user satisfaction by 24%, improving art management efficiency and quality.

Keywords:
Art managementBig data applicationManagement model developmentVisual image

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Area of Science:

  • Computer Science
  • Art Management
  • Data Science

Background:

  • Big data (BD) presents unique characteristics and applications within art management.
  • Visual image technology, including image processing (IP) and computer vision, offers potential for art management innovation.

Purpose of the Study:

  • To explore the application of visual image big data in art management.
  • To propose and develop a novel art management model integrating visual image technology and BD.
  • To enhance art resource management, teaching, and market trend assessment.

Main Methods:

  • Conducted research on big data characteristics and applications in art management.
  • Introduced image processing (IP) technology and computer vision classification for art management.
  • Developed an art management model based on visual image acquisition, BD, and IP algorithms.

Main Results:

  • The developed art management model integrates visual image technology, big data, and IP algorithms.
  • Experimental results show a 24% increase in user satisfaction with the new model.
  • The model provides significant technical support and improves efficiency and quality in art management.

Conclusions:

  • The novel art management model effectively enhances user satisfaction and operational efficiency.
  • Visual image big data and IP technology offer robust solutions for art resource management and teaching.
  • The model serves as a valuable tool for designers in assessing market trends and promoting design concepts.