
Visual Computing Marketbased on By Component (Software And Hardware), By Display Platform (Monitors, Interactive Whiteboards, And Others), And By Industry (Media & Entertainment, Automotive, Healthcare, Gaming, And Others), Regional Outlook– Global Foreca
Description
In order to find and gather data relevant for a technical, business-oriented, and market-oriented study of artificial intelligence in the computer vision market, this research project used a wide range of secondary sources, directories, and databases, including Hoovers, Bloomberg, Businessweek, Factiva, and OneSource. The primary sources included a number of industry experts from core and allied fields as well as preferred manufacturers, distributors, service providers, tech developers, and suppliers from all points along the AI value chain in the computer vision ecosystem.
Key industry participants, subject matter experts (SMEs), C-level executives of major market players, and industry consultants were among the primary respondents with whom in-depth interviews were conducted in order to gather and validate crucial qualitative and quantitative data for evaluating the prospects of the future market.
After learning about the current state of the AI in computer vision market through secondary research, a significant amount of primary research was carried out. Experts from the demand and supply sides in each of the four main regions—North America, Europe, Asia Pacific, and the Rest of the World—were interviewed in multiple primary interviews. Emails, phone interviews, and questionnaires were used to gather this primary data.
Many secondary sources have been consulted during the secondary research process in order to locate and gather data crucial to this investigation. Annual reports, press announcements, investor presentations, white papers, journals, certified publications, articles by reputable writers, websites, directories, and databases are some examples of secondary sources. Important data regarding the industry's supply chain, the market's value chain, the entire group of important players, market segmentation based on industry trends (down to the lowest level), geographical markets, and significant advancements from both a market- and a technology-oriented standpoint have been gathered through secondary research. The total market size has been determined by gathering and analyzing secondary data, and primary research has further confirmed this estimate.
Visual Computing Market based on Component:
One of the main obstacles to the market's expansion is the high cost of purchasing and integrating AI computer vision technologies. AI computer vision system development and implementation can be costly, frequently needing specialized hardware, software, and technical know-how. For companies unfamiliar with AI computer vision, acquiring and deploying these systems can be expensive, involving expenses for hardware, software licensing, and technical assistance. Furthermore, companies might have to spend money on programs for training and development in order to increase their technical proficiency with these systems.
AI computer vision is a rapidly developing topic with many potential uses. Manufacturing, logistics, and agriculture are just a few industries that stand to gain greatly from the use of this technology. AI computer vision is utilized in agriculture to automate a number of tasks, including disease identification, yield prediction, and crop monitoring. Images of crops can be analyzed by AI algorithms to reveal details about their growth, health, and potential yield. Farmers can use this information to better understand how to take care of their crops and enhance their output.
One of the biggest problems the industry is experiencing is keeping AI computer vision systems transparent and trustworthy. The results of AI computer vision systems can have a big influence on people and society in important applications like healthcare and security. Consequently, it is imperative to guarantee that these systems exhibit dependability, precision, and transparency in their decision-making procedures. Ensuring that the data used to train AI computer vision systems accurately represents the population to whom it is being applied is a significant challenge in upholding trust and transparency. A biased training set of data may cause the system to make biased conclusions that have the unintended effect of discrimination or other issues.
Key industry participants, subject matter experts (SMEs), C-level executives of major market players, and industry consultants were among the primary respondents with whom in-depth interviews were conducted in order to gather and validate crucial qualitative and quantitative data for evaluating the prospects of the future market.
After learning about the current state of the AI in computer vision market through secondary research, a significant amount of primary research was carried out. Experts from the demand and supply sides in each of the four main regions—North America, Europe, Asia Pacific, and the Rest of the World—were interviewed in multiple primary interviews. Emails, phone interviews, and questionnaires were used to gather this primary data.
Many secondary sources have been consulted during the secondary research process in order to locate and gather data crucial to this investigation. Annual reports, press announcements, investor presentations, white papers, journals, certified publications, articles by reputable writers, websites, directories, and databases are some examples of secondary sources. Important data regarding the industry's supply chain, the market's value chain, the entire group of important players, market segmentation based on industry trends (down to the lowest level), geographical markets, and significant advancements from both a market- and a technology-oriented standpoint have been gathered through secondary research. The total market size has been determined by gathering and analyzing secondary data, and primary research has further confirmed this estimate.
Visual Computing Market based on Component:
- Software
- Hardware
- Monitors
- Interactive Whiteboards
- Others
- Media and Entertainment
- Automotive
- Healthcare
- Gaming
- Others
- North America
- US
- Canada
- Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe (RoE)
- Asia Pacific (APAC)
- China
- Japan
- India
- Australia
- South Korea
- Rest of Asia Pacific (RoAPAC)
- Latin America (LATAM)
- Brazil
- Argentina
- Rest of South America
- Middle East and Africa (MEA)
- UAE
- Turkey
- Saudi Arabia
- South Africa
- Rest of Middle East & Africa
One of the main obstacles to the market's expansion is the high cost of purchasing and integrating AI computer vision technologies. AI computer vision system development and implementation can be costly, frequently needing specialized hardware, software, and technical know-how. For companies unfamiliar with AI computer vision, acquiring and deploying these systems can be expensive, involving expenses for hardware, software licensing, and technical assistance. Furthermore, companies might have to spend money on programs for training and development in order to increase their technical proficiency with these systems.
AI computer vision is a rapidly developing topic with many potential uses. Manufacturing, logistics, and agriculture are just a few industries that stand to gain greatly from the use of this technology. AI computer vision is utilized in agriculture to automate a number of tasks, including disease identification, yield prediction, and crop monitoring. Images of crops can be analyzed by AI algorithms to reveal details about their growth, health, and potential yield. Farmers can use this information to better understand how to take care of their crops and enhance their output.
One of the biggest problems the industry is experiencing is keeping AI computer vision systems transparent and trustworthy. The results of AI computer vision systems can have a big influence on people and society in important applications like healthcare and security. Consequently, it is imperative to guarantee that these systems exhibit dependability, precision, and transparency in their decision-making procedures. Ensuring that the data used to train AI computer vision systems accurately represents the population to whom it is being applied is a significant challenge in upholding trust and transparency. A biased training set of data may cause the system to make biased conclusions that have the unintended effect of discrimination or other issues.
- This report illustrates the most vital attributes of the Visual Computing Market, which are driving and providing opportunities.
- This research gives an in-depth analysis of the Visual Computing Market growth on the basis of several segments in the market.
- This report presents the predictions of the past and present trends of the Visual Computing Market.
- This study also presents the competitive analysis, such as key strategies and capabilities of major players of the Visual Computing Market.
Table of Contents
150 Pages
- 1. Executive Summary
- 2. Industry Outlook
- 2.1. Industry Overview
- 2.2. Industry Trends
- 3. Market Snapshot
- 3.1. Market Definition
- 3.2. Market Outlook
- 3.2.1. Porter Five Forces
- 3.3. Related Markets
- 4. Market characteristics
- 4.1. Market Overview
- 4.2. Market Segmentation
- 4.3. Market Dynamics
- 4.3.1. Drivers
- 4.3.2. Restraints
- 4.3.3. Opportunities
- 4.4. DRO - Impact Analysis
- 5. Component: Market Size & Analysis
- 5.1. Overview
- 5.2. Software
- 5.3. Hardware
- 6. Display platform: Market Size & Analysis
- 6.1. Overview
- 6.2. Monitors
- 6.3. Interactive Whiteboards
- 6.4. Others
- 7. Industry: Market Size & Analysis
- 7.1. Overview
- 7.2. Media and Entertainment
- 7.3. Automotive
- 7.4. Healthcare
- 7.5. Gaming
- 8. Others
- 9. Geography: Market Size & Analysis
- 9.1. Overview
- 9.2. North America (U.S., Mexico, Canada)
- 9.3. Europe (France, Germany, UK, Italy, Netherlands, Spain, Russia, Rest of Europe)
- 9.4. Asia Pacific (Japan, China, India, Australia, South East Asia, Rest of APAC)
- 9.5. Latin America (Brazil, Argentina)
- 9.6. Middle East & Africa (Saudi Arabia, UAE, South Africa, Rest of Middle East and Africa)
- 10. Competitive Landscape
- 10.1. Competitor Comparison Analysis
- 10.2. Market Developments
- 10.2.1. Mergers and Acquisitions, Legal, Awards, Partnerships
- 10.2.2. Product Launches and execution
- 11. Vendor Profiles
- 11.1. NVIDIA
- 11.1.1. Overview
- 11.1.2. Financial Overview
- 11.1.3. Product Offerings
- 11.1.4. Developments
- 11.1.5. Business Strategy
- 11.2. INTEL
- 11.2.1. Overview
- 11.2.2. Financial Overview
- 11.2.3. Product Offerings
- 11.2.4. Developments
- 11.2.5. Business Strategy
- 11.3. ADVANCED MICRO DEVICES
- 11.3.1. Overview
- 11.3.2. Financial Overview
- 11.3.3. Product Offerings
- 11.3.4. Developments
- 11.3.5. Business Strategy
- 11.4. ARM
- 11.4.1. Overview
- 11.4.2. Financial Overview
- 11.4.3. Product Offerings
- 11.4.4. Developments
- 11.4.5. Business Strategy
- 11.5. IMAGINATION TECHNOLOGIES.
- 11.5.1. Overview
- 11.5.2. Financial Overview
- 11.5.3. Product Offerings
- 11.5.4. Developments
- 11.5.5. Business Strategy
- 11.6. MATROX
- 11.6.1. Overview
- 11.6.2. Financial Overview
- 11.6.3. Product Offerings
- 11.6.4. Developments
- 11.6.5. Business Strategy
- 11.7. BIODIGITAL
- 11.7.1. Overview
- 11.7.2. Financial Overview
- 11.7.3. Product Offerings
- 11.7.4. Developments
- 11.7.5. Business Strategy
- 11.8. EXXACT
- 11.8.1. Overview
- 11.8.2. Financial Overview
- 11.8.3. Product Offerings
- 11.8.4. Developments
- 11.8.5. Business Strategy
- 11.9. CUBIX
- 11.9.1. Overview
- 11.9.2. Financial Overview
- 11.9.3. Product Offerings
- 11.9.4. Developments
- 11.9.5. Business Strategy
- 11.10. SOFTKINETIC
- 11.10.1. Overview
- 11.10.2. Financial Overview
- 11.10.3. Product Offerings
- 11.10.4. Developments
- 11.10.5. Business Strategy
- 12. Analyst Opinion
- 13. Annexure
- 13.1. Report Scope
- 13.2. Market Definitions
- 13.3. Research Methodology
- 13.3.1. Data Collation and In-house Estimation
- 13.3.2. Market Triangulation
- 13.3.3. Forecasting
- 13.4. Report Assumptions
- 13.5. Declarations
- 13.6. Stakeholders
- 13.7. Abbreviations
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