Six Deep Learning-Enabled Vision Applications in Digital Media, Healthcare, Agriculture, Retail, Manufacturing, and Other Industries
The enterprise applications for deep learning have only scratched the surface of their potential applicability and use cases. Because it is data agnostic, deep learning is poised to be used in almost every enterprise vertical market, including agriculture, media, manufacturing, medical, healthcare, and retail, to name a few. Deep learning is particularly applicable to computer vision systems because it promises to be less costly, more accurate, and more reliable than traditional programming approaches.
Some of the most successful companies in the world have been early adopters of this technology. Although the enterprise market for deep learning is still small in relation to the total enterprise software sector, the variety, breadth, and scope of the applications than deep learning is being considered for suggests that a tremendous growth opportunity exists.
This Tractica white paper covers the market for computer vision and deep learning technologies, providing real world use cases of how they are being used in various industry verticals. The verticals covered include agriculture, media, manufacturing, medical, healthcare, and retail. This white paper is published in partnership with the Embedded Vision Alliance, host of the Embedded Vision Summit, which is being held May 2-4, 2016 in Santa Clara, California.
To download a PDF of the white paper, please see here. Note that you'll need to register on the Alliance website and then sign in prior to accessing the download.
Key Questions Addressed:
- What are the key use cases for deep learning to enable computer vision systems?
- Which business needs are being addressed with each of these use cases?
- In various vertical markets, how does the deep learning-enabled computer vision solution work from a technology and process perspective?
- What are the business benefits associated with each of these use cases?
- What are some of the key challenges associated with utilizing deep learning for computer vision?
- What are some of the additional potential use cases for deep learning-enabled computer vision in the future?
Who Needs This Report?
- Artificial intelligence technology developers
- Enterprise software companies
- Semiconductor and component manufacturers
- Service providers and systems integrators
- End-user organizations deploying deep learning and computer vision systems
- Industry associations
- Government agencies
- Investor community
Table of Contents
- Embedded Vision Alliance, Embedded Vision Summit
- Introduction
- Use Cases
- Static Image Recognition, Classification, and Tagging
- Why there Is a Need
- How It Works
- Benefits
- Challenges
- Digital Radiology Analysis
- Why there Is a Need
- How It Works
- Benefits
- Challenges
- Agricultural Crop Health Analysis
- Why there Is a Need
- How It Works
- Benefits
- Challenges
- Clinical Trial Medication Compliance
- Why there Is a Need
- How It Works
- Benefits
- Challenges
- Clothes and Accessories Sizing and Fitting
- Why there Is a Need
- How It Works
- Benefits
- Challenges
- Industrial Automation Quality Assurance
- Why there Is a Need
- How It Works
- Benefits
- Challenges
- Other Opportunities
- Emotion Recognition
- Neuromorphic Computing
- Static Image Recognition, Classification, and Tagging
- Company Directory
- Acronym and Abbreviation List
- Table of Contents
- Table of Charts and Figures
- Scope of Study, Sources and Methodology, Notes
- Additional Reading
List of Charts and Figures
- Total Deep Learning Software Revenue by Industry, World Markets: 2015-2024
- Static Image Recognition, Classification, and Tagging Example
- Digital Radiology Analysis Example
- Agricultural Crop Health Analysis Example
- Clinical Trial Medication Compliance Example
- Clothes and Accessories Sizing and Fitting Example
- Industrial Automation Quality Assurance Example