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computer vision

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to interpret and understand images, videos, and other visual inputs in a manner similar to human vision. It has the capability to surpass human performance in specific tasks due to the speed and accuracy of machine processing..


At its core, computer vision processes images as arrays of pixels, where each pixel contains values representing the intensity of primary colors (red, green, and blue). These pixels form a digital image, which is essentially a matrix that computer vision algorithms manipulate to interpret the visual data. The process typically involves three main steps:


  1. Acquisition: Capturing the image or video and converting it into a digital format.
  2. Processing: Applying computer vision algorithms to analyze the geometric elements and features within the digital data.
  3. Analysis and Action: Interpreting the processed data to make decisions or take actions based on the analysis.


Computer vision has a wide range of applications across various industries, demonstrating its versatility and the value it brings to technological advancements and digital transformation:


  1. Manufacturing: Quality control, defect detection, and automation of production processes.
  2. Healthcare: Disease detection, medical image analysis (e.g., X-rays, MRI), and surgery assistance.
  3. Agriculture: Crop monitoring, pest detection, and yield estimation using drones and image processing.
  4. Transportation: Autonomous vehicles, traffic monitoring, and vehicle classification.
  5. Security: Surveillance, facial recognition, and access control.
  6. Retail: Customer behavior analysis, inventory management, and checkout processes.


The Role of Deep Learning in Computer Vision

Deep learning, a subset of machine learning, plays a crucial role in advancing computer vision technologies. By using neural networks with multiple layers, deep learning enables the creation of more accurate and complex computer vision models. These models can recognize patterns in visual data and make interpretations that guide predictive or decision-making tasks. Deep learning techniques have significantly improved the capabilities of computer vision systems, making them more efficient and effective in processing and understanding visual information.


Compare with: machine vision


Citations:

  1. https://www.ibm.com/topics/computer-vision
  2. https://viso.ai/applications/computer-vision-applications/
  3. https://www.v7labs.com/blog/what-is-computer-vision
  4. https://en.wikipedia.org/wiki/Computer_vision
  5. https://cnvrg.io/computer-vision-applications/
  6. https://www.javatpoint.com/computer-vision-techniques
  7. https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-computer-vision
  8. https://www.v7labs.com/blog/computer-vision-applications
  9. https://www.run.ai/guides/deep-learning-for-computer-vision
  10. https://www.sas.com/en_hk/insights/analytics/computer-vision.html
  11. https://www.xenonstack.com/blog/computer-vision-applications
  12. https://www.linkedin.com/pulse/5-computer-vision-techniques-change-how-you-see-world-james-le
  13. https://www.sciencedirect.com/topics/engineering/computervision
  14. https://www.simplilearn.com/computer-vision-article
  15. https://towardsdatascience.com/a-beginners-guide-to-computer-vision-dca81b0e94b4
  16. https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-computer-vision/
  17. https://research.aimultiple.com/computer-vision-use-cases/
  18. https://www.techtarget.com/searchenterpriseai/definition/machine-vision-computer-vision
  19. https://aws.amazon.com/what-is/computer-vision/
  20. https://www.techopedia.com/definition/32309/computer-vision



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