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COMPUTER VISION

How Machines Learn to See

COMPUTER VISION

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Introduction to Computer Vision

  • A branch of Artificial Intelligence (AI).
  • Enables computers to see images and understand videos.
  • Identifies objects and patterns.
  • Makes decisions from visual data – like giving eyes and brain to a computer.

History of Computer Vision

  • 1960s – Basic image processing.
  • 1980s – Pattern recognition improved.
  • 2000s – Machine learning introduced.
  • 2012 – Deep learning revolution. Today – Used everywhere.

How Computer Vision Works

  • Image Capture (Camera/Sensors).
  • Image Processing (Enhancement & Cleaning).
  • Feature Extraction (Detecting Important Details).
  • Pattern Recognition & Decision Making using AI algorithms.

Key Technologies Used

  • Machine Learning – Learns from data.
  • Deep Learning – Uses Neural Networks.
  • Convolutional Neural Networks (CNN) – Best for images.
  • Image Processing – Enhancing and analyzing visuals.

Applications of Computer Vision

  • Self-Driving Cars.
  • Face Recognition.
  • Medical Diagnosis.
  • Industrial Quality Check, Object Detection & Surveillance.

Computer Vision in Healthcare

  • Cancer detection from X-rays.
  • MRI and CT scan analysis.
  • Tumor detection systems.
  • AI-based disease diagnosis support.

Computer Vision in Security

  • Face recognition technology.
  • Biometric authentication.
  • Smart surveillance cameras.
  • Crime detection systems in airports and banks.

Computer Vision in Self-Driving Cars

  • Detect pedestrians.
  • Recognize traffic signs.
  • Identify lanes.
  • Avoid obstacles using real-time AI vision.

Advantages of Computer Vision

  • High accuracy and precision.
  • Fast data processing.
  • Reduces human error.
  • Operates 24/7 and boosts automation.

Challenges of Computer Vision

  • Privacy concerns.
  • High development cost.
  • Needs large training datasets.
  • Struggles in poor lighting or complex scenes.

Future of Computer Vision

  • Smarter robots.
  • Advanced healthcare AI.
  • Fully autonomous vehicles.
  • Smart cities & Augmented Reality integration.

Conclusion

  • Machines can now see and understand the world.
  • Computer Vision enables intelligent decisions.
  • Used in healthcare, security, transport & industry.
  • It is shaping the future of technology.

Thank You

  • Thank You!
  • Any Questions?

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