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Computer Vision

How Machines Learn to See

COMPUTER VISION

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  • An Introduction to Artificial Intelligence Vision Systems

Introduction to Computer Vision

  • Computer Vision is a branch of Artificial Intelligence (AI).
  • It enables computers to see images and understand videos.
  • It helps identify objects and patterns.
  • It allows machines to make decisions from visual data. Like giving eyes and brain to a computer.

History of Computer Vision

  • 1960s – Basic image processing started.
  • 1980s – Pattern recognition improved.
  • 2000s – Machine learning introduced.
  • 2012 – Deep learning revolution. Today – Used in self-driving cars, face recognition, and medical diagnosis.

How Computer Vision Works

  • 1. Image Capture using cameras.
  • 2. Image Processing to enhance quality.
  • 3. Feature Extraction to find important details.
  • 4. Pattern Recognition and Decision Making using Machine Learning and Deep Learning algorithms.

Key Technologies Used

  • 1. Machine Learning – Computers learn from data.
  • 2. Deep Learning – Uses Neural Networks.
  • 3. Convolutional Neural Networks (CNN) – Specialized for image recognition.
  • 4. Image Processing – Enhancing and analyzing images.

Applications of Computer Vision

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

Computer Vision in Healthcare

  • Detecting cancer from X-rays.
  • MRI scan analysis.
  • Tumor detection.
  • Disease diagnosis with high accuracy. Helps doctors make faster decisions.

Computer Vision in Security

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

Computer Vision in Self-Driving Cars

  • Detects pedestrians.
  • Identifies traffic signs.
  • Recognizes lanes.
  • Avoids obstacles. Used by companies like Tesla and Waymo.

Advantages of Computer Vision

  • High accuracy.
  • Faster processing.
  • Reduces human error.
  • Works 24/7 and improves automation.

Challenges of Computer Vision

  • Privacy concerns.
  • High development cost.
  • Requires large amounts of data.
  • May make mistakes in poor lighting or complex environments.

Future of Computer Vision

  • Smarter robots.
  • Advanced healthcare diagnosis.
  • Fully autonomous vehicles.
  • Smart cities and Augmented Reality systems. Computer Vision will become part of daily life.

Conclusion

  • Computer Vision allows machines to see and understand the world.
  • It analyzes visual data intelligently.
  • It makes smart decisions.
  • It plays a major role in healthcare, security, transportation, and industry.

Thank You

  • Thank You!
  • Any Questions?
  • Computer Vision – The Future of AI Vision Systems

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