Introduction to Computer Vision
• Understanding the Eyes of AI
• Your Name | Date | Institution
What is Computer Vision?
• - Computer Vision is a field of Artificial
Intelligence that enables computers to
interpret and make decisions based on visual
data.
• - Real-life applications: facial recognition,
object detection, medical imaging.
How Computer Vision Works
• - Input: Digital Images or Videos
• - Processing: Algorithms / Machine Learning /
Deep Learning
• - Output: Labels, Predictions, Actions
Key Techniques in Computer Vision
• - Image Classification
• - Object Detection
• - Image Segmentation
• - Feature Matching
Tools and Libraries
• - OpenCV
• - TensorFlow
• - PyTorch
• - Keras
Real-World Applications
• - Self-driving cars
• - Medical imaging
• - Surveillance systems
• - Augmented Reality (AR)
Challenges in Computer Vision
• - Lighting and Shadows
• - Occlusion
• - Variability in objects
• - Real-time processing
Deep Learning in Computer Vision
• - CNNs (Convolutional Neural Networks)
• - Transfer Learning
• - YOLO, R-CNN, etc.
Future of Computer Vision
• - Integration with Robotics
• - AI-powered smart cities
• - Ethical Considerations
Conclusion
• - Recap of key points
• - Importance of continued research
• - Personal insights or project ideas
Q&A
• Any Questions?

computer vision presentation about the developing technology.pptx

  • 1.
    Introduction to ComputerVision • Understanding the Eyes of AI • Your Name | Date | Institution
  • 2.
    What is ComputerVision? • - Computer Vision is a field of Artificial Intelligence that enables computers to interpret and make decisions based on visual data. • - Real-life applications: facial recognition, object detection, medical imaging.
  • 3.
    How Computer VisionWorks • - Input: Digital Images or Videos • - Processing: Algorithms / Machine Learning / Deep Learning • - Output: Labels, Predictions, Actions
  • 4.
    Key Techniques inComputer Vision • - Image Classification • - Object Detection • - Image Segmentation • - Feature Matching
  • 5.
    Tools and Libraries •- OpenCV • - TensorFlow • - PyTorch • - Keras
  • 6.
    Real-World Applications • -Self-driving cars • - Medical imaging • - Surveillance systems • - Augmented Reality (AR)
  • 7.
    Challenges in ComputerVision • - Lighting and Shadows • - Occlusion • - Variability in objects • - Real-time processing
  • 8.
    Deep Learning inComputer Vision • - CNNs (Convolutional Neural Networks) • - Transfer Learning • - YOLO, R-CNN, etc.
  • 9.
    Future of ComputerVision • - Integration with Robotics • - AI-powered smart cities • - Ethical Considerations
  • 10.
    Conclusion • - Recapof key points • - Importance of continued research • - Personal insights or project ideas
  • 11.