Computer Vision and Pattern Recognition

Computer vision is a learning to redevelop, describe and understand a 2D and its properties of the structures present in the scene for a 3D scene. The final goal of computer vision is to model, duplicate and exceed human vision using computer software and hardware at disparate levels. It demands knowledge in computer science, mathematics, biology, electrical engineering physiology and cognitive science. Computer vision is the establishment of clear, meaningful representation of physical objects from their images. The output of computer vision is an illustration or an interpretation of the structures in the 3D scene. Image recognition and Image processing are the techniques of computer vision employs to achieve its goals.

  • Object Recognition
  • Video: events, activities and surveillance
  • Segmentation, grouping and shape representation
  • Recognition: detection, categorization, indexing, matching
  • Physics-based vision and shape-from-X
  • Optimization methods
  • Real-time Tracking
  • Vision for Web
  • Vision for Graphics
  • Vision for Robotics
  • Computational Photography
  • Human-Computer Interaction
  • Action Recognition
  • Human Motion Analysis/Capture
  • Gesture/Hand Recognition
  • Face Detection and Recognition
  • Motion/Stereo Processing and Augmented Reality
  • Applications and research

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