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UCF Computer Vision Course Notes (I) - Introduction and Filtering

Lecture I: Introduction to Computer Vision

Image

  • 2D array of numbers
  • Gray levels 0~255
  • Color image is 3 2D array of numbers

Video

  • Sequence of frames

Image Formation

  • Light source
  • Camera
  • Scene

Perspective Projection

  • Pictures are 2D array of numbers describe 3D real world, and that is one of the hardness of computer vision.
  • Pin hole camera model

Applications

  • Human detection
  • Object recognition
  • Face recognition
  • Facial expressions - detecting driver alertness from eyes' moving
  • Lipreading
  • Video surveillance and monitoring - obeject detecting -> object tracking -> object categorization and classification -> event or activities recognition
  • Unmanned aerial vehicles (drones)

Dataset Introduction

  • Almost all human action dataset, review here if need.

Lecture II: Filtering

Basic Image Knowledge

  • Image kind

    • Binary - only 0 and 1
    • Gray Scale - only 0~255
    • Color - 3D 0~255
  • Image noise

    • Light variations
    • Camera electronics
    • Surface relectance
    • Lens

Correlation and Convolution

  • Convolution adds two flip operations comparing to correlation

MATLAB funcs about filter

refer to: here


Author:Hongyang Wang, http://mr-why.com
Feel free to repost if obey Creative Commons BY-NC-ND 3.0

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