Digital Image Processing - Introduction

Digital Image Processing

• A digital image is a two-dimensional function, F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point.
• Common image formats include:
  • 1 sample per point (B&W or Grayscale)
  • 3 samples per point (Red, Green, and Blue)

Image Processing, Analysis, and Understanding

• Image Processing: image in → image out
• Image Analysis: image in → measurements out
• Image Understanding: image in → high-level description out

Examples of Digital Image Processing

• Image Enhancement: improve quality, remove noise, etc.
• The Hubble Telescope: used image processing techniques to fix incorrect mirror images
• Medicine: take slice from MRI scan of canine heart, and find boundaries between types of tissue
• Geographic Information Systems (GIS): use digital image processing techniques to manipulate satellite imagery
• Industrial Inspection: use machines to inspect products, reducing human error
• Human Computer Interaction (HCI): use digital image processing to make human-computer interfaces more natural, e.g. face recognition and gesture recognition

Human Visual System

• The amount of light entering the eye is controlled by the pupil
• The light is focused on the retina, where receptors convert it into nerve signals that pass to the brain
• Elements of visual perception:
  • Cones: 6-7 million in each eye, involved in photopic or bright-light vision, highly sensitive to color
  • Rods: 75-150 million, involved in scotopic or dim-light vision, not involved in color vision

Human Perception Phenomena

• Simultaneous Contrast: a region's perceived brightness does not depend simply on its intensity
• Optical Illusions: the eye fills in nonexisting information or wrongly perceives geometrical properties of objects