1. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Outline:
Motivation
Human vision and illusions
Image representation
Sampling
Quantization
Thresholding
CSE (c) S. Tanimoto, Image Understanding

2. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Motivation
Allow computer and robots to read books.
Allow mobile robots to navigate using vision.
Support applications in industrial inspection, medical image analysis, security and surveillance, and remote sensing of the environment.
Permit computers to recognize users’ faces, fingerprints, and to track them in various environments.
Provide prostheses for the blind.
Develop artistic intelligence.
CSE (c) S. Tanimoto, Image Understanding

3. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Human Vision
25% of brain volume is allocated to visual perception.
Human vision is a parallel & distributed system, involving 2 eyes, retinal processing, and multiple layers of processing in the striate cortex.
Most humans are trichromats and they perceive color in a 3-D color space (except for bichromats and monochromats).
Vision provides a high-bandwidth input mechanism... “a picture is worth 1000 words.”
CSE (c) S. Tanimoto, Image Understanding

4. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Visual Illusions
They provide insights about the nature of the human visual system, helping us understand how it works.
Mueller-Lyer illusion
CSE (c) S. Tanimoto, Image Understanding

5. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Herman Grid Illusion
CSE (c) S. Tanimoto, Image Understanding

6. Herman Grid Illusion (dark on light)
CSE (c) S. Tanimoto, Image Understanding

7. Subjective Contour (Triangle)
CSE (c) S. Tanimoto, Image Understanding

8. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Image Representation
Sampling: Number and density of “pixel” measurements
Quantization: Number of levels permitted in pixel values.
CSE (c) S. Tanimoto, Image Understanding

9. Image Representation (cont.)
Sampling: e.g., 4 by 4, square grid, 1 pixel/cm
Quantization: e.g., binary, {0, 1}, 0 = black, 1 = white. 1 1 1 1 1 1
CSE (c) S. Tanimoto, Image Understanding

10. Aliasing due to Under-sampling
Here the apparent frequency is about 1/5 the true frequency.
CSE (c) S. Tanimoto, Image Understanding

11. CSE 415 -- (c) S. Tanimoto, 2002 Image Understanding
Quantization
Capturing a wide dynamic range of brightness levels or colors requires fine quantization. Common is 256 levels of each of red, green and blue.
Segmentation is simplified by having a small number of levels -- provided foreground and background pixels are reliably distinguished by their dark or light value.
Grayscale thresholding is typically to used to reduce the number of quantization levels to 2.
CSE (c) S. Tanimoto, Image Understanding