Image Understanding

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Image Understanding
Outline Motivation Human vision and
illusions Image representation Sampling
Quantization Thresholding
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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.
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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.
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Visual Illusions
They provide insights about the nature of the
human visual system, helping us understand how it
works.
Mueller-Lyer illusion
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Herman Grid Illusion
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Herman Grid Illusion (dark on light)
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Subjective Contour (Triangle)
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Image Representation
Sampling Number and density of pixel
measurements Quantization Number of levels
permitted in pixel values.
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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
0
0
0
1
0
0
1
1
1
0
0
0
0
0
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Aliasing due to Under-sampling
Here the apparent frequency is about 1/5 the true
frequency.
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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.