In the name of Allah
the compassionate, the merciful
Digital Image Processing
S. Kasaei
Sharif University of Technology
Room: CE307
E-Mail: [email protected]
Home Page: http://sharif.edu/~ceinfo
http://mehr.sharif.edu/~ipl
http://sharif.edu/~skasaei
Kasaei
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Chapter 3
Image Perception
Kasaei
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Introduction
„
In presenting the output of an imaging
system to a human observer, it is essential
to consider how it is transformed into
information by the viewer.
„
Understanding of the visual perception
process is important for developing
measures of image fidelity.
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Introduction
„
Visual image data represents spatial
distribution of physical quantities (luminance
& spatial frequencies).
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Perceived information may be presented
by attributes (brightness, color, & edges).
„
The goal is to study how the perceived
information may be represented
quantitatively.
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Introduction
Fig. 1: Simplified diagram of a cross section of the human eye.
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Introduction
Fig. 2: Cross section of the eye.
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Introduction
„
Light is defined as the electromagnetic
radiation that stimulates our vision response.
„
It is expressed as a spectral energy
distribution.
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Introduction
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Fig. 3: The electromagnetic spectrum.
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Color Representation
Fig. 4: Visible wavelengths.
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Introduction
Green
Red
Blue
Fig. 5: Typical relative luminous efficiency function of human eye.
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Introduction
„
The luminance of an object is independent
of the luminance of the surrounding objects.
„
The (apparent) brightness of an object is
the perceived luminance & depends on the
luminance of the surround.
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Introduction
Fig. 6: Simultaneous contrast. Top: Middle squares have equal luminance
but do not appear equally; Bottom: Middle squares appear almost equally
bright, but their luminance are different.
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Introduction
„
The spatial interaction of luminance from an
object & its surround creates a phenomenon
called the match band effect.
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It shows that brightness is not a monotonic
function of luminance.
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Introduction
(a)
(b)
Fig. 7: Mach band effect. (a) Gray level bar chart; (b) Luminance versus brightness.
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Introduction
„
Measurement of the mach band effect can
be used to estimate the impulse response of
the visual system [h(n)].
„
The negative lobes [in h(n)] indicate that the
neural signal at a given location has been
inhibited by some of the laterally located
receptors.
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MTF of the Visual System
„
A direct measurement of the visual system’s
modulation transfer function (MTF), is possible
by considering a sinusoidal grating of varying
contrast (ratio of the Max to Min intensity) &
spatial frequency.
„
Observation of this Fig. shows the thresholds
of visibility at various frequencies.
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MTF of the Visual System
cpd
Fig. 8: Modulation transfer function (MTF) of the human visual system.
(a) Contrast versus spatial frequency sinusoidal grating; (b) Typical MTF plot.
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MTF of the Visual System
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Human visual system is most sensitive to midfrequencies (3~10 cycles/degree) & least
sensitive to high frequencies.
„
Contrast sensitivity also depends on
orientation of the grating (max for horizontal &
vertical grating).
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MTF of the Visual System
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Angular sensitivity variations are within 3dB
(Max. deviation at 45 degree).
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Spatial frequency components, separated by
about one octave, can be detected
independently by observers.
„
Thus, visual system contains a number of
independent spatial channels, each tuned to a
different spatial frequency & orientation angle.
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Image Fidelity Criteria
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There are 2 types of fidelity criteria:
subjective & quantitative.
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Subjective criteria use rating scales such as
goodness scales & impairment scales.
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Quantitative criteria includes: average LSE,
MSE, average MS, SNR, PSNR, &
frequency weighted MS.
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Subjective Criteria
Table 1: Image goodness scales.
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Subjective Criteria
Table 2: Image impairment scales.
Sk: score,
nk: # observers,
n: # grades.
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Quantitative Criteria
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Quantitative Criteria
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Quantitative Criteria
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Quantitative Criteria
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Quantitative Criteria
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Color Representation
„
Use of color is not only more pleasing but it
also enables us to receive more visual
information.
„
While human can perceive only a few dozen
gray levels, have the ability to distinguish
between thousands of colors.
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Color Representation
Fig. 9: Visible color spectrum.
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Color Representation
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Fig. 10: Visible wavelengths.
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Color Representation
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The perceptual attributes of colors are
brightness, hue, & saturation.
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Brightness presents the perceived
luminance.
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Hue refers to its “redness”, “greenness”, ...
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Saturation is that aspect of perception that
varies most strongly as more while light is
added.
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Color Representation
Fig. 11: Hue representation.
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Color Representation
Fig. 12: Hue representation.
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Color Representation
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Fig. 13: HSV color model representation.
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Color Representation
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Fig. 14: HSV color model representation.
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Color Representation
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Fig. 15: HSV color model representation.
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Color Representation
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Fig. 16: HSV color model.
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Color Representation
Fig. 17: HIS color model.
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Color Representation
Fig. 18: HIS color model.
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Color Representation
Fig. 19: HIS color model.
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Color Representation
„
For monochromatic light sources,
differences in hues are manifested by the
differences is wavelengths.
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These definitions are somewhat imprecise.
„
Hue, brightness,& saturation all change
when either the wavelength, the intensity,
the hue, or amount of white light in a color is
changed.
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Color Representation
„
A human observer perceives color through
the stimuli of 3 different pigmented cones.
Fig. 20: Typical absorption spectra of cons in the retina, as a function of wavelength.
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Color Representation
Fig. 21: Monitor phosphor.
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Color Representation
„
A weighted sum of primaries produces a
color that cannot be distinguished by an
observer from the color of the spectrum.
Fig. 22: Additive color model
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Color Representation
Fig. 23: Primary & secondary colors of light & pigments.
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Color Representation
Fig. 24: Single chip color CCD.
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Color Representation
Table 3: Color coordinate systems
[Commission Internationale de L’Eclairage (CIE)].
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Color Representation
Table 3: Color coordinate systems (Cntd).
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Color Representation
Table 3: Color coordinate systems (Cntd).
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Color Representation
Table 4: Transformation from NTSC Receiver Primary to other coordinate systems.
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Color Representation
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Color Representation
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Color Representation
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Color Representation
Fig. 25: CIE XYZ.
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Color Representation
Fig. 26: CIE XYZ chromaticity diagram.
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Color Representation
Fig. 27: CIE XYZ chromaticity diagram.
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Color Representation
Fig. 28: CIE XYZ chromaticity diagram.
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Color Representation
Fig. 29: CIE XYZ chromaticity diagram.
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Color Representation
Fig. 30: The RGB safe-color cube.
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Color Representation
Fig. 31: RGB color model.
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Color Representation
Fig. 32: CIE Lab color models.
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Color Representation
Fig. 33: color copier.
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Color Representation
Fig. 34: System overview.
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Color Representation
Fig. 35: Pseudo color for detection.
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Color Representation
Fig. 36: Pseudo color example.
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Color Representation
Fig. 37: Color manipulation.
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The End