Computer Vision

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Chapter 6
Color and Shading
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Color

• Used heavily in human vision
• Color is a pixel property, making some
  recognition problems easy
• Visible spectrum for humans is 400nm (blue) to
  700 nm (red)
• Machines can see much more ex. X-rays,
  infrared, radio waves

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Causes of color

• The sensation of color is caused by the brain.
• Some ways to get this sensation include
• Pressure on the eyelids
• Dreaming, hallucinations, etc.
• Main way to get it is the response of the visual
  system to the presence/absence of light at
  various wavelengths.

• Issues that affect perception of color
• Light sources with different spectrums (compare
  the sun and a fluorescent light bulb)
• Differential reflection (e.g. some pigments) and
  absorption
• Differential refraction - (e.g. Newtons prism)
• Different distance and angle of reflection
• Sensitivity of sensor

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Some physics

• White light is composed of all visible
  frequencies (400-700)
• Ultraviolet and X-rays are of much smaller
  wavelength
• Infrared and radio waves are of much longer
  wavelength

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Color varies along a linear scale
(wavelength). Different colors typically have
different spectral albedo. Measurements by
E.Koivisto.
Violet Indigo Blue Green
Yellow Orange Red
Spectral albedos for different leaves, with color
names attached.
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The appearance of colors

• Color appearance is strongly affected by (at
  least)
• other nearby colors,
• adaptation to previous views
• state of mind
• Image from
• http//web.mit.edu/persci/people/adelson/checkersh
  adow_illusion.html

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The appearance of colors

• Color appearance is strongly affected by (at
  least)
• other nearby colors,
• adaptation to previous views
• state of mind
• Image from
• http//web.mit.edu/persci/people/adelson/checkersh
  adow_illusion.html

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Color spaces

• RGB primaries are monochromatic (formally
  645.2nm, 526.3nm, 444.4nm)
• CIE XYZ Primaries are imaginary (negative
  spectral radiance), but have other convenient
  properties
• Also
• CMY subtractive color space used for printing
• HSV perceptually salient space for several
  applications
• YIQ used for TV good for compression

• A choice of three primaries yields a linear color
  space --- the coordinates of a color are given
  by the weights of the primaries used to match it.
• Choice of primaries is equivalent to choice of
  color space.

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Comparing color spaces
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Color cube

• R, G, B values normalized to (0, 1) interval
• humans perceive gray for triples on the diagonal
• Pure colors on corners

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Color receptors and color deficiency

• Trichromacy is justified - in most people, there
  are three types of color receptor, called cones,
  which vary in their sensitivity to light at
  different wavelengths (shown by molecular
  biologists).

• Some people have fewer than three types of
  receptor most common deficiency is red-green
  color blindness in men.

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Chapter 7
Texture
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Texture

• Texture is a description of the spatial
  arrangement of color or intensities in an image
  or a selected region of an image.
• Structural approach Texture is a set of
  primitive texels in some regular or repeated
  relationship.

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Texture

• Finding texels is difficult in most images

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Statistical texture

• Most common approach in computer vision to
  compute statistics in the image to represent
  texture.
• Computationally efficient
• Can be used for classification and segmentation
• Simple approach apply edge detection
• Number of edge pixels is one measure of texture
• Orientation is another (average or histogram)

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Co-occurrence matrix

• A co-occurrence matrix is a 2D array N (or C) in
  which
• Both the rows and columns represent a set of
  possible image values
• Nd(i,j) indicates how many times value i
  co-occurs with value j in a particular spatial
  relationship d.
• The spatial relationship is specified by a vector
  d (dr,dc).
• Essentially a 2D histogram storing a particular
  spatial relationship between intensity values.

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Co-occurrence matrix
1
0 1 2
1 1 0 0 1 1 0 0 0 0 2 2 0 0 2 2 0 0
2 2 0 0 2 2
j
i
0 1 2
6 0 4 2 2 0 0 0 4
d (0,1)
C
d
co-occurrence matrix
gray-tone image
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Co-occurrence features
Numeric features computed from the co-occurrence
matrix can be used to represent and compare
textures.
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Co-occurrence matrix

• How do you choose d?
• Are the textures small, medium, large?
• One suggestion (Zucker and Terzopoulos) use a
  statistical test to select value(s) that have the
  most structure.

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Texture representation

• Another method to represent image texture is by
  convolving the image with a set of filters.
• Each pixel is represented by a vector of filter
  responses, the texture signature
• Strong response when image is similar to filter
• Weak response when not similar
• The filters that are typically used look like
• Spots
• Bars

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Filters are templates

• Applying a filter at some point can be seen as
  taking a dot-product between the image and the
  filter
• Both are viewed as 1D vectors rather than 2D
  images
• Filtering the image is a set of dot products

• Insight
• filters look like the effects they are intended
  to find
• filters find effects they look like
• why?

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Filters are templates

Positive responses
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Filters are templates

Positive responses
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Scaled representations

• Big bars and little bars (elongated features like
  limbs or stripes) are both interesting features
  to detect in an image
• Also could be dots or other shapes
• Inefficient to detect big bars with big filters
• And there is superfluous detail in the filter
  kernel

• Alternative
• Apply filters of fixed size to images of
  different sizes
• Typically, a collection of images whose edge
  length changes by a factor of 2 (or the square
  root of 2)
• This is a pyramid by visual analogy (sometimes
  called a Gaussian pyramid)

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A bar in the biggest image is a hair on the
zebras nose in middle images, a stripe in the
smallest, the animals nose
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How can these textures be represented?
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Representing textures

• Real textures are made up of patterns of
  irregular subelements
• Representation
• find the subelements, and represent their
  statistics
• What are the subelements?
• not well defined, in general
• usually reduced to most basic shapes spots and
  bars at various sizes and orientations

• How do we find them?
• by applying filters
• After applying bar and spot filters apply
  statistics locally
• mean
• standard deviation
• histograms

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Filters (not to scale)
Original image
Filter responses