; Texture
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									Colour and Texture
  Extract 3-D information Using

• Extract 3-D information for performing certain
  tasks such as manipulation, navigation, and
• There are three aspects of this:
  – Segmentation of the scene into distinct objects
  – Determining the position, orientation and shape of
    each object relative to the observer
  – Feedback to modify the motion of the robot
       How Do We Recover 3-D
• There are number of cues available in the
  visual stimulus
  –   Motion
  –   Binocular stereopsis
  –   Texture
  –   Shading
  –   Contour
• Each of these cues relies on background
  assumptions about physical scenes in order to
  provide unambiguous interpretation.
Studies by psychologist and artists have demonstrated that the
presence and distribution of colours induce sensations and
convey meanings in the observer, according to specific rules -
colour images can also be retrieved according to the meaning
they convey or to sensations they provoke.
• Light is energy, specifically electromagnetic
  energy – eye (energy detector)
• The eye can distinguish between some
  types of electromagnetic energy. Those
  distinctions are seen as colours.
• The whiteness of an image area and the
  amount of light hitting the eye
  – The actual reflectance
  – The brightness of incident light
  – The incidence angle: the angle at which the light
    hits the object (one per light source, and there
    may be several)
  – The surface orientation of the area with respect
    to the viewer
"Colour is the visual effect that is caused by the spectral
composition of the light emitted, transmitted, or reflected
by objects.“
•   What is texture?
•   How to detect texture information?
•   How to measure it?
•   How to use it?
Shape and texture
• Together with colour, texture is a powerful
  discriminating feature, present almost
  everywhere in nature.
• Like colours, textures are connected with
  psychological effects. In particular, they
  emphasize orientations and spatial depth
  between overlapping object.
• There are three standard problems to do with
   – Texture segmentation is the problem of breaking an
     image into components within which the texture is
     constant. Texture segmentation involves both
     representing a texture, and determining the basis on
     which segment boundaries are to be determined. How
     texture should be represented.
   – texture synthesis seeks to construct large regions of
     texture from small example images.
   – Shape from texture involves recovering surface
     orientation or surface shape from image texture.
 Traditional Definition of Texture
• Texture refers to a spatially repeating pattern on
  a surface that can be sensed visually
• In the image, the apparent size, shape, spacing
  etc, of the texture elements (the texels) do
  indeed vary
   – Varying distances of the different texels from the
   – Varying foreshortening of the different texels.
• texture gradients - systematic change in the
  size and shape of the elements making up a
recover shape from texture
  Recover Shape From Texture
• After some mathematical analysis , one can
  compute expressions for the rate of change of
  various image texel features, such as area,
  foreshortening, and density. These texture
  gradients are functions of the surface shape as
  well as its slant and tilt with respect to the viewer.
• To recover shape from texture, one can use two-
  step process:
   – 1) measure the texture gradients
   – 2) estimate the surface shape, slant, and tilt that
     would give rise to the measured texture gradients.
Definition of
Texture is a broad
term used in
recognition to
identify image
patches (of any
size) that are
characterized by
differences in
•Techniques to extract
meaningful texture
descriptors from
image are many,
based on different
models and
•An effective
representation of
textures can be based
on statistical and
structural properties
of brightness
  Texture Content Measurement
• Textures may be described according to their
  spatial, frequency or perceptual properties.
  Periodicity, coarseness, preferred direction, degree
  of complexity are some of the most perceptually
  salient attributes of a texture.
• Feature spaces based on these attributes are
  particularly interesting for image retrieval by
  texture similarity.
        Space – based models
• Auto-correlation function A texture can be
  represented taking into account the spatial size of
  grey-level primitives. Fine textures have a small
  size of their grey-level primitives. Coarse
  textures a large size.
• Co – occurrence matrix A different way of
  measuring textures is by taking into account the
  spatial arrangement of grey-level primitives.
      Co-Occurrence Matrices
• Define a relative separation vector
  – e.g. 3 pixels across, 2 up
• Use each pair of pixels separated by the
  vector as matrix indices
• Increment this matrix element
• Shape of matrix characterises the texture
• Can be characterised by factors derived
  from it.
            Edge Frequency
• Density of microedges is characteristic of
• Apply an edge detector
  – Sobel is suitable
• Threshold result
• Compute density of edge elements
Calculate Texture



Image features

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