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2.  Introduction
 Types of Digital Image Processing
 History
 Basic Concepts
 Image functions
 Digital image properties
 Uses
 Conclusion

3.  computer converts the analogue image, in this
case a videotape, to a digital image by dividing
it into a microscopic grid and numbering each
part by its relative brightness.
 Specific image processing programs can then
radically improve the contrast, for example by
stretching the range of brightness throughout
the grid from black to white, emphasizing
edges, and suppressing random background
noise that comes from the equipment rather
than the document.

4.  In order to simplify the task of computer
vision understanding, two levels are usually
distinguished; low-level image processing
and high level image understanding

5.  Many of the techniques of digital image
processing, or digital picture processing as
it was often called, were developed in the
1960s at the Jet Propulsion Laboratory, MIT,
Bell Labs, University of Maryland, and few
other places, with application to satellite
imagery, wire photo standards conversion,
medical imaging, videophone, character
recognition, and photo enhancement.
 In the 1970s, digital image processing
proliferated, when cheaper computers
Creating a film or electronic image of any
picture or paper form.

6.  A signal is a function depending on some
variable with physical meaning.
 Signals can be
◦ One-dimensional (e.g., dependent on time),
◦ Two-dimensional (e.g., images dependent on
two co-ordinates in a plane),
◦ Three-dimensional (e.g., describing an object in
space),
 Or higher dimensional.
 Pattern recognition aims to classify data
(patterns) based on either a priori
knowledge or on statistical information
extracted from the patterns.

7.  The image can be modeled by a continuous
function of two or three variables.
 Arguments are co-ordinates x, y in a plane,
while if images change in time a third
variable t might be added.
 The image function values correspond to the
brightness at image points.
 The function value can express other
physical quantities as well (temperature,
pressure distribution, distance from the
observer, etc.).

8.  Metric properties of digital images
 Topological properties of digital images

9. Distance:
The distance between two pixels in a digital
image is a significant quantitative measure.
Pixel adjacency:
 4-neighborhood
 8-neighborhood
Border and edge:
The border is a global concept related to a
region, while edge expresses local
properties of an image function

10.  Topological properties of images are
invariant to rubber sheet transformations.
 Convex hull is used to describe topological
properties of objects.
 The convex hull is the smallest region which
contains the object, such that any two points
of the region can be connected by a straight
line, all points of which belong to the
region.

11.  A scalar function may be sufficient to
describe a monochromatic image.
 vector functions are to represent color
images consisting of three component
colors.

12.  Further, surveillance by humans is dependent
on the quality of the human operator and lot
off actors like operator fatigue negligence may
lead to degradation of performance.
 These factors may can intelligent vision system
a better option.
 As in systems that use gait signature for
recognition in vehicle video sensors for driver
assistance.