BINARY IMAGE PROCESSING
This module presents the major components of a complete (albeit trivial)
vision system.
Contents
INTRODUCTION
The simplest type of image which is used widely in a variety of industrial and
medical applications is
binary,
i.e. a black-and-white or silhouette image.
Binary image processing has several advantages
but some corresponding drawbacks:
Advantages
-
Easy to acquire:
simple digital cameras can be used together with very simple framestores,
or low-cost scanners, or thresholding may be applied to grey-level images.
-
Low storage:
no more than 1 bit/pixel, often this can be reduced as such images are
very amenable to compression (e.g. run-length coding).
-
Simple processing:
the algorithms are in most cases much simpler than those applied to grey-level
images.
Disadvantages
-
Limited application:
as the representation is only a silhouette,
application is restricted to tasks where internal detail is
not required as a distinguishing characteristic.
-
Does not extend to 3D:
the 3D nature of objects can rarely be represented by silhouettes.
(The 3D equivalent of binary processing uses voxels, spatial
occupancy of small cubes in 3D space).
-
Specialised lighting is required for silhouettes:
it is difficult to obtain reliable binary images without restricting
the environment. The simplest example is an overhead projector or
light box.
THRESHOLDING
In the simplest case, an image may consist of a single object or several
separated objects of relatively high intensity, viewed against a background
of relatively low intensity. This allows figure/ground separation
by thresholding.
In order to create the two-valued binary image a simple threshold may be
applied so that all the pixels in the image plane are classified into
object and background pixels.
A binary image function can then be constructed such that pixels above the
threshold are foreground (``1'') and below the threshold are background
(``0'').
You can see the effect of thresholding by moving the slider in this applet.
The slider sets the threshold and the image on the right shows the result
and binary image. In this case, white pixels are above the threshold and
black are below it.
- Exercise 1:
This image is of a white square against a black
background. Try to find a threshold which makes the square completely
white and the background completely black.
-
Exercise 2: This image is of a bracket and a J-clamp.
Try to find a threshold which separates the bracket from both the clamp and
the background.
Then try to separate the objects from the background.