24-09-2012, 03:09 PM
Edge Detection Using Morphological Method and Corner Detection Using Chain Code Algorithm
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ABSTRACT
In this paper we show a very good
approach to detect edge and corner of any image.
Edges and corners are very important part of an
image .In our present days edge and corner
detections is very essential for object
identification. In this paper we show edge
detection using morphological method and
corner detection using chain code algorithm.
This two method can work on any type of image.
INTRODUCTION
An image may be defined as a twodimensional
function, f(x, y), where x and y are
the spatial coordinates, and the amplitude of f at
any pair of coordinates (x, y) is called the
intensity or grey level of the image at the point.
When x, y, and the amplitude values of f are all
finite, discrete quantities, we call the image a
digital image. The terms morphology commonly
denotes a branch of biology that deals with the
form and structure of animals and plants.
Morphology as a tool for extracting image
components that is useful in the representation
and description of region shape boundaries and
corners.
MORPHOLOGICAL EDGE
DETECTION
First we need to read an image. That image
may be RGB image. In RGB image each
pixel of that image will be in the range
between 0 to 6.
Then we convert that image into greyscale
image. In greyscale image each pixel of that
image will be in the range between 0 to 255
Then we convert that image into binary
image. In binary image each pixel of that
image will be either 0 or 1.
HIT-OR-MISS TRANSFORM
The hit-or-miss transformation may be
defined as morphological operator, which is used
for making one pixel thick image from two or
more pixel thick image. A small odd size mask
typically, 3 × 3, can be scanned over a binary
image. The hit-and-miss transformation operates
as a binary matching between the image and the
structuring element. If the foreground and
background pixels in the structuring element
exactly match the foreground and background
pixels in the image, then the pixel underneath the
origin of the structuring element is set to the
foreground color. If it does not, that pixel is set
to background color.
THINNING OPERATION
Thinning is a morphological operation
that is used to remove selected foreground pixels
from binary images, somewhat like erosion or. It
can be used for several applications, but is
particularly useful for skeletonization. In this
mode it is commonly used to tidy up the output
of edge detectors by reducing all lines to single
pixel thickness. Thinning is normally only
applied to binary images, and produces another
binary image as output. The thinning operation is
related to the hit-or-miss transform.
CORNER DETECTION
Corner detection is an important aspect
in image processing and researchers find many
practical applications in it. There are many
algorithm for detect corner like, Corner detector
based on global and local curvature properties.
But, we use the Freeman chain codes algorithm
for detect true corner of an object in an image.
CHAIN CODES ALGORITHM
Chain codes are used to represent a
boundary by a connected sequence of straightline
segments of specified length and direction.
Typically, this representation is based on 4- or 8-
connectivity of the segments. The direction of
each segment is coded by using a numbering
scheme such as ones.
CONCLUSION
The mapping algorithm has been tested
and validated in visualizing and transcribing
thinned binary images into chain code by using
three thinned binary image objects that are cube,
stair, and rectangle. The results show that the
visualizing algorithm capable to visualize
thinned binary image into rectangular chain code
cells. Reciprocally the transcribing algorithm
also capable to transcribe the rectangular chain
code cells into Vertex Chain Code. Both of these
algorithms is called The Mapping Algorithm of
Rectangular Vertex Chain Code. In this paper we
proposed a novel simple and efficient method for
detection of edge or boundary and corner.