2.4. EDGE DETECTION
2.4.1 INTRODUCTION TO THE
EDGE DETECTION
Edge detection is the process of localizing pixel intensity
transitions. The edge detection
have been used by object recognition, target tracking,
segmentation, and etc. Therefore, the edge detection is one of the most
important parts of image processing.
There mainly exist several edge detection methods (Sobel,
Prewitt, Roberts, Canny ). These methods have been proposed for detecting
transitions in images. Early methods determined the best gradient operator to
detect sharp intensity variations .
Commonly used method for detecting edges is to apply
derivative operators on images.
Derivative based approaches can be categorized into two
groups, namely first and second order derivative methods. First order
derivative based techniques depend on computing the gradient several directions
and combining the result of each gradient. The value of the gradient magnitude
and orientation is estimated using two differentiation masks. In this work,
Sobel which is an edge detection method is considered. Because of the
simplicity and common uses, this method is preferred by the others methods in
this work. The Sobel edge detector uses two masks, one vertical and one
horizontal. These masks are generally used 3×3 matrices. Especially, the
matrices which have 3×3 dimensions are used in matlab. The masks of the
Sobel edge detection are extended to 5×5 dimensions, are constructed in
this work. A matlab function, called as Sobel 5×5, is developed by using
these new matrices. Matlab, which is a product of The Mathworks Company,
contains has a lot of toolboxes. One of these toolboxes is image toolbox which
has many functions and algorithms. Edge function which contains several
detection methods (Sobel, Prewitt,Roberts, Canny, etc) is used by the user.
The image set, which consist of 8 images (256×256), is
used to test Sobel 3×3 and
Sobel 5×5 edge detectors in matlab.
Edge detection is one of the techniques used for detecting the
gray-level discontinuities.
It is the most common approach for detecting meaningful
discontinuities in gray-level.[3]
Edge detection is an important concept, both in the area of
image processing and in the area of object recognition. Without being able to
determine where the edges of an object fall a machine would be unable to
determine many things about that object such as shape, volume, area and so
forth. Being able to recognize an object is a key step towards the development
of artificial intelligence.
The goal of edge detection is to mark the points in a digital
image at which the luminous intensity changes sharply. Edge detection of an
image reduces significantly the amount of data and filters out information that
may be regarded as less relevant, preserving the important structural
properties of an image.
2.4.2 EDGE PROPERTIES.
Edges may be viewpoint dependent - these are edges that may
change as the viewpoint changes, and typically reflect the geometry of the
scene, objects occluding one another and so on, or may be viewpoint independent
- these generally reflect properties of the viewed objects such as surface
markings and surface shape.
Edges play quite an important role in many applications of image
processing.
Edge detection of an image reduces significantly the amount of
data and filters out information that may be regarded as less relevant,
preserving the important structural properties of an image. There are many
techniques for edge detection, but most of them can be grouped into two
categories: Search-based and zero-crossing based.
-The search-based methods detect edges by looking for maxima
and minima in the first derivative of the image, usually local directional
maxima of the gradient magnitude.
-The zero-crossing based methods search for zero crossings in
the second derivative of the image in order to find edges.
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