13-08-2012, 11:45 AM
Image Segmentation in Programming Environment MATLAB
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Abstract
The task of image segmentation is a first step in
many computer vision methods. The paper deals
with image segmentation methods with application
in programming environment Matlab. Next are
described some chosen algorithms and their
advantages and disadvantages.
Introduction
Main goal of image segmentation is to divide an
image into parts that have a strong correlation with
objects or areas of the real world contained in the
image. In image processing useful pixels in the image
are separated from the rest. The result of image
segmentation is a set of segments that collectively
cover the entire image, or a set of contours extracted
from the image [1]. A segmentation could be used
for object recognition, occlusion boundary
estimation, image compression, image editing, etc.
Many segmentation problems can be solved
successfully using lower-level processing only (e.g.
contrasted printed characters). Complex scene as
face detection is processed then cooperation with
higher processing levels which use specific
knowledge of the problem domain is necessary.
Thresholding
It is widely used in simple applications. In
brightness threshold, all the pixels brighter than a
specified brightness level are taken as 1 and rest are
left 0. In this way we get a binary image with useful
image as 1 and unwanted as 0. Sufficient contrast on
objects and background is necessary to do the
thresholding [2]. Thresholding is the transformation
of an input image / to an output binary image g as
follows
Edge-based segmentation
In edge detection special algorithms are used to
detect edges of objects in the image. Edges mark
image locations of discontinuities in gray-level, color,
texture, etc. Edges typically occur on the boundary
between two different regions in an image. There are
XI International PhD Workshop
OWD 2009, 17–20 October 2009
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a number of algorithms for this, but these may be
classified as derivative based where the algorithm
takes first or second derivative on each pixel or
gradient based where a gradient of consecutive pixels
is taken in x and y direction. Operation called kernel
operation is usually carried out. A kernel is a small
matrix sliding over the image matrix containing
coefficients which are multiplied to corresponding
image matrix elements [1]
Conclusion
The three image segmentation methods presented
in this paper are the methods used in practice and
they give good results for specific application. All
methods were implemented in MATLAB. Threshold
segmentation is good for simple images. If objects
do not touch each other, and if their gray-levels are
clearly distinct from background, thresholding is a
suitable segmentation method. Thresholding is
computationally inexpensive and fast. Edge-based
segmentation is the earliest segmentation approaches
and still remains very important. The more prior
information that is available to the segmentation
process, the better the segmentation results that can
be obtained. The most common problems of edgebased
segmentation, caused by image noise or
unsuitable information in an image. The perceptual
color image segmentation algorithm is slow and has
larger segments, but has better results in complex
images.