31-10-2012, 06:02 PM
Texture Segmentation Using Optimal Gabor Filter
Texture Segmentation Using.pdf (Size: 2.27 MB / Downloads: 143)
Abstract
Texture segmentation is one of the most important feature utilized in practical
diagnosis because it can reveal the changing tendency of the image. A texture
segmentation method based on Gabor lter is proposed in the project. This method
synthesis the information of location, color and texture features to be the wight, this
can make satisfactory segmentation according to texture of image. The experiment
shows that overall rate correctness for this method exceeds 81%.
Introduction
According to computer vision segmentation can be dene as the process of partitioning
a digital image into multiple segments, where multiple segments are sets of pixels, in
other words super pixels. Main objective of segmentation is to change and, or simplify
the representation of a digital image into something that is much more signicant
and easier to analyze. Objects and boundaries like lines, curves, etc. in images can
be normally located by using image segmentation. More accurately, the process of
assigning a tag to every pixel in an image such that pixels with the same label share
specic visual characteristics is known as image segmentation.
Image Segmentation
The outcome of image segmentation is a set of surface (especially of a curving
form) extracted from the image, a set of segments that as a group cover the entire
image. In a segment every pixels are similar with regard to computed property or
some characteristic, such as intensity, texture, or color. Neighboring segments are
considerably dierent with regard to the same characteristics.
Image segmentation can also be considered as partition of an image into set of non-
overlapping areas whose combination is the complete image, few rules to be followed
for regions resultant from the image segmentation can be conrmed as (Haralick,
1985):
All segments should be uniform and homogeneous with regard to some
characteristics
Region's interiors should be uncomplicated and without many small holes
Neighboring segments should have signicantly dierent values with regard to
the characteristic according which they are uniform
Every segment's boundaries should be simple, not blurring, and must be
spatially accurate.
Figure 1.1: Original Picture
Let this gray-scale lightning image be a original image. The value gray-scale for
this image is from 0 to 255.
What is a gray-scale? A gray-scale or grey-scale digital image is an image in
computing and photography in which a single sample is the value of each pixel, i.e., it
holds only intensity information. Images of this type are collected exclusively of gray
shades, varies to white at the strongest from black at the weakest.
Images are with two colors only, black and white also known as bi-level or binary
images in the context of computer imaging and grayscale images are dierent for
these one-bit images. Grayscale images contain many gray shades in between. Due
to the absence of any chromatic variation (no color) grayscale images are also known
as monochromatic.
In a single band of the electromagnetic spectrum (infrared, visible light, ultraviolet,
etc.) grayscale images are frequently resulted from measuring the intensity of light at
each pixel, and in a case like this when only a given frequency is captured they are
monochromatic proper. They can also be created from full color image.
These are some segmented images of previous grayscale original image using
dierent values for segmentation. This proves an image cannot have one segmentation
that can be considered to be \accurate". Only in the brain of the observer an
\accurate" segmentation exists, which can change not only among observers, but
also within the same observer at dierent instants. In many line of work like
Face Recognition, Medical Imaging, Machine Vision, Fingerprint Recognition, Script
Recognition etc. image segmentation is applied.
Texture Segmentation
For more than 50 years understanding of processes occurring in the early stages of
visual perception has been a primary research topic. For regular properties like color,
brightness, size and the slopes of lines composing gures preattentive segmentation
occurs strongly (Beck 1966, 1972, 1973, 1983; Olson and Attneave 1970). Research
into the statistical properties of preattentively discriminable texture was started by
Julesz in early 1960's. Complex topic where psychophysics meets physiology Beck
and Julesz were among the rst to deep in.
What is a texture? A measurement of the variation of the intensity of a surface,
quantifying properties such as regularity, smoothness and coarseness. You can also
explain with term is color map. Texture is mapped onto an already available surface.
A surface texture is created by the regular repetition of an element or pattern, called
surface texel, on a surface.
In computer graphics there are deterministic (regular) and statistical (irregular)
texture It's often used as a region descriptor in image analysis and computer vision.
The three principal approaches used to describe texture are structural, spectral
and statistical. Apart from the level of gray and color texture is a spatial belief
indicating what characterizes the visual homogeneity of given zone of an image in
a innite(true) image which generate another image based on the original texture
and nally analyze these two fragments by classifying them in a dierent or a same
category. In other words we can also say that the main objective is to decide if texture
samples belong to the same family by comparing them.
By using lter-bank model the process is bring to conclusion, dividing and
decomposing of an input image into numerous output image is prepared by a set
of linear image lters working in parallel which is used by the lter-bank model.
These lters gives rise to concept of joint space/ spatial-frequency decomposition by
simultaneously concentrate on local spatial interactions and on particular range of
frequencies.
Filter
In optics, device that let light pass on which have certain properties like particular
range of wavelengths, i.e., range of colors of light, while blocking the rest. Mathemat-
ical operations carry out on an image represented as a sampled, discrete-time signal
to enhance or reduce certain aspects of that signal in digital image processing is a
Filter. Filtering is process which is used in Fourier transform for signal processing in
frequency domain. Depending upon the relationship between input and output, lter
may be linear or non-linear.