**ABSTRACT**

Edge
detection is one of the most important steps leading to the analysis of
processed image data. Image texture
segmentation is an important problem and occurs frequently in many
image-processing applications. The image can be segmented by detecting there
boundaries. This paper presents some
algorithms that are required for Edge detection of textured image. Algorithms
for Edge detection are presented that works directly for non-textured image.
These algorithm can be carried out for edge detection of textured images also
by preprocessing the image through discrete Filters. Experimental results on
images containing various synthetic and natural textures have been carried out
and a comparison of existing techniques is shown.

**INTRODUCTION**

Edge detection is the
process of detecting the edges of the image.
This task becomes particularly difficult in the case of textured images.
Texture is a term that refers to properties that represent the surface of an
object. We might define texture as
something consisting of mutually related elements therefore we consider a group
of pixels . Image segmentation is the
process of partitioning an image into homogenous regions. The existing segmentation methods are
commonly classified according to the texture description. The algorithm for texture segmentation deals
with first extracting textural features of image by Gabor transform or Discrete
Wavelet Transform. The statistical Features can be derived from these images.
In case of Wavelet transform Wavelet Pyramid or Wavelet packet Filters are
used. In case of proposed method the image is first smoothened and passed
through Wavelet pyramid filters, the image is than passed through Histogram
filter. Edge detection algorithms are then applied on these images so as to get
the boundary of different textures. For
non-textured image boundary can be detected by smoothing and then directly
applying edge detection algorithm .

**Basic Concepts**

**Statistical analysis**

·

**Co-occurrence matrix:**This method of texture description is based on the repeated occurrence of some Grey level configuration in the texture; this configuration varies rapidly with distance slowly in coarse textures. An occurrence of some grey level configuration may be described by a matrix of relative frequencies P_{f}_{,d }(a, b) describing how frequently two pixels with the grey level a, b appear in the window separated by a distance d in the direction f.**Textural Feature Extraction**

The filtering approaches to texture
classification generally compute the filter output statistics as features.
Figure 1 describes a typical scheme of textural feature extraction. The
textured image is filtered through a bank of filters tuned to different
frequencies. The filtered image undergoes a nonlinear transform followed by a
smoothing operation for output statistics computation as features.

**Gabor Filter**

Using a Gabor filter bank, an image can be decomposed into
orientational components lying in a specified frequency range. This decomposition simplifies higher level
image processing like extraction of contours or pattern recognition.Here, the
input textural image is filtered through a filter bank having Gabor Filters and
Gaussian smoothing filters in cascade. The feature vector is constructed from
the output statistics of the images obtained from each branch of the filter
bank.

**Discrete Wavelet Transform**

Wavelet transform is capable of providing the time and
frequency information simultaneously, hence giving a time-frequency
representation of the signal. A
multi-resolution approach is suggested to give a robust segmentation
process. This is when an image is
decomposed and represented at different scales.
The discrete wavelet transform is implemented with a 2-channel analysis
filter bank . First the low pass filter
(intensity) and high pass filter (texture) are applied to the rows of the
image. After this stage the columns are
down sampled by a factor of 2 (the odd numbered columns are discarded). After this stage the same technique is
applied to the 2 resultant images, however this time the filters are applied to
the columns of the image and the rows are then down sampled. The resultant is 4 frequency bands, each one
quarter of the original size, which makes up the original image.

·
Wavelet Pyramid Decomposition:

The pyramid wavelet transform recursively decomposes sub-signals in the
low frequency channel. This has the effect of concentrating the energy of the
image towards the low end of the frequency spectrum, emitting the high
frequency information and approaching an approximation of the image.

·
Wavelet
Packet (Tree) Decomposition.

Pyramid wavelet transform may not be suitable for quasi-periodic
signals, whose dominant frequency channels are located in the middle frequency
region.

**Outline Of Propose Method**

The algorithm for textured and non-textured images is given. for
non-textured images only smoothing and edge detecting algorithm is required,
while for detecting edge of textured image the image need to be preprocessed by
passing it through wavelet pyramid.

·
Algorithm For Textured Image
Edge Detection

1) Preprocess the
image by passing through Low
Pass filter.

2) Pass the image
through Wavelet Pyramid Filter.

3) Segment the image
by passing through Histogram filter.

4) Detect the image
by Edge Detection algorithm.

·

**Edge Detection algorithm**
1) From difference
of Pixel

2) Read the input
Pixel.

3) Read another
input pixel.

4) Take the
difference of the RGB values of two pixels.

5) Display the
difference of Pixels.

·

**From Image Difference.**
1)
Read the image.

2)
Smooth the image
by applying smoothing Filter.

3)
Take the
difference of these images.

·

**Histogram filter Algorithm**
1)
Read image
pixels.

2)
Convert RGB into
HSV Hue saturation value.

3)
Find intensity
part of HSB.

4)
Create Histogram
for 255 binary level.

5)
if histogram
value lies between 10 and 50 make it constant less divide it by 4.

6)
display output
image

·

**Wavelet Pyramid**
1)
Read Image.

2)
Filter the image
through Low pass and high pass Filter.

3)
Downsample
output of (b) by 2

4)
Repeat step (a)
& (b) such that low passed image is passed as input of (b).

5) Above step is repeated till a certain level of
pyramid.

**RESULTS**

The method has been tested on a large number of
various images including synthetic and natural textures. The method of finding
edge requires less time as it is simply the difference, rather than applying
filter mask like as used in laplacian and sobel operator. Although, this method showed good results it
appeared that is was not robust (this is due to the random nature of genetic
algorithm).shows an example of
Edge Detection of famous Lena image, the
output has taken less calculations. shows output from Typical textured image used image processing,, the
result of detecting edges of this from algorithm mentioned for textured image
edge detection is also found good

**CONCLUSION**

This paper has presented a novel algorithm for
Edge detection of textured images. The main contribution is on applying a
combination of Wavelet pyramid, histogram filter and Edge Detection Filter. The method presented has shown good behavior
for both textured and non-textured images.
The algorithm for Edge Detection requires less computation than edge
detection through masking since the computation required for masking requires
convolution. Algorithm for textured
image may fail if the textures of images have very low intensity difference. A
combination of Gabor Filter along with Histogram filter also can be used to
segment multi-textured image.

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