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Feature Extraction Using GLCM

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Filter used for Denoising:

2-D Gabor Filter:

Gabor filter, Gabor filter bank, Gabor transform and Gabor wavelet are widelyapplied to image processing, computer vision and pattern recognition. This

function can provide accurate time-frequency location governed by theUncertainty Principle. A circular 2-D Gabor filter in the spatial domain has the

following general form:-

Where i 1 ; u is the frequency of the sinusoidal wave; controls the orientation

of the function and s is the standard deviation of the Gaussian envelope. SuchGabor filters have been widely used in various applications. In addition to accurate

time-frequency location, they also provide robustness against varying brightnessand contrast of images.

Image denoising using wavelet transform

According to actual image characteristic, noise statistical property and frequencyspectrum distribution rule, people have developed many methods of eliminating

noises, which approximately are divided into space and transformation fieldsThe transformation field is management in the transformation field of images, and

the coefficients after transformation are processed. Then the aim of eliminating

noise is achieved by inverse transformation, like wavelet transform. Successfulexploitation of wavelet transform might lessen the noise effect or even overcome itcompletely. There are two main types of wavelet transform- continuous and

discrete. The denoising of a natural image corrupted by Gaussian noise is a classicproblem in signal processing. The wavelet transform has become an important tool

for this problem due to its energy compaction property.

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Indeed, wavelets provide a framework for signal decomposition in the form of a

sequence of signals known as approximation signals with decreasing resolutionsupplemented by a sequence of additional touches called details. Denoising or

estimation of functions, involves reconstituting the signal as well as possible on thebasis of the observations of a useful signal corrupted by noise. The methods based

on wavelet representations yield very simple algorithms that are often more

powerful and easy to work with than traditional methods of function estimation . It

consists of decomposing the observed signal into wavelets and using thresholds toselect the coefficients, from which a signal is synthesized.

Image Segmentation Using Threshold Technique

The division of an image into meaningful structures, image segmentation, is often

an essential step in image analysis, object representation, visualization, and many

other image processing tasks. In chapter 8, we focused on how to analyze and

represent an object, but we assumed the group of pixels that identified that

object was known beforehand. In this chapter, we will focus on methods that find

the particular pixels that make up an object.

Thresholding Method:

Bays Shrink