Abstract:
Texture analysis is an extremely active and useful area of research. In texture
analysis the invariance to rotation, scale and translation are the most typical requirements.
Moreover, gray-scale invariance is another important issue. It arises due to the reason that
a texture may be subject to different levels of illumination. The purpose of this study is to
investigate some inexpensive approaches that are rotation and gray scale invariant and to
large extent translation invariant as well. There are three different types of approaches,
which have been addressed in this dissertation.
In the first approach, we have done texture analysis using Radon Transform (RT)
based Hidden Markov Model (HMM). We have introduced three different ways to extract
feature vectors using RT. All three give rotation invariant features, while the last one gives
rotation, as well as, gray scale invariant features. The textures in this case have been taken
from Brodatz album. Due to the inherent property of the RT, we are able to capture the
directional features of a certain texture having arbitrary orientation. This set of directional
features is used for training of an HMM specifically for that particular texture. Once all
the HMMs have been trained, the testing is carried out by using any one of these textures
at random with arbitrary orientation.
The second approach is somewhat similar to the above one except that the
modified or Differential Radon Transform (DRT) has been used instead of the ordinary
RT. Hence, we are able to capture the features which are not only rotation but are also
gray scale invariant. The reason for the later property is that, unlike the ordinary RT, the
DRT is based on the differences between adjacent pixels instead of summing up the pixel
values. These features have been used for training of HMMs, one for each texture, and
finally testing is carried out. Similar experimentation has been done to extract features
using both RT and DRT to give low pass and high pass features. The training and testing
process using HMM has been done in a similar manner as above.
The third approach is quite different from the above two approaches. In this
approach, some principal direction of a texture is defined. Once this direction is estimated,
discrete wavelet transform is applied in that particular direction to extract features. These
features are then used for classification by k-nearest neighbor classifier. There are two
definitions of principal direction, which have been proposed in the dissertation. In case of
vthe first definition, Principal Component Analysis (PCA) has been used to estimate this
principal direction. In the case of second definition, the direction has been found out by
using DRT. This scheme is computationally lighter compared to the previous one.
However, the third approach is limited to anisotrpic textures only unlike the previous
method
Considering the percentage of correct classification as figure of merit, we have
carried out the performance evaluation of the above three approaches. The average result
has been found to be 95% approximately and the best result has been close to 100%.