טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentMittelman Roni
SubjectSegmentation and Classification of Textures based on
Statistical Models of Wavelet Transforms
DepartmentDepartment of Electrical Engineering
Supervisor Professor Moshe Porat


Abstract

This study deals with the application of multi-resolution statistical models to the tasks of texture classification and segmentation. These tasks are at the core of many applications such as document processing and medical image processing. In this work

we choose to characterize textures using the statistical distribution of their multi-resolution

representation’s coefficients, and employ various statistical models for this

purpose. First we describe the statistical models which we employ in this work, and

make the distinction between models that assume that every coefficient of the

multi-resolution representation is statistically independent as well as models that can

account for statistical dependencies between the coefficients.

In this work we propose new feature statistics for wavelet based texture classification,

and an appropriate feature weighting method. We show that the classification results

of the new method are superior to those reported recently in the literature using the same

experimental tools and testing procedures.


When considering segmentation of multi-textured images, it is common to enforce

additional prior information for the characterization of the texture classes. Two

such prior models which are employed in this work are the level-set framework that

can enforce smooth texture boundaries, and the Markov random fields framework

that can enforce the smoothness of the different texture regions. Using the level-set

framework we propose a new multi-scale level-set supervised texture segmentation

scheme, which employs a coarse to fine strategy, thus we show that the new scheme

may have advantages over presently available level-set supervised texture

segmentation schemes, where the segmentation is performed on a single level.

Finally, we employ the Markov random fields framework and consider the use of a

pre-processed feature space common in the literature for unsupervised texture

segmentation using Gaussian Markov random fields. We show that the pre-processed

feature space has certain advantages for this task due to its Gaussian statistics.