Diploma and Master Theses (authored and supervised):

M. Cerman:
"Structurally Correct Image Segmentation using Local Binary Patterns and the Combinatorial Pyramid";
Supervisor: W. Kropatsch, Y. Haxhimusa; Institute of Computer Graphics and Algorithms, 2015; final examination: 2015-10-05.

English abstract:
Local Binary Patterns (LBP) were first introduced in 1996 as a way to locally
describe the texture of a 2­dimensional surface. The basic LBP operator captures the spatial
structure information around a pixel by thresholding the 8 pixels in its 3*3 neighborhood
by its gray scale value and encoding the result as a 8­bit binary number. The histogram of
these binary numbers describes the texture within a specified region. Current image
segmentation methods using the LBP texture descriptor utilize the LBP around a pixel as an
additional feature and perform a segmentation using a clustering method, or they employ
hierarchical windowed split­and­merge techniques in conjunction with the LBP histogram.
Our method in contrast, uses the LBP to define a representative image which is structurally
equal to the original image, and which is then used to partition the image into irregular
regions. The minimal internal and external contrast between these regions defines the order
of merging operations. Our algorithm additionally uses the combinatorial pyramid data
structure to preserve the topological properties of the image, capture the merging history,
and enable a full top­to­bottom reconstruction of the image.

Created from the Publication Database of the Vienna University of Technology.