Abbas Cheddad, Joan Condell, Kevin Curran and Paul Mc Kevitt Intelligent Systems Research Centre Faculty of Computing and Engineering University of Ulster United Kingdom s: cheddad-a [ AT ] .ulster.ac.uk Web:

Presentation Outline Introduction Applications Our Method Examples Conclusions

Introduction Segmentation techniques can be classified into two categories: boundary-based techniques and region-based techniques. Region-based algorithms include region growing, region splitting and region merging k-means minimize the mean squared distance from each data point to its nearest center (k) Dynamic thresholding determined by examining repetitively the minima between two peaks in the bi-model image histogram Edge detection Sobel, Prewitt, Laplacian, and Canny Voronoi Diagram (VD) based on selected feature points residing along the image edges of high gradient magnitude (M. A. Suhail et al. and M. Burge and W. Burger) 3

4 Introduction Applications Remote sensing Vehicle and robot navigation Medical imaging Optical Character Recognition (OCR) Skeletonization Scene analysis Shape reconstruction, etc

5 Methodology Image segmentation remains a long-standing problem in computer vision and has been found difficult and challenging for two main reasons (Z. Tu and S. Zhu): The fundamental complexity of modelling a vast amount of visual data that appears in the image is a considerable challenge The intrinsic ambiguity in image perception, especially when it concerns the so-called unsupervised segmentation (e.g., a decision whereby a region cut is not a trivial task)

6 Methodology Voronoi Diagram (VD): Given a set of 2D points, the Voronoi region for a point Pi is defined as the set of all the points that are closer to Pi than to any other points. The dual tessellation of VD is known as the Delaunay Triangulation (DT). VD of four generatorsVD of two generatorsVD of three generators

7 Methodology VD of n scattered generators

8 Methodology Various literature studies have tended to apply VD on the image itself (after binarizing it and capturing its edges). This is usually time consuming Thus, VD is constructed from feature generators that result from gray intensity frequencies. O (n log n), where n<=255

13/02/20149 Methodology Local flip effect on the histogram Voronoi Diagram in red and Delaunay Triangulations in blue applied on an image histogram

10 Examples

12 Conclusions We have presented our novel algorithm for image segmentation based on points geometry derived from the image histogram Our proposal shows less complexity while maintaining high performance This work is a pre-processing phase for our ongoing research on adaptive digital image Steganography. The latter is the science of concealing confidential data in multimedia medium in an imperceptible way

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