1. An Enhanced Cellular Automata and Image Pyramid Decomposition Based Algorithm for Image Segmentation : A New Concept Anand Prakash Shukla Suneeta Agarwal Paper id: 40

2. Agenda Introduction The tool or technique Methodology Experiments and Results Conclusion CSECS 2015 Boston2

3. Two Dimensional Cellular Automata CSECS 2015 Boston3 A (bi-directional, deterministic) cellular automaton is a triple A = (S;N;δ), Where,  S is a non-empty finite set of states,  N is the neighborhood system,  δ : S N →S is the local transition function(rule).

4. Motivation Cellular automata have a number of advantages over traditional methods of computations few simple rules, combination leads to more sophisticated emergent global behavior. Simplicity of implementation and complexity of behavior. CA are both inherently parallel and computationally simple. CA are extensible; Supports n-dimensions and m-label categories Number of labels does not increase computational time or complexity. CSECS 2015 Boston4

5. CA based Algorithm for Image Segmentation The cell state S p used here is actually a triplet (l p ;θ p ; C p ) where l p is the label of the current cell, θ p is the strength of the current cell, C p is the feature vector defined by the image. Without loss of generality we will assume θ p ϵ [0,1] CSECS 2015 _ Boston5 Automata evolution rule

6. Gaussian Pyramid Let g 0 be the input image and g 1 be the image obtained by filtering the original image by low pass filter. g 1 is called reduced version of g 0 Similarly find sequence of images g 0, g 1… g n This sequence is called Gaussian pyramid. CSECS 2015 _ Boston6

7. Laplacian and Gaussian Pyramid Generation CSECS 2015 _ Boston7

8. Proposed Method Select the input image I. Apply the Gaussian pyramid decomposition method to obtain the plane of desired level I l. Apply GrowCut algorithm to I l. Find the segmented image I l ’ Resize I l ’ to original size by pyramid reconstruction method. CSECS 2015 _ Boston8

9. Results CSECS 2015 _ Boston9

10. 10 Segmented and reconstructed at level 2 Segmented at level 0 Pixels replaced by original image

11. Conclusion. CSECS 2015 _ Boston11 A new approach has been proposed to improve the performance of GrowCut algorithm. The time taken in the segmentation decreases drastically. Segmentation quality is also good. Some background pixels are acquired at higher level of pyramid plane.