1. EDGE DETECTION USING EVOLUTIONARY ALGORITHMS

2. INTRODUCTION What is edge detection? Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Those boundaries are called Edges. Hence, edges are significant local changes of intensity in an image. Edges typically occur on the boundary between twodifferent regions in an image.

3. What are the applications of Edge detection? Edge is one of the simplest and most important features of image, and this feature is broadly used in image recognition, segmentation, enhancement and compression. The purpose of edge detection is not only to extract the edges of the interested objects from an image, but also to lay the foundation for image fusion, shape extraction, image segmentation, image matching and image tracking. Edge detection is a fundamental tool used in most image processing applications to obtain information from the image as a precursor step to feature extraction and object segmentation.

4. A Tulips image Edges of the Tulips image

5. WHAT CAUSES INTENSITY CHANGES Physical edges are produced by variation in the reflectance, illumination, orientation and depth of scene surfaces. Various physical events cause intensity changes.  Geometric events object boundary (discontinuity in depth and/or surface colour and texture) surface boundary (discontinuity in surface orientation and/or surface colour and texture)  Non-geometric events direct reflection of light, such as a mirror shadows (from other objects or from the same object) inter-reflections

6. Edge Models Edges can be modelled according to their intensity profiles. Step edge Ramp edge Roof edge

7. THE STEPS IN EDGE DETECTION The edge detection process generally includes five steps: Filtering: Filtering out the noise from the image and improve performance of the edge detector. Enhancement: Emphasising pixels which have important change in local intensity. Detection: Identifying the edges and thresholding. Link: Linking the broken edges. Localisation: Locating the edge accurately and estimating the edge orientation (edge and orientation map)

8. Problems regarding Edge Detection The problem is that in general edge detectors behave very poorly. While their behaviour may fall within tolerances in specific situations, in general edge detectors have difficulty adapting to different situations. The quality of edge detection is highly dependent on lighting conditions, the presence of objects of similar intensities, density of edges in the scene, and noise. While each of these problems can be handled by adjusting certain values in the edge detector and changing the threshold value for what is considered an edge, no good method has been determined for automatically setting these values, so they must be manually changed by an operator each time the detector is run with a different set of data. In the presence of noise, detection of edges becomes very difficult because both edges and noise are characterized by high frequency. Erroneous edge detection may lead to artefacts in severe cases like medical, security and biometrics.

9. Evolutionary Algorithms  Evolution as it is observed in nature is initiated  Function Optimizers  Well suited to solve complex computational problems such as optimization of objective functions, pattern recognition,image processing, filter modelling,etc.

10. Various Heuristic Algorithms Ant Colony Optimization,ACO, mimics the behavior of ants foraging for food Genetic Algorithm,GA, are inspired from Darwinian Evolutionary Theory Simulated Annealing,SA, is designed by the use of thermodynamic effects Artificial Immune Systems,AIS, simulate biological immune systems Bacterial Foraging Algorithm, BFA, comes from search and the optimal foraging of bacteria. Particle Swarm Optimization,PSO, simulates the flock of birds

11. Bacteria Foraging Algorithm INTRODUCTION  Developed in Passino (2002)  Exploits the foraging behaviour of Bacteria  Already applied in the optimal control engineering, harmonic estimation, transmission loss reduction, machine learning, active power filter design, color image enhancement etc.

12. What is Bacteria Foraging Technique? Foraging can be modeled as an optimization process where bacteria seek to maximize the energy obtained per unit time spent during foraging. Four Stages in the life cycle of Bacteria  Chemo taxis  Swarming  Reproduction and  Elimination and Dispersal These stages in the search space generate an optimal solution to the problem of optimization.

13.  In Chemo taxis stage, the bacteria either resort or tumble followed by a tumble or make a tumble followed by a run or swim. Movement stage of Bacteria  In Swarming, each E. coli bacterium signals another bacterium via attractants to swarm together. Cell to cell signalling stage.

14.  In the Reproduction the least healthy bacteria die and of the healthiest each bacterium splits into two bacteria, which are placed at the same location.  In the Elimination and Dispersal stage, any bacterium from the total set can be either eliminated or dispersed to a random location during the optimization. This stage prevents the Bacterium from attaining local optimum.

15. Modified Bacterial Foraging Technique for Edge Detection The original bacterial foraging (BF) Technique modified to make it suitable for edge detection. The nutrient concentration at each position is calculated using a derivative approach. Modifications 1.Search Space 2-dimension search space of bacteria consists of the x and y coordinates of a pixel in an image.

16. 2. Chemo taxis  It decides the direction in which bacterium should move.  Goal of this stage is to let the bacterium search for the edge pixels of the image.  Another goal is to keep the bacterium away from the noisy pixels.  Probabilistic derivative approach is used to find the edge pixels.

17. PSO