21-12-2012, 06:28 PM
EDGE DETECTION USING EVOLUTIONARY ALGORITHMS
EDGE DETECTION USING.pptx (Size: 1.48 MB / Downloads: 30)
INTRODUCTION
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.
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.
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
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)
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