Edge Detection Technique using HSI and Fuzzy Interference System

The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories 1) clustering methods, 2) region-based methods, 3) hybrid methods, and 4) Bayesian Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Interest in digital image processing methods stems from two principals applications areas improvement of pictorial information for human interpretation, and processing of image data for storage, transmission, and representation for autonomous machine perception. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. In many image processing applications, we have to use expert knowledge to overcome the difficulties (e. g. object recognition, scene analysis). Fuzzy set theory and fuzzy logic offer us powerful tools to represent and process human knowledge in form of fuzzy if-then rules. On the other side, many difficulties in image processing arise because the data/tasks/results are uncertain. This uncertainty, however, is not always due to the randomness but to the ambiguity and vagueness. Beside randomness which can be managed by probability theory we can distinguish between three other kinds of imperfection in the image processing. The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. For an image formation model, discontinuities in image brightness are likely to correspond to- a) Discontinuities in depth b) Discontinuities in surface orientation c) Changes in material properties Variations in scene illumination