CHAPTER ONE

INTRODUCTION

1.1 Background

Edge detection can be defined as the process of identifying set of connected pixels that forms a boundary between two disjoint regions (Gang et al., 2008). It can also be defined as the process of locating and identifying sharp discontinuities in images (Rashmi et al., 2013). It is mostly used in image analysis to preserve image features and partition images into regions of interest. The discontinuities in these images can be caused by (Ghasemi et al., 2011):

i. Discontinuity in depth and/or surface colour and texture.

ii. Reflection of light, Shadows and Illumination.

Edge detection is an image segmentation technique in which images are partitioned into meaningful regions of interest. Some of the practical applications of edge detection algorithms are in face and finger print recognition, location of objects in satellite images, medical images, and computer aided surgery or diagnosis amongst others (Rashmi et al., 2013). One of the most important challenges of edge detection algorithm is to detect the edges in noisy images. Many traditional edge detection algorithms have been developed to overcome noise such as Sobel, Prewitt, Roberts and Gradient based edge detection algorithms etc. (Rashmi et al., 2013).

These traditional edge detection algorithms are very fast but they cannot perform well on noisy images. Hence, the significant problem of these edge detection algorithms are displacement, removed edges, false and broken edges(Maini & Aggarwal, 2011). Noise phenomenon is an obstacle in detection of continuous edges as it causes some variation of pixel intensities, thus reducing the performance of an edge detection algorithm in noisy images (Setayesh et al., 2013). It also leads to unclear and displaced edges (Chaudhary & Gulati, 2013). Many edge detection algorithms have been developed in the literature over the past years to improve precision of recognized edges. However, they still suffer from producing broken edges and false edges due to noise effect (Maini & Aggarwal, 2011). Therefore, an improved edge detection algorithm is required to detect edges with greater continuity in noisy images in order to reduce the shortcomings of traditional edge detection algorithms. In the field of image processing, there exists basically two types of images which are the gray scale and the coloured images.

Numerous researchers have developed edge detection algorithms for gray scale images in the past. But in recent times, with improvement in computer capabilities and the increased applications of coloured images there is need to develop an effective edge detection algorithm for coloured images (Haque & Aljahdali, 2013). Some of the applications of the edge detection algorithms are in image segmentation, image compression, face recognition, computer vision, computer surveillance, medical diagnosis, image encryption/communication multimedia and remotely sensed images, amongst others(Vijayarani & Vinupriya, 2013). In the areas of medical diagnosis, satellite images, face recognition, and computer surveillance, representation of images by its edges reduced the amount of data required to be stored whilst retaining useful information in the image.

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