New Method Based on Multi-Threshold of Edges Detection in Digital Images

Amira S,Shimaa E,S Abdel-Khalek,Mohamed A

doi:10.14569/ijacsa.2014.050214

Amira S, Shimaa E + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2014.050214

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Abstract

Edges characterize object boundaries in image and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. Edges detection is used to classify, interpret and analyze the digital images in a various fields of applications such as robots, the sensitive applications in military, optical character recognition, infrared gait recognition, automatic target recognition, detection of video changes, real-time video surveillance, medical images, and scientific research images. There are different methods of edges detection in digital image. Each one of these methods is suited to a particular type of images. But most of these methods have some defects in the resulting quality. Decreasing of computation time is needed in most applications related to life time, especially with large size of images, which require more time for processing. Threshold is one of the powerful methods used for edge detection of image. In this paper, We propose a new method based on different Multi-Threshold values using Shannon entropy to solve the problem of the traditional methods. It is minimize the computation time. In addition to the high quality of output of edge image. Another benefit comes from easy implementation of this method.

Highlights

In many applications of image processing, the gray levels of pixels belonging to the object are quite different from the gray levels of the pixels belonging to the background
This paper shows the new algorithm based on the Shannon entropy for edge detection using histogram of the image
An edge detection performance is compared to the previous classic methods, such as, Laplacien of Gaussian (LOG), Prewitt, Roberts and Sobel

Summary

Introduction

In many applications of image processing, the gray levels of pixels belonging to the object are quite different from the gray levels of the pixels belonging to the background. Thresholding becomes a simple but effective tool in edge detection to separate objects from the background. The edge is a prominent feature of an image; it is the front-end processing stage in object recognition and image understanding system. Edge detection can be defined as the boundary between two regions separated by two relatively distinct gray level properties[6]. An effective edge detector reduces a large amount of data but still keeps most of the important feature of the image. Edge detection refers to the process of locating sharp discontinuities in an image. These discontinuities originate from different scene features such as discontinuities in depth, discontinuities in surface orientation, and changes in material properties and variations in scene illumination [8,9]

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