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Implementation of edge detection for a digital image

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par Innocent MBARUBUKEYE
KIST - AO Electronics and telecommunication engineering 2008
  

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1.2 PROBLEM IDENTIFICATION.

As already started in the introduction, edge detection is used to detect an object (solid object). Not only the image detection, but also the intensity of gray-level of that object is noted.

There are some software used to process image and the implementation details of any image processing algorithm are inaccessible (lack of using programming language). Our project is focused on software used to detect edges of image employing mainly the MATLAB program for solving this problem.

1.3. SCOPE OF THE STUDY.

The areas of this work are in electronics and telecommunication engineering, which are very wide fields.

This work is intended to implement the edge detection for digital image, so that it may be carried out to a big contour (face) identification of an object (an image).

1.4. TECHNIQUES USED.

In order to accomplish this task, the researchers require the use of the following methods:

· The study guide utilizes an extensive conversation containing edge detection objectives.

· Library search was used to find background of digital image processing in this project.

· Websites consultancies.

General methods and techniques were used to collect for this project as primary as well as secondary data collection. Secondary data was collected from published materials related to the topic. These include: book class notes and online information by visiting some websites on the Internet.

1.5. OBJECTIVES OF THE PROJECT.

The objectives of this project are the following:

1. To face identification of an object.

2. To implement an edge detection for digital image.

1.6. PROJECT OUTLINE.

The outline of this project consists of five chapters.

The first is Introduction. It discusses the motivation for the work is being reported, provides a brief overview of each of the main chapters that the reader will encounter.

The second chapter is Literature review, which provides details about what other others have done and hence set a benchmark for the current project as well as to justify the use of specific solution techniques.

The third chapter is Research methodology. This chapter deals with the methodology and materials used.

The fourth chapter is Results and discussion. It deals with the analysis of results obtained from different file format of images used in this work.

This section presents the results from experiments or survey.

The fifth chapter is conclusion and recommendation. It gives summary of the main findings also the problems limitation is discussed.

.

CHAPTER 2. LITERATURE REVIEW.

2.1 INTRODUCTION TO THE IMAGE.

2.1.0. DIGITAL IMAGE.

An image may be defined as a two-dimensional function, f (x,y), where x and y are special(plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called intensity or grey- level of the image at that point.

When x, y and the amplitude values of f are all finite, discrete quantities, we call this image a digital image.

2.1.1. DIGITAL IMAGE PROCESSING

Image processing: is to perform a particular series of operation on that image, such a set of the field of digital image processing refers to processing digital images by means of a digital computer.

Note that a digital image is composed of a finite number of elements, each of which has a particular location and value.

These elements are referred to as picture elements, images elements, pels and pixels.

The area of an image analysis (also called image understanding) is in between image processing and computer vision.

There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However one useful paradigm is to consider three types of computerized processes in this continuum: low-level, mid-level and high level processes.

Low-level process: involves primitive operation such as image processing to reduce noise, contrast enhancement and image sharpening. A low level process is characterised by the fact that both its inputs and outputs are images.

Mid-level processing on images: involves task such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing and classification (recognition) of individual objects.

A mid-level process is characterized by the fact that its input general is images, but its outputs are attributes extracted from those images. (Example: edges, contours, and the identity of individual objects).

Finally, high level processing involves making sense, of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive function normally associated with vision.

Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image.

Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition encompasses processes that extract attributes from images, up to and including the recognition of individual object.

Digital Image Processing (DIP) is a multidisciplinary science that borrows principles from diverse fields such as optic, surface physique, visuals psychophysics, computer science, and mathematics. The main application of image processing include: astronomy, ultrasonic imaging, remote sensing, Video communication and microscopy, among innumerable others.

In digital image processing system, an image processor reads out image data from a window of an image buffer memory having N-columns and M- rows.

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