Implementation of edge detection for a digital image

2.10. GAUSSIAN SMOOTHING

The Gaussian smoothing operator is a 2-D convolution operator that is used to `blur' images and remove detail and noise. In this sense it is similar to the mean filter, but it uses a different kernel that represents the shape of a Gaussian (`bell-shaped') hump. This kernel has some special properties which are detailed below.

The idea of Gaussian smoothing is to use this 2-D distribution as a `point-spread' function, and this is achieved by convolution. Since the image is stored as a collection of discrete pixels we need to produce a discrete approximation to the Gaussian function before we can perform the convolution. In theory, the Gaussian distribution is non-zero everywhere, which would require an infinitely large convolution mask, but in practice it is effectively zero more than about three standard deviations from the mean, and so we can truncate the mask at this point.[10]

2.11. FACE IDENTIFICATION.

Face identification is very important in security, surveillance and telecommunication applications. The proposed algorithm will be used for face tracking in video surveillance system. In most cases the quality and resolution of the recorded image-segments is poor and hard to identify a face. For this reason, surveillance projects often use additional zooming cameras to focus on region of interests. Disturbing effects might distort recordings such as variations in illuminations, in pose or occlusions. Due to the acquisition conditions the size of the face images is smaller (sometimes much smaller) than the input image size in most of the existing face recognition systems. For example, the valid face region could be as small as 15×15 pixels, whereas the face images used in still image based systems are

at least 128 ×128 . Small-size images not only make the recognition task difficult, but also affect the accuracy of face segmentation.

In our approach we assume that the human head is detected and localized. The task is divided into the following sub-tasks. First some features are extracted to estimate the procession of the picture .Than the digital image is transformed to create edges on the boundary of digital image, in which the face is surrounded and scaled to a standard size. At last searching the most similar face in a database makes the identification of face.

CHAPTER 3. RESEARCH METHODOLOGY.

This chapter deals with method used in conducting this project, the materials and equipments used, including origin and standard specifications.

3.1. METHOD USED AND SOFTWARE DEVELOPMENT.

In order to accomplish this task, general methods and techniques were used to collect data for this project as primary as well as secondary data collection.

Secondary data correction.

Secondary data was corrected from published materials related to the topic.

These include:

Book class notes and online information by visiting some websites on the internet.

Library search was used to find background of digital image processing of our project.

Websites consultancies: the main websites were

http://www.nacs.uci.edu/dcslib/matlab/matlabv60/help/toolbox/images/bwunpack.html and http://www.cogs.susx.ac.uk/courses/compvis/index/html

Primary data collection.

The study guide utilizes an extensive conversation containing edge detection objectives. This conversation was done between researchers and supervisor; also it was between researchers and class mast.

Data analysis.

After getting information about this project; we decided to use software called «MATLAB version 6.5»(MATLAB 6p5) to achieve our main objectives in this research.