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Digital Image Processing
Digital image processing is essential for engineers and students to fully understand both the fundamentals and also the implementation and application principles of the same. This courseware introduces the fundamental concepts of digital image processing systems which are illustrated by various animations and relevant examples for quick understanding of students along with the simplified theory. At the end of each topic relevant quizzes & FAQs are also given for the students to self estimate their understanding. It is helpful for the students to develop their base to deal with higher semester subjects as well.
Unit 1: Introduction of Digital Image Processing
One picture is worth more than ten thousand words.The field of digital image processing refers to processing digital images by means of a digital computer. A digital image is composed of a finite number of elements, each of which has a particular location and value.
Unit 2: Matlab Introduction
Unit 3: Image Enhancement in the Spatial Domain
The principle objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application. Image enhancement approaches fall into two broad categories : spatial domain methods and frequency domain methods. The term spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image.
Unit 4: Image Enhancement in the Frequency Domain
Enhance : to make greater (as in value, desirability or attractiveness) Frequency : The number of times that a periodic function repeats the same sequence of values during a unit variation of the independent variable. Frequency domain processing techniques are based on modifying the fourier transform of an image.
Unit 5: Colour Image Processing
Colour image processing is divided into two major areas : full – colour and pseudocolour processing. In first category, the images in question typically are acquired with a full colour sensor, such as a colour TV camera or colour scanner. In second category, the problem is one of assigning a colour to a particular monochrome intensity or range of intensities.
Unit 6: Image Compression
Image compression addresses the problem of reducing the amount of data required to represent a digital image. Currently it is recognized as an “enabling technology ”. It plays a major role in many important and diverse applications remote sensing, medical imaging and many more.
Unit 7: Image Segmentation
Segmentation subdivides an image into its constituent regions or objects. Image segmentation algorithm generally are based on one of two basic properties of intensity values : discontinuity and similarity .We discuss here a number of approaches in these two categories.
Unit 8: Image Classification
Unit 9: Morphological Image Processing
The word morphology commonly denoted a branch of biology that deals with the form and structure of animals and plants. It offers a unified and powerful approach to numerous image processing problems.
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