Image Processing and AI

Course Overview

Image processing is one of the most exciting fields in machine learning. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this course, students will understand image processing and Artificial Intelligence and its various applications.

As part of this course, students will be able to utilize Matlab and Python for image processing, image classification and object detection. This is a hands-on course and involves several labs and exercises to train and test custom image classifier and detection models. At the end of the course, students will be able to classify and recognize objects and create their own model. This course does not require any prior Machine Learning or Computer Vision experience. However, some knowledge of the programming language and high school math is necessary.

Modules Offered

Modules offered in Image Processing and AI are designed to cater real world situations. Following are the modules included in this course:

1. Introduction and Review of Basic Concepts
After completion of this module student will be competent to understand computer for an engineer. It gives the students an introduction to computer, its usage and management.
Student will be able to program in a machine language by making logics and decisions.

2. Digital Image Fundamentals
This module introduces several concepts related to digital images including image formation, light and other components of the electromagnetic spectrum and their imaging characteristics. By end of this module student will be able to use Matlab image processing toolbox for various operations on images.

3. Spatial Domain Analysis
This module introduces a number of concepts, such as filtering with spatial masks. The topics included in this module serve as a foundation for understanding the state of the art in enhancement techniques.

4. Frequency Domain Analysis
In this module student will learn about application of Fourier transform for image enhancement. This mainly focuses on connection between image characteristics and mathematical models.

5. Image filtering and Data Extraction
In this module student will learn some of restoration techniques, such as random noise reduction using convolution masks. Concepts introduced in this module is development of fundamental approaches that have reasonably predictable behaviour.

6. Color Image Processing
This module focuses on color models that are not only useful in color image processing but are necessary tools for further study in area of color image processing.

Basic concepts of Artificial IntelligenceThis module teaches the most important and foundational principles of Machine Learning to implement those principles by applying models to real-world problems. This modules takes students to next level by beginning to solve problems of computer vision with a few lines of code.

7. Artificial Intelligence applications for Image Processing
By the end of this module, the student will be able to use technologies learned throughout the specialization to design and create own applications for processing, data retrieval and visualization.

8. Internship
After completion of this module, student will get hands-on knowledge and learn work ethics. Students will gain practical knowledge and confidence that help them in future in achieving work goals.

Final Project
After completion of this module, the student will be competent enough to design and develop a model by keeping in view real world problem.