Matlab For Image Processing

Learn Image Processing with Matlab

What you'll learn

Fundamental Image Processing Techniques

Build Confidence in Implementing Image Processing Algorithms

Develop Practical Skills

Analytical Thinking Through Image Analysis

Requirements

Everyone can learn as long as they have laptop, internet and willingness to learn

Description

This beginner-level course is designed to introduce participants to the fundamental concepts and practical applications of image processing using MATLAB. No prior experience in image processing is required, making it the perfect starting point for anyone new to this exciting field. Throughout the course, participants will explore the essential techniques used to manipulate, analyze, and enhance digital images.Starting with the basics, learners will be guided through core operations such as loading, displaying, and saving images. They will practice manipulating images through simple operations like resizing, rotating, flipping, and adjusting brightness or contrast. By the end of the foundational lessons, participants will have a solid understanding of how to apply basic filters and transformations to images.As the course progresses, participants will engage in hands-on study cases that bring real-world relevance to their learning. For example, they will learn how to enhance the contrast of poorly lit images, count objects in binary images, and perform basic segmentation techniques. These practical exercises will not only solidify their understanding of core concepts but also give them the confidence to solve real-world problems using MATLAB.The course emphasizes hands-on learning, with numerous coding exercises that reinforce each concept. By the end of the course, participants will have the skills needed to approach more advanced image processing tasks, setting the stage for future exploration in this growing field.

Overview

Section 1: Basic Image Processing using Matlab

Lecture 1 Introduction

Lecture 2 Matlab Online

Lecture 3 Basic Commands / Functions - #1

Lecture 4 Basic commands and functions - #2

Lecture 5 Image processing methods - Image Conversion

Lecture 6 Image processing methods - Morphological Operations

Lecture 7 Image processing methods - Image Segmentation

Lecture 8 Image processing methods - Histogram Equalization

Lecture 9 Image processing methods - Add and Remove Noise from an image

Lecture 10 Image processing methods - Image collage

Lecture 11 Image processing methods - Basic Image Arithmetic

Lecture 12 Image processing methods - Negative Image (max intensity)

Lecture 13 Image processing methods - Flip image horizontally and vertically

Lecture 14 Image processing methods - Channel Separation (Red, Green, Blue Channels)

Lecture 15 Image processing methods - Image Filtering using conv2

Section 2: Learning by use cases

Lecture 16 Introduction - Learning by use cases

Lecture 17 Use Case 1 - Automatic Contrast Adjustment

Lecture 18 Use Case 2 - Object Counting in a Binary Image

Lecture 19 Use Case 3 - Edge Detection in an Image

Lecture 20 Use Case 4 - Image Sharpening

Lecture 21 Use Case 5 - Image Smoothing (Blurring) Using a Gaussian Filter

Lecture 22 Create own function 1 - Simple threshold to create a binary image

Lecture 23 Create own function 2 - Gaussian Blur

Lecture 24 Create own function 3 - Edge Detection

Everyone can learn