Matlab Digital Image Processing From Ground Up™

Matlab Digital Image Processing From Ground Up™
Matlab Digital Image Processing From Ground Up™
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 4.5 Hours | 2.39 GB

Image Processing : Edge-Detection Algorithms , Convolution, Filter Design, Gray-Level Transformation, Histograms etc.

With a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Image Processing in an engaging and easy to follow way. The goal of this course is to present practical techniques while avoiding obstacles of abstract mathematical theories. To achieve this goal, the image processing techniques are explained in plain language, not simply proven to be true through mathematical derivations.

Still keeping it simple, this course comes in different programming languages so that students can put the techniques to practice using a programming language of their choice. This version of the course uses the Matlab programming language.

By the end of the course you should be able to perform 2-D Discrete Convolution with images in matlab, perform Edge-Detection in matlab, perform Spatial Filtering in matlab, compute an Image Histogram and Equalize it in matlab, perform Gray Level Transformations, suppress noise in images, understand all about operators such as Laplacian, Sobel, Prewitt, Robinson, even give a lecture on image processing and more. Please take a look at the full course curriculum.

What you’ll learn

  • Be able to suppress noise in images
  • Be able to develop the 2-D Convolution algorithm in Matlab
  • Apply Edge-Detection Operators like Laplacian, Sobel, Prewitt, Robinson etc. on Images
  • Be able to develop Spatial Filtering Algorithms in Matlab
  • Be able to compute an Image Histogram and Equalize it in Matlab
  • Be able to perform Arithmetic and Boolean Operations like Addition, Subtraction, AND, OR etc. on images
  • Be able to perform Image Enhancement Techniques such as Blurring and Sepia using Python
  • Be able to give a lecture on Digital Image Processing
Table of Contents

1 Introduction
2 On Setup

Basic Image Processing Concepts and Terminologies
3 Overview of Image Processing
4 Understanding Image Color and Resolution
5 Coding Reading and Writing Image
6 Coding More on Displaying Images
7 Understanding Image Formats and Datatypes
8 Coding RGB to Grayscale Conversion
9 Coding RGB to HSV Conversion
10 Coding Extracting RGB Color Channels
11 Overview of Image Processing Techniques
12 Coding Performing Image Binarization
13 Getting familiar with some commonly used terms
14 Overview of Image Processing Applications in Computer Vision

Arithmetic Operations
15 Effects of Addition and Subtraction on Images
16 Coding Darkening an Image with Subtraction
17 Coding Brightening an Image with Addition
18 Coding Performing Image Multiplication and Division
19 Coding Performing Boolean Operations on Images

Histogram and Equalization
20 Introduction to Image Histogram
21 Understanding Histogram Equalization
22 Introduction to Adaptive Thresholding
23 Coding Computing the Histogram of an Image
24 Coding Equalizing the Image Histogram
25 Coding Equalizing the Channels of an RGB Image

Geometric Operations
26 Introduction to Geometric Operations
27 Mapping and Affine Transformation

Image Enhancement Techniques
28 Introduction to Image Enhancement
29 The Filter Kernel

Gray Level Transformation
30 Introduction to Gray Level Transformation
31 Coding Finding the Inverse of an Image

Neighborhood Processing
32 Introduction to Neighborhood Processing
33 Convolution And Correlation
34 Introduction to 2-D Convolution and Correlation
35 Coding Convolving an Image with Gaussian Blur and Motion Blur Kernels
36 Introduction of Low-pass Filters
37 Coding Adding Gaussian and Salt & Pepper Noise to an Image
38 Coding Performing Noise Reduction using a Gaussian Filter
39 Coding Performing Noise Reduction using a Mean Filter
40 Coding Performing Noise Reduction using a Median Filter

41 Understanding the concept of Operators
42 Coding Performing Edge-Detection
43 Coding Performing Laplacian Edge-Detection

Image Formation
44 Understanding how images are formed
45 Understanding the mathematics of image formation

Foundations Of Matlab Programming
46 Introduction to Matrices
47 Matrix concatenation
48 Working with Complex Numbers
49 Array Indexing
50 Saving and loading variables
51 Plotting 2D graphs
52 Plotting multiple graphs
53 Dealing with missing data
54 Writing to a file
55 Reading from a file

Setting Up
56 Downloading Matlab
57 Installing Matlab
58 Overview of Matlab

59 Closing Remarks