English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 2.51 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 Python programming language. By the end of the course you should be able to perform 2-D Discrete Convolution with images in python, perform Edge-Detection in python , perform Spatial Filtering in python, compute an Image Histogram and Equalize it in python, 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 Python
- Apply Edge-Detection Operators like Laplacian, Sobel, Prewitt, Robinson etc. on Images
- Be able to develop Spatial Filtering Algorithms in Python
- Be able to compute an Image Histogram and Equalize it in Python
- Understand all about operators such as Laplacian, Sobel, Prewitt, Robinson etc.
- Be able to perform Image Processing using Python’s Imaging Library
- Be able to perform Image Processing using SKImage
- 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**

**Introduction**

1 Introduction

**Setting Up**

2 Downloading Python

3 Installing Python

4 Using IDLE

5 Installing Python packages

**Python Essentials**

6 Printing statements

7 Variables

8 Lists

9 Operators

10 Conditions

11 For Loops

12 While Loops

13 Functions

14 Dictionaries

15 Classes and Objects

**Basic Image Processing Concepts and Terminologies**

16 Overview of Image Processing

17 Understanding Image Color and Resolution

18 Understanding Image Formats and Datatypes

19 Coding Introduction to Python Imaging Library

20 Coding Converting Image Format

21 Coding Basic Image Manipulations

22 Coding Getting Image Information

23 Coding Plotting Descriptive Images

24 Coding Adding Interactive Annotations

25 Overview of Image Processing Techniques

26 Coding Performing Image Binarization

27 Getting familiar with some commonly used terms

28 Overview of Image Processing Applications in Computer Vision

**Histogram and Equalization**

29 Introduction to Image Histogram

30 Understanding Histogram Equalization

31 Coding Computing the Histogram of an Image

32 Coding Equalizing An Image Histogram

33 Introduction to Adaptive Thresholding

**Geometric Operations**

34 Introduction to Geometric Operations

35 Mapping and Affine Transformation

**Image Enhancement Techniques**

36 Introduction to Image Enhancement

37 The Filter Kernel

38 Coding Performing Gamma Correction

**Gray Level Transformation**

39 Introduction to Gray Level Transformation

40 Coding Performing Gray-Level Transformations

41 Effects of Addition and Subtraction on Images

**Neighborhood Processing**

42 Introduction to Neighborhood Processing

43 Convolution And Correlation

44 Introduction to 2-D Convolution and Correlation

45 Introduction of Low-pass Filters

46 Coding Filtering Images with the Python Imaging Library

47 Coding Applying the Mean Filter

48 Coding Applying the Minimum Filter

49 Coding Applying the Maximum Filter

50 Coding Applying the Median Filter

**Edge Detection**

51 Understanding the Concept of Operators

52 Coding Detecting Edges with the Prewitt Mask

53 Coding Performing Sobel Edge-Detection with SKImage

54 Coding Performing Sobel Edge-Detection with OpenCV

55 Coding Performing Laplacian Edge-Detection using OpenCV

**Image Formation**

56 Understanding how images are formed

57 Understanding the mathematics of image formation

58 Coding Creating an Image

**Alternate Setup Setting Up the Raspberry Pi**

59 Remotely Accessing the Raspberry Pi by SSH

60 Remotely Accessing the Raspberry Pi by Remote Desktop Connection

**Closing**

61 Closing Remarks

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