Python Digital Image Processing From Ground Up™

Python Digital Image Processing From Ground Up™
Python Digital Image Processing From Ground Up™

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 2.51 GB
eLearning | Skill level: All Levels


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