Python Digital Image Processing From Ground Up™

Python Digital Image Processing From Ground Up&, available at $54.99, has an average rating of 4.25, with 63 lectures, based on 565 reviews, and has 3304 subscribers.

You will learn about 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 Pythons 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 This course is ideal for individuals who are If you are an absolute beginner to image processing , then take this course. or If you are a seasoned programmer and want to get a quick guide to performing image processing in python, then take this course. or If you are a university student taking the theory of image processing in school, then take this course to learn how the theory is applied practically. It is particularly useful for If you are an absolute beginner to image processing , then take this course. or If you are a seasoned programmer and want to get a quick guide to performing image processing in python, then take this course. or If you are a university student taking the theory of image processing in school, then take this course to learn how the theory is applied practically.

Enroll now: Python Digital Image Processing From Ground Up&

Summary

Title: Python Digital Image Processing From Ground Up&

Price: $54.99

Average Rating: 4.25

Number of Lectures: 63

Number of Published Lectures: 62

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 62

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will 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 Pythons 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

Who Should Attend

If you are an absolute beginner to image processing , then take this course.

If you are a seasoned programmer and want to get a quick guide to performing image processing in python, then take this course.

If you are a university student taking the theory of image processing in school, then take this course to learn how the theory is applied practically.

Target Audiences

If you are an absolute beginner to image processing , then take this course.

If you are a seasoned programmer and want to get a quick guide to performing image processing in python, then take this course.

If you are a university student taking the theory of image processing in school, then take this course to learn how the theory is applied practically.

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 imagesin python, perform Edge-Detectionin python, perform Spatial Filteringin python, compute an Image Histogramand Equalizeit 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.

REMEMBER : I have no doubt you will love this course. Also it comes with a? FULL money back guarantee for 30 days!? So put simply, you really have nothing to loose and everything to gain.

Sign up and lets start manipulating some pixels.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Setting Up

Lecture 1: Downloading Python

Lecture 2: Installing Python

Lecture 3: Using IDLE

Lecture 4: Installing Python packages

Chapter 3: Python Essentials

Lecture 1: Printing statements

Lecture 2: Variables

Lecture 3: Lists

Lecture 4: Operators

Lecture 5: Conditions

Lecture 6: For Loops

Lecture 7: While Loops

Lecture 8: Functions

Lecture 9: Dictionaries

Lecture 10: Classes and Objects

Chapter 4: Basic Image Processing Concepts and Terminologies

Lecture 1: Overview of Image Processing

Lecture 2: Understanding Image Color and Resolution

Lecture 3: Understanding Image Formats and Datatypes

Lecture 4: Coding : Introduction to Python Imaging Library

Lecture 5: Coding : Converting Image Format

Lecture 6: Coding : Basic Image Manipulations

Lecture 7: Coding : Getting Image Information

Lecture 8: Coding : Plotting Descriptive Images

Lecture 9: Coding : Adding Interactive Annotations

Lecture 10: Overview of Image Processing Techniques

Lecture 11: Coding : Performing Image Binarization

Lecture 12: Getting familiar with some commonly used terms

Lecture 13: Overview of Image Processing Applications in Computer Vision

Chapter 5: Histogram and Equalization

Lecture 1: Introduction to Image Histogram

Lecture 2: Understanding Histogram Equalization

Lecture 3: Coding : Computing the Histogram of an Image

Lecture 4: Notice

Lecture 5: Coding : Equalizing An Image Histogram

Lecture 6: Introduction to Adaptive Thresholding

Chapter 6: Geometric Operations

Lecture 1: Introduction to Geometric Operations

Lecture 2: Mapping and Affine Transformation

Chapter 7: Image Enhancement Techniques

Lecture 1: Introduction to Image Enhancement

Lecture 2: The Filter Kernel

Lecture 3: Coding : Performing Gamma Correction

Chapter 8: Gray Level Transformation

Lecture 1: Introduction to Gray Level Transformation

Lecture 2: Coding : Performing Gray-Level Transformations

Lecture 3: Effects of Addition and Subtraction on Images

Chapter 9: Neighborhood Processing

Lecture 1: Introduction to Neighborhood Processing

Lecture 2: Convolution And Correlation

Lecture 3: Introduction to 2-D Convolution and Correlation

Lecture 4: Introduction of Low-pass Filters

Lecture 5: Coding : Filtering Images with the Python Imaging Library

Lecture 6: Coding : Applying the Mean Filter

Lecture 7: Coding : Applying the Minimum Filter

Lecture 8: Coding : Applying the Maximum Filter

Lecture 9: Coding : Applying the Median Filter

Chapter 10: Edge Detection

Lecture 1: Understanding the Concept of Operators

Lecture 2: Coding : Detecting Edges with the Prewitt Mask

Lecture 3: Coding : Performing Sobel Edge-Detection with SKImage

Lecture 4: Coding : Performing Sobel Edge-Detection with OpenCV

Lecture 5: Coding : Performing Laplacian Edge-Detection using OpenCV

Chapter 11: Image Formation

Lecture 1: Understanding how images are formed

Lecture 2: Understanding the mathematics of image formation

Lecture 3: Coding : Creating an Image

Chapter 12: Alternate Setup : Setting Up the Raspberry Pi

Lecture 1: Remotely Accessing the Raspberry Pi by SSH

Lecture 2: Remotely Accessing the Raspberry Pi by Remote Desktop Connection

Chapter 13: Closing

Lecture 1: Closing Remarks

Instructors

Israel Gbati
Embedded Firmware Engineer

PyTribe .
Practical Python Mastery for Everyone

Rating Distribution

1 stars: 16 votes

2 stars: 33 votes

3 stars: 112 votes

4 stars: 190 votes

5 stars: 214 votes

Frequently Asked Questions

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