Introduction to Computer Vision

Name: Introduction to Computer Vision
Rating: 4.7 (4 reviews)

A Quick and Easy Intro into Computer Vision

Created byLucas Bazilio

Last updated 1/2023

English

What you'll learn

Become an advanced, confident, and modern Computer Vision expert from scratch
How to think and work like a developer: problem-solving, researching, workflows
Digital image processing: gray level transformations, geometric transformations, convolution, image enhancement and smoothing, morphological filters, etc.
Get fast and friendly support in the Q&A area

Course content

11 sections • 51 lectures • 9h 4m total length

Course Introduction2:19
On this lesson we introduce the Course.
Learning MATLAB0:56
On this lesson we mention the last sections of the fundamentals of MATLAB.

Basic Concepts2:53
In this lesson we introduce some Basic Concepts about images.
Discretization of the Image3:52
In this lesson we see how digital images are formed.
Working with Images17:22
In this lesson we begin to see how we can work with images in MATLAB.
Domain Evaluation7:34
In this lesson we study the domain in images.
How to Save an Image2:33
In this lesson we see how to save a digital image in MATLAB.
Mesh Function5:46
On this lesson we study the mesh function in MATLAB.

Introduction to Image Preprocessing1:36
On this lesson we introduce the image preprocessing section.
Image Information Retrieval9:51
In this lesson we practise the linear image processing.
Gray Level Histogram15:10
On this lesson we study the gray level histogram.
Histogram Equalization8:26
In this lesson we learn the histogram equalization and work with an example.
Affine Transformation4:48
In this lesson we introduce the affine transformations and see a brief example.
Arithmetic Operations10:23
On this lesson we begin to see the arithmetic operations on images.
Moreover, we practise the operation of image subtraction.
Quality Control Project - Statement6:53
In this lesson we solve a computer vision problem. This is a problem where we have to apply the image of images to detect anomalies.
Quality Control Project - Solution - Part 140:24
On this lesson we start the resolution of the Quality Control Project.
We solve the stages 1 and 2.
Quality Control Project - Solution - Part 213:48
On this lesson we finish the solution of the Quality Control Project.
We solve the stages 3 and 4 and visualize the final difference image obtained.
Introduction to Convolution5:31
On this lesson we introduce the convolution operation.
Working with Convolution22:05
On this lesson we start working with convolution in the MATLAB environment.
We study how to apply the convolution operation with a manual algorithm.
Moreover, we also study the imfilter predefined function that allows us to apply the convolution operation.
Image Noise and Smoothing Filters17:13
On this lesson we study the image noise and smoothing filters.
We learn how to add noise to an image as well as how to remove noise.
Contour Detection35:21
On this lesson we study the contour detection in images.
Contour Detection Exercise - Laplacian Filter

Introduction to Morphological Image Processing6:46
On this lesson we introduce the section of Morphological Image Processing.
Dilations21:38
On this lesson we study dilations and learn different ways to dilate images.
Erosions9:24
On this lesson we study the erosion operation and we see how we can calculate contours in morphological image processing.
Morphological Gradients13:30
On this lesson we study the Morphological Gradients.
We see that they can be computed with operations between the original image, dilated image and eroded image.
Distance Transform21:25
On this lesson we study the distance transform.
Conditional Dilation and Reconstruction14:54
On this lesson we study conditional dilation and reconstruction.
Challenge 1 - Wrench Reconstruction8:34
On this lesson we solve a problem about reconstruction. The problem is about reconstructing an image and only obtain the wrench tool from a kit of tools.
Challenge 2 - Greek Symbols6:34
On this lesson we solve a problem about reconstruction. In the problem we have an input symbols image, and we need to only maintain the ψ symbols.
Challenge 3 - Closed Holes9:33
On this lesson we solve a problem about reconstruction. In the problem we have an input image, and we need to only obtain three white closed holes.
Opening and Closing10:14
On this lesson we introduce opening and closing.
Gear Detection5:06
On this lesson we study an application of opening. We learn how to detect the tips of a gear.
Skeletons12:33
On this lesson we introduce skeletons and see the powerful application of the skeleton by influence zones.
Morphology for Multilevel Images10:06
On this lesson we apply the morphological operations learned in a multilevel image.

Introduction to Variables3:01
In this lesson we see how variables are defined in MATLAB.
Storage and Data Loading5:07
In this lesson we learn the foundamentals of Storage and Data Loading in MATLAB.
Hence, we see how we can save our workspace into a .mat file and also how to load these files into our workspace.
Using Constants and built-in Functions6:31
In this lesson we use constants and built-in functions in MATLAB.

Introduction to Arrays7:28
In this lesson we start working with Arrays in MATLAB.
Arrays and Spacing9:29
In this lesson we study the spacing in arrays.
Array Creation Functions6:33
In this lesson we study and work with MATLAB creation functions in arrays.
Indexing4:55
In this lecture we start working the indexing of arrays in MATLAB.
Extracting Multiple Elements3:56
In this lesson we work on extracting multiple elements in arrays.

Requirements

No coding experience is necessary to take this course! I take you from beginner to expert!
Any computer and OS will work — Windows, macOS or Linux.

Description

Do you want to start your journey in Computer Vision?

Or is it that Computer Vision interests you?

If you need a quick brush-up, or learning Computer Vision for the first time, you've come to the right place!

Let's get started learning one of the most interesting computer science topics out there right now. There's no need to fret if you haven't coded before. This courses starts from scratch.

Computer Vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering and computer science, it seeks to understand and automate tasks that the human visual system can do. This course will be a quick way to introduce to the Computer Vision world. You'll be a whiz in no time.

We'll begin with the basics of Computer Vision, learning about the digital image, discretization, domains and getting to know the image quantification. We'll soon move on to the basics of image information retrieval in Computer Vision.

Now, after completing this Introduction Course you can finish your training with The Complete Computer Vision Course available in my udemy profile with more than 12 hours duration.

I hope you're excited to dive into the World of Computer Vision with this course. Well, what are you waiting for? Let's get started!

Who this course is for:

Take this course if you want to start your journey in Computer Vision
Take this course if you have been trying to learn Computer Vision but: 1) still don't really understand Computer Vision, or 2) still don't feel confident to work real projects

Introduction to Computer Vision

What you'll learn

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Course content

Course Overview2 lectures • 3min

The Digital Image6 lectures • 40min

Image Quantification2 lectures • 32min

Image Preprocessing13 lectures • 3hr 11min

Morphological Image Processing13 lectures • 2hr 30min

MATLAB Fundamentals - Commands2 lectures • 12min

MATLAB Fundamentals - Variables3 lectures • 15min

MATLAB Fundamentals - Desktop and Editor2 lectures • 9min

MATLAB Fundamentals - Arrays5 lectures • 32min

MATLAB Fundamentals - Plots2 lectures • 19min

Requirements

Description

Who this course is for: