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The Complete Computer Vision Bootcamp: From Zero to Expert!

Name: The Complete Computer Vision Bootcamp: From Zero to Expert!
Rating: 4.1 (19 reviews)

The modern Computer Vision course for everyone! Projects, challenges and theory. Many courses in one!

Created byLucas Bazilio

Last updated 1/2023

English

What you'll learn

Become an advanced, confident, and modern Computer Vision expert from scratch
Become job-ready by understanding how Computer Vision really works behind the scenes
Computer Vision fundamentals: digital image, discretization and quantification, color spaces, etc.
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
Complex concepts like the 'Mahalanobis' classifier, object identification, etc.
Image segmentation: región growing, split & merge, watershed, k-means, normalized cuts, etc.
Practice your skills with 50+ challenges and assignments (solutions included)

Course content

7 sections • 51 lectures • 9h 41m total length

Introduction to the Course1:10
Working in MATLAB1:11
On this lesson we mention that we will use MATLAB to develop our computer vision applications.

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.

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.
Noise Elimination5:58
On this lesson we learn how to remove noise from a multilevel image.
Final Applications13:37
On this lesson we see some applications on the morphology for multilevel images.
Multilevel Reconstruction - Capping of Cell Nuclei6:06
On this lesson we solve a problem about reconstruction in a multilevel image. We cap the nuclei of the cells in the image.
Multilevel Reconstruction - Border Structures7:03
On this lesson we solve a problem about reconstruction in a multilevel image. We remove the rice that touch the borders of the image.
Regional Maxima and Minima14:27
On this lesson we learn how to compute the regional maxima and minima in multilevel images.
We see different filtering techniques and apply them.

Introduction to Image Segmentation9:42
On this lesson we introduce image segmentation.
Canny Edge Detector10:39
On this lesson we study and apply the canny edge detector.
Watershed18:11
On this lesson we study watershed.
Automatic Markers Acquisition9:04
On this lesson we study how to acquire automatic markers for image segmentation.
Separation of Objects11:43
On this lesson we study the separation of objects in image segmentation.
K-Means Clustering9:55
On this lesson we introduce K-Means Clustering.
K-Means Clustering Application13:37
On this lesson we work with K-Means Clustering from a practical point of view.

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

You’ve just stumbled upon the most complete, in-depth Computer Vision course online.

Whether you want to:

- build the skills you need to get your first Computer Vision programming job

- move to a more senior software developer position

- become a computer scientist mastering in computation

- or just learn Computer Vision to be able to work with your own projects quickly.

...this complete Computer Vision Masterclass is the course you need to do all of this, and more.

This course is designed to give you the Computer Vision skills you need to become a Computer Vision expert. By the end of the course, you will understand Computer Vision extremely well and be able to work with your own Computer Vision projects and be productive as a computer scientist and software developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Computer Vision course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous coding experience and takes you from absolute beginner core concepts. You will learn the core Computer Vision skills and master logic programming. It's a one-stop shop to learn Computer Vision. If you want to go beyond the core content you can do so at any time.

Here’s just some of what you’ll learn

(It’s okay if you don’t understand all this yet, you will in the course)

Understand the formation mechanisms of Digital Images and their characteristics.
Compare and select the most appropriate Image Preprocessing Tools depending on the problem to be solved.
Segment and Label the regions of an image based on their common characteristics and/or differences.
Know, design and efficiently apply the most suitable Descriptors for the characterization of Regions, Contours or Singular Points of an image.
Detect and recognize the presence of certain Items in an image.
Correctly carry out experiments aimed at evaluating the chosen or proposed methods, their limitations and weak points, based on objective results.
Detect Motion in a scene and Track Targets.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have 7 days a week.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

There’s no risk either!

This course comes with a 30-day money-back guaranteed by Udemy. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.

You either end up with Computer Vision skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Computer Vision brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, Computer Vision is waiting!)

Who this course is for:

Take this course if you want to gain a true and deep understanding of 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
Take this course if you already know Computer Vision and are looking for an advanced course. This course includes expert topics!
Take this course if you are interested in face recognition, early diagnosis of diseases, detection and location of objects and people, gestural interaction with systems, robot guidance or automatic driving

The Complete Computer Vision Bootcamp: From Zero to Expert!

What you'll learn

Explore related topics

Course content

Introducción2 lectures • 2min

The Digital Image6 lectures • 40min

Image Quantification2 lectures • 32min

Image Preprocessing13 lectures • 3hr 11min

Morphological Image Processing18 lectures • 3hr 17min

Image Binarization3 lectures • 35min

Image Segmentation7 lectures • 1hr 23min

Requirements

Description

Who this course is for: