Mô tả

Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. In the CV process, real-world images and videos are captured, processed, and analyzed to allow machines to extract contextual, useful information from the physical world.

Until recently, computer vision functioned in a limited capacity. But due to the recent innovations in artificial intelligence and deep learning, this field has made great leaps. Today, CV surpasses humans in most routine tasks connected with detecting and labeling objects.

The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you to understand the digital imaging process and identify the key application areas of CV. The course is:

  • · Easy to understand.

  • · Descriptive.

  • · Comprehensive.

  • · Practical with live coding.

  • · Rich with state of the art and updated knowledge of this field.

Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The Homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.

The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-time) and Smart DVRs (Real-time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.

The course tutorials are divided into 320+ videos along with detailed code notebooks. The videos are available in HD, and the total runtime of the videos is 27 hours+.

Now is the perfect time to learn computer vision. Get started with this best-in-class course without any further delay!

Teaching is our passion:

In this course, we apply the proven learning by doing methodology. We build the interest of learners first. We start from the basics and focus on helping you understand each concept clearly. The explanation of each theoretical concept is followed by practical implementation. We then encourage you to create something new out of your learning.

Our aim is to help you master the basic concepts of CV before moving onward to advanced concepts. The course material includes online videos, course notes, hands-on exercises, project work, quizzes, and handouts. We also offer you learning support. You can approach our team in case of any queries, and we respond in quick time.


Course Content:

The comprehensive course consists of the following topics:

1. Introduction

a. Intro

i. What is computer vision?

2. Image Transformations

a. Introduction to images

i. Image data structure

ii. Color images

iii. Grayscale images

iv. Color spaces

v. Color space transformations in OpenCV

vi. Image segmentation using Color space transformations

b. 2D geometric transformations

i. Scaling

ii. Rotation

iii. Shear

iv. Reflection

v. Translation

vi. Affine transformation

vii. Projective geometry

viii. Affine transformation as a matrix

ix. Application of SVD (Optional)

x. Projective transformation (Homography)

c. Geometric transformation estimation

i. Estimating affine transformation

ii. Estimating Homography

iii. Direct linear transform (DLT)

iv. Building panoramas with manual key-point selection

3. Image Filtering and Morphology

a. Image Filtering

i. Low pass filter

ii. High pass filter

iii. Band pass filter

iv. Image smoothing

v. Image sharpening

vi. Image gradients

vii. Gaussian filter

viii. Derivative of Gaussians

b. Morphology

i. Image Binarization

ii. Image Dilation

iii. Image Erosion

iv. Image Thinning and skeletonization

v. Image Opening and closing

4. Shape Detection

a. Edge Detection

i. Definition of edge

ii. Naïve edge detector

iii. Canny edge detector

1. Efficient gradient computations

2. Non-maxima suppression using gradient directions

3. Multilevel thresholding- hysteresis thresholding

b. Geometric Shape detection

i. RANSAC

ii. Line detection through RANSAC

iii. Multiple lines detection through RANSAC

iv. Circle detection through RANSAC

v. Parametric shape detection through RANSAC

vi. Hough transformation (HT)

vii. Line detection through HT

viii. Multiple lines detection through HT

ix. Circle detection through HT

x. Parametric shape detection through HT

xi. Estimating affine transformation through RANSAC

xii. Non-parametric shapes and generalized Hough transformation

5. Key Point Detection and Matching

a. Corner detection (Key point detection)

i. Defining Corner

ii. Naïve corner detector

iii. Harris corner detector

1. Continuous directions

2. Tayler approximation

3. Structure tensor

4. Variance approximation

5. Multi-scale detection

b. Project: Building automatic panoramas

i. Automatic key point detection

ii. Scale assignment

iii. Rotation assignment

iv. Feature extraction (SIFT)

v. Feature matching

vi. Image stitching

6. Motion

a. Optical Flow, Global Flow

i. Brightness constancy assumption

ii. Linear approximation

iii. Lucas–Kanade method

iv. Global flow

v. Motion segmentation

b. Object Tracking

i. Histogram based tracking

ii. KLT tracker

iii. Multiple object tracking

iv. Trackers comparisons

7. Object detection

a. Classical approaches

i. Sliding window

ii. Scale space

iii. Rotation space

iv. Limitations

b. Deep learning approaches

i. YOLO a case study

8. 3D computer vision

a. 3D reconstruction

i. Two camera setups

ii. Key point matching

iii. Triangulation and structure computation

b. Applications

i. Mocap

ii. 3D Animations

9. Projects

a. Change detection in CCTV cameras (Real-time)

b. Smart DVRs (Real-time)



After completing this course successfully, you will be able to:

  • · Relate the concepts and theories in computer vision with real-world problems.

  • · Implement any project from scratch that requires computer vision knowledge.

  • · Know the theoretical and practical aspects of computer vision concepts.

Who this course is for:

  • · Learners who are absolute beginners and know nothing about Computer Vision.

  • · People who want to make smart solutions.

  • · People who want to learn computer vision with real data.

  • · People who love to learn theory and then implement it using Python.

  • · People who want to learn computer vision along with its implementation in realistic projects.

  • · Data Scientists.

  • · Machine learning experts.



Unlock the fascinating world of Computer Vision and take your first step towards becoming an expert in this field.

Enroll now and embark on a learning journey that combines theory and hands-on projects. Start mastering Computer Vision today!


List of Keywords:

  1. Image Processing

  2. Deep Learning for Computer Vision

  3. Artificial Intelligence in Computer Vision

  4. Machine Learning Models for Image Analysis

  5. Object Detection and Recognition

  6. Image Filtering and Enhancement

  7. Shape Detection Algorithms

  8. Key Point Detection and Matching Techniques

  9. Optical Flow and Motion Analysis

  10. 3D Computer Vision and Reconstruction

  11. Real-time Computer Vision Applications

  12. Change Detection in CCTV

  13. Smart DVR Systems

  14. Computer Vision Projects

  15. Image Segmentation

  16. Feature Extraction in CV

  17. Harris Corner Detector

  18. Scale-Invariant Feature Transform (SIFT)

  19. RANSAC Algorithm

  20. YOLO (You Only Look Once)

  21. 3D Reconstruction from Images

  22. Structure from Motion (SfM)

  23. Mocap (Motion Capture)

  24. Computer Vision for 3D Animation

  25. Computer Vision for Data Scientists

  26. Computer Vision for Machine Learning Practitioners

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18 sections

Introduction to Course and Instructor

9 lectures
Why Computer Vision
02:42
Introduction to Instructor
02:10
About AI Sciences
01:21
Course Outline (Optional)
12:22
Methodology
02:10
Computer Vision Applications
08:26
Final Project
03:32
Request for Your Honest Review
01:18
Github & OneDrive Link to get the Course Materials
00:15

Introduction to Images

25 lectures
Github & OneDrive Link to get the Course Materials
00:15
Grayscale Image
03:34
Quiz(Grayscale Image)
00:32
Solution(Grayscale Image)
01:11
Python Warning
01:15
Grayscale Spectrum
08:19
Answer to Question
04:45
Reading, Manipulating and Saving Grayscale Image using Matplotlib Python
14:08
Quiz(Reading, Manipulating and Saving Grayscale Image using Matplotlib Python)
00:36
Solution(Reading, Manipulating and Saving Grayscale Image using Matplotlib Python)
03:17
Reading, Manipulating and Saving Grayscale Image using OpenCV Python
09:03
Introduction to RGB Images
05:40
Quiz(Introduction to RGB Images)
00:38
Solution(Introduction to RGB Images)
01:59
RGB Color Images Matplotlib and OpenCV
14:40
Quiz(RGB Color Images Matplotlib and OpenCV)
01:01
Solution(RGB Color Images Matplotlib and OpenCV)
04:15
RGB to HSV theory and Algorithm
06:02
RGB to HSV Algorithm Implementation using Python
09:57
Quiz(RGB to HSV Algorithm Implementation using Python)
00:38
Solution(RGB to HSV Algorithm Implementation using Python)
01:01
Red Rose Extraction or Segmentation using HSV Python
11:14
Quiz(Red Rose Extraction or Segmentation using HSV Python)
02:18
Solution(Red Rose Extraction or Segmentation using HSV Python)
01:54
Hyper Spectral Images
05:59

2D Scaling Transformations

38 lectures
Github & OneDrive Link to get the Course Materials
00:15
Introduction to Geometric Transformations
08:28
Scaling Example in OpenCV
08:57
Quiz(Scaling Example in OpenCV)
00:35
Solution(Scaling Example in OpenCV)
01:27
Scaling in Real Space
07:18
Quiz(Scaling in Real Space)
06:04
Solution(Scaling in Real Space)
04:59
Linear Transformation Explained
05:24
Scaling is a Linear Transformations
03:19
Scaling as a Matrix Multiplication Example Python
04:15
Quiz(Scaling as a Matrix Multiplication Example Python)
01:03
Solution(Scaling as a Matrix Multiplication Example Python)
01:55
Image Coordinate System
04:41
Image Copy and Flipping Vertically
07:31
Quiz 01(Image Copy and Flipping Vertically)
00:52
Solution 01(Image Copy and Flipping Vertically)
01:44
Quiz 02(Image Copy and Flipping Vertically)
00:28
Solution 02(Image Copy and Flipping Vertically)
00:47
Continuous Coordinates
03:24
Saturations and Holes
05:18
Image Doubling and Holes using Python
09:23
Inverse Scaling and Quiz
05:12
Solution and Nearest Neighbour Interpolation
05:01
Inverse Scaling Python
07:48
Quiz 01(Inverse Scaling Python)
00:21
Solution 01(Inverse Scaling Python)
01:38
Quiz 02 (Inverse Scaling Python)
01:03
Solution 02(Inverse Scaling Python)
02:35
Nearest Neighbour Interpolation
04:45
Weighted Average vs Simple Average
03:56
Bilinear Interpolation
09:54
Bilinear Interpolation Implementation in Python
10:11
Scaling Transformation with Bilinear Interpolation Implementation
08:07
Scaling Transformation Algorithm(Recap)
03:54
Exam
04:21
Exam Solution 01
04:27
Exam Solution 02
03:05

2D Geometric Transformations

41 lectures
Github & OneDrive Link to get the Course Materials
00:15
Rotation Introduction
05:21
Optional Rotation is Linear Transform Proof
04:21
Rotation can Result Negative Coordinates(Problem)
03:07
Rotation Computing Width and Hight of Resultant Image(Solution)
08:06
Rotation Index Shifting
08:11
Quiz(Rotation Index Shifting)
00:56
Solution(Rotation Index Shifting)
01:25
Rotation Implementation Complete
14:42
Quiz(Rotation Implementation Complete)
00:21
Solution(Rotation Implementation Complete)
01:01
Rotation Implementation(Good Coding Practice)
06:33
Quiz(Rotation Implementation(Good Coding Practice))
00:34
Solution(Rotation Implementation(Good Coding Practice))
02:30
Reflection Introduction
06:22
Quiz(Reflection Introduction)
00:55
Solution(Reflection Introduction)
00:53
Reflection Implementation
04:10
Quiz 01(Reflection Implementation)
00:23
Solution 01(Reflection Implementation)
00:43
Quiz 02(Reflection Implementation)
00:33
Solution 02(Reflection Implementation)
00:45
Shear Introduction
04:15
Shear Implementation and Quiz
02:27
Translation and its Nonlinearity(Problem)
05:19
Homoginuous Coordinates
04:19
Translation as a Matrix(solution)
04:13
Homoginuous Representations Off all Transformations
06:29
Affine Transformation Implementation
07:09
Quiz(Affine Transformation Implementation)
00:55
Rotation about any Point Theory
04:09
Rotation about any Point Implementation
07:12
Reflection about a Line Quiz
02:33
Solution(Reflection about a Line)
03:39
Transformation Matrix Properties
04:29
Transformation Matrix Properties Implementation
06:43
Affine Transformation Hierarchy
05:50
Optional Affine Transformation SVD
05:54
Projective Transformation Homography
03:54
Projective Transformation Implementation
07:41
Projective Warping Algorithm
02:10

Geometric Transformation Estimation(Panorama)

29 lectures
Github & OneDrive Link to get the Course Materials
00:15
Goal
01:23
Affine Transformation Estimation Introduction
02:32
Quiz(Affine Transformation Estimation Introduction)
00:38
Solution(Affine Transformation Estimation Introduction)
02:30
Affine Transformation Estimation Points Correspondences
04:22
Estimation Points Marking using Python and Quiz
07:23
Affine Transformation Min Number of Points Needed
04:25
Affine Transformation Estimation using Python
05:06
Affine Transformation Estimation Verification using Python
04:06
Affine Transformation Estimation with more than 3 Points
04:23
Quiz(Affine Transformation Estimation with more than 3 Points)
00:42
Solution(Affine Transformation Estimation with more than 3 Points)
01:30
Affine Transformation Estimation with more than 3 Points Implementation
07:41
Quiz(Affine Transformation Estimation with more than 3 Points Implementation)
01:25
Solution(Affine Transformation Estimation with more than 3 Points Implementation)
01:35
Optional Affine Transformation Estimation with LeastSquared
05:39
Projective Transformation Estimation Introduction
03:36
Projective Transformation Estimation First Implementation having Bug
05:21
Projective Transformation Estimation Reason of the Bug
08:54
Projective Transformation Estimation Removing Scale Factor
06:47
Projective Transformation Estimation DLT
08:48
Projective Transformation Estimation DLT Nullspace and Why 4 Points
08:16
Projective Transformation Estimation DLT Nullspace Implementation
05:50
DLT Implementation
16:52
Quiz(DLT Implementation)
00:38
Panorama Stitching
09:13
Panorama Stitching Implementation in OpenCV
05:07
How Projective Transformation Helps in Panorama
02:16

Binary Morphology

24 lectures
Github & OneDrive Link to get the Course Materials
00:15
Binary Images Theory
03:49
Binary Images Python
05:55
Structuring Element Kernel and Sliding Window Theory
06:03
Structuring Element Python
04:07
Erosion Theory
06:55
Quiz 01(Erosion Theory)
00:34
Solution 01(Erosion Theory)
00:46
Quiz 02(Erosion Theory)
01:04
Solution 02(Erosion Theory)
00:24
Erosion Python
06:42
Dilation Theory
02:34
Quiz 01(Dilation Theory)
00:21
Solution 01(Dilation Theory)
00:48
Quiz 02(Dilation Theory)
01:05
Solution 02(Dilation Theory)
00:16
Dilation Python
03:59
Opening Theory
02:32
Opening Python
09:57
Closing Theory
01:19
Closing Python
03:44
Gradient Morphology
01:07
Gradient Morphology Python
02:08
Tophat Blackhat
05:58

Image Filtering

12 lectures
Github & OneDrive Link to get the Course Materials
00:15
Image Blurring 01
10:52
Image Blurring 02
08:13
General Image Filtering
04:58
Convolution
07:37
Naive Edge Detection
08:50
Image Sharpening
02:26
Quiz(Image Sharpening)
00:32
Solution(Image Sharpening)
01:31
Implementation Of Image Blurring Edge Detection Image Sharpening in Python
16:19
Lowpass Highpass Bandpass Filters
04:48
CNN Course(You can Skip)
00:55

Canny Edge Detector

37 lectures
Github & OneDrive Link to get the Course Materials
00:15
Canny Edge Detector Algorithm Introduction
05:04
Canny Edge Detector OpenCV
03:27
Quiz(Canny Edge Detector OpenCV)
00:43
Solution(Canny Edge Detector OpenCV)
01:11
Gaussian Filter Introduction
04:19
Gaussian Filter to Mask Computation
09:16
Gaussian Filter Window Size
04:42
Gaussian Filter Implementation
11:05
Quiz(Gaussian Filter Implementation)
00:52
Solution(Gaussian Filter Implementation)
02:31
Gaussian Filter Smoothing Implementation
03:58
Quiz(Gaussian Filter Smoothing Implementation)
00:43
Solution(Gaussian Filter Smoothing Implementation)
02:11
Image Gradients Theory
03:14
Image Gradients Implementation
06:45
Image Gradients Implementation Datatype Bug
04:19
Derivative of Gaussian
07:27
Derivative of Gaussian Expression
03:21
Derivative of Gaussian Implementation
03:39
Applying DOG Filters
03:00
Gradient Vector
03:33
Gradient Magnitude and Gradient Direction
05:38
Non Maxima Suppression
03:53
Gradient Direction Quantization
05:22
Quiz(Gradient Direction Quantization)
00:20
Solution(Gradient Direction Quantization)
02:08
Gradient Direction Quantization Implementation
07:38
Gradient Direction Quantization Implementation Better Way
04:03
NMS Implementation
10:50
Quiz 01(NMS Implementation)
00:59
Solution 01(NMS Implementation)
01:18
Quiz 02(NMS Implementation)
00:30
Solution 02(NMS Implementation)
02:05
Last Step Thresholding
00:46
Hesterysis Thresholding
04:22
Hesterysis Thresholding Implementation
02:53

Shape Detection

27 lectures
Github & OneDrive Link to get the Course Materials
00:15
Shape Detection Introduction
02:44
Why Edge Detection is not Enough
03:13
RANSAC Introduction
03:53
RANSAC For Lines Coordinate Arrays
02:16
RANSAC For Lines Sampling Points Randomly Implemenation
06:39
Quiz(RANSAC For Lines Sampling Points Randomly Implemenation)
00:24
Solution(RANSAC For Lines Sampling Points Randomly Implemenation)
00:31
RANSAC For Lines Fitting Line With 2 Points
05:51
RANSAC For Lines Fitting Line With 2 Points Implementation
08:46
Quiz(RANSAC For Lines Fitting Line With 2 Points Implementation)
00:42
Solution(RANSAC For Lines Fitting Line With 2 Points Implementation)
02:03
RANSAC For Lines Computing Consistency Score
05:43
RANSAC For Lines Computing Consistency Score Implementation
03:38
RANSAC For Lines Implementation
07:13
RANSAC For Lines Implementation Test on Real Image
06:30
Drawback
01:20
RANSAC For Lines Implementation Test on Real Image Drawing and Quiz
12:43
RANSAC For Circles
06:00
RANSAC For Circles Consistency Score
03:39
RANSAC For Circles Implementation
02:37
RANSAC For Circles Implementation Real Image
03:22
Drawback
00:37
RANSAC For Circles Implementation Real Image Drawing
03:01
RANSAC General
01:58
RANSAC Quiz
02:40
RANSAC Quiz Solution
07:17

Shape Detection Hough Transform

16 lectures
Github & OneDrive Link to get the Course Materials
00:15
Hough Transform Introduction
02:08
Hough Transform as Voting
03:45
Hough Transform as Voting Loop
06:02
Hough Transform Polar Representation
04:54
Hough Transform Polar Representation Benifits
04:11
Hough Transform Polar Representation Implementation
05:28
Hough Transform Lines Implementation Real Image
04:51
Hough Transform Lines Parameters Conversion
03:46
Hough Transform Lines Drawing
02:25
Solution(Hough Transform Lines Drawing)
03:16
Hough Transform Fast Version
02:34
Hough Transform Circles
03:22
Hough Transform Circles Implementation
03:20
Hough Transform Circles Implementation Drawing
05:17
Solution(Hough Transform Circles Implementation Drawing)
02:10

Corner Detection

21 lectures
Github & OneDrive Link to get the Course Materials
00:15
Corner Definition
04:04
Why Corner
05:53
Corner Measure
04:18
SSD
05:37
Why SSD to be Muted Somewhere
05:56
Corner Detection Implementation 01
06:48
Corner Detection Implementation 02
12:17
Corner Detection Implementation 03
08:30
Moravec Corner Detector
02:32
Scale Space
08:27
Infinite Directions Towards Harris Corner Detector
06:45
Harris Corner Detector 01
05:44
Harris Corner Detector 02
07:34
Harris Corner Detector 03
04:54
Harris Corner Detector 04 Structure Tensor
04:56
Harris Corner Detector 05 Final Expression
04:15
Harris Corner Detector Implementation Speedup Convolution
03:31
Harris Corner Detector Implementation 01
04:34
Harris Corner Detector Implementation 02
06:44
Harris Corner Detector as Edge Detector
04:10

Automatic Panorama SIFT

6 lectures
Github & OneDrive Link to get the Course Materials
00:15
Point Correspondence Introduction
06:55
Point Drawing Implementation
09:57
Scale and Orientation Alignment
05:57
SIFT and HOG
08:33
Points Matching
09:57

Object Detection

10 lectures
Github & OneDrive Link to get the Course Materials
00:15
Introduction to Object Detection
04:44
Classification PipleLine
07:41
Sliding Window Implementation
06:08
Shift Scale Rotation Invariance
09:47
Person Detection
11:07
HOG Features
08:54
HandEngineering vs CNNs
09:25
Implementation
16:24
Activity
04:42

YOLO Object Detector

13 lectures
Github & OneDrive Link to get the Course Materials
00:15
CNNS Introduction
04:42
Face Detection Implementation
08:18
YOLO Implementation
08:10
YOLO Image Classfication Revisited
04:56
YOLO Sliding Window Object Localization
06:27
YOLO Sliding Window Efficient Implementation
08:13
YOLO Introduction
07:47
YOLO Training Data Generation
06:27
YOLO Anchor Boxes
07:49
YOLO Algorithm
06:16
YOLO Non Maxima Supression
05:56
YOLO RCNN
04:01

Motion

4 lectures
Github & OneDrive Link to get the Course Materials
00:15
Optical Flow
03:57
BC Assumption
02:57
Optical Flow Derivation
07:34

Object Tracking

7 lectures
Github & OneDrive Link to get the Course Materials
00:15
Tracking by Detection
03:56
Tracking by Detection Motion Model Assumption
03:34
Tracking KLT TLD
06:13
Single Object Tracking
21:56
Multiple Object Tracking
14:09
WebCam and Saving Annotations of Multiple Object Tracking
12:58

3D Reconstruction

8 lectures
Github & OneDrive Link to get the Course Materials
00:15
3d Reconstruction Introduction
03:46
3d Motion Capture
04:40
Camera
04:08
Camera Matrix
05:31
Triangulation
05:01
Camera Matrix Estimation
04:08
Mocap Revisited
02:31

Smart CCTV Project

18 lectures
Github & OneDrive Link to get the Course Materials
00:15
Introduction to the Project
04:39
Introduction to Data
08:49
Reading a Video File
04:38
Change Detection Frame Differencing
02:47
Change Detection Frame Differencing Implementation
07:15
Change Detection Background Subtraction
04:16
Change Detection Background Subtraction MOG
05:05
Denoising using Morphology
05:13
Connected Components
02:48
Connected Components Filtering
10:59
Tracking Change
05:37
Saving Segments
06:14
Saving and Viewing Segments
12:53
Saving and Viewing Segments with Object Detection
09:40
Applications
05:14
THANK YOU Bonus Video
01:29
About AI Sciences
02:10

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