Computer Vision in Python for Beginners (Theory Projects)

Computer Vision in Python for Beginners (Theory & Projects), available at $69.99, has an average rating of 4.53, with 346 lectures, based on 258 reviews, and has 2336 subscribers.

You will learn about ? The introduction and importance of Computer Vision (CV). ? Why is CV such a popular field nowadays? ? The fundamental concepts from the absolute beginning with comprehensive unfolding with examples in Python. ? Practical explanation and live coding with Python. ? The concept of colored and black and white images with practice. ? Deep details of Computer Vision with examples of every concept from scratch. ? TensorFlow (Deep learning framework by Google). ? The use and applications of state-of-the-art Computer Vision (with implementations in state-of-the-art framework Numpy and TensorFlow). ? Theory and implementation of Panoramic images. ? Geometric transformations. ? Image Filtering with implementation in Python. ? Edge Detection, Shape Detection, and Corner Detection. ? Object Tracking and Object detection. ? 3D images. ? Building your own applications for change detection in the live feed of cameras by using Computer Vision Techniques using Python. ? Developing a complete project to make a very intelligent and efficient DVR using Python. This course is ideal for individuals who are ? Learners who are absolute beginners and know nothing about Computer Vision. or ? People who want to make smart solutions. or ? People who want to learn computer vision with real data. or ? People who love to learn theory and then implement it using Python. or ? People who want to learn computer vision along with its implementation in realistic projects. or ? Data Scientists. or ? Machine learning experts. It is particularly useful for ? Learners who are absolute beginners and know nothing about Computer Vision. or ? People who want to make smart solutions. or ? People who want to learn computer vision with real data. or ? People who love to learn theory and then implement it using Python. or ? People who want to learn computer vision along with its implementation in realistic projects. or ? Data Scientists. or ? Machine learning experts.

Enroll now: Computer Vision in Python for Beginners (Theory & Projects)

Summary

Title: Computer Vision in Python for Beginners (Theory & Projects)

Price: $69.99

Average Rating: 4.53

Number of Lectures: 346

Number of Published Lectures: 345

Number of Curriculum Items: 346

Number of Published Curriculum Objects: 345

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

? The introduction and importance of Computer Vision (CV).

? Why is CV such a popular field nowadays?

? The fundamental concepts from the absolute beginning with comprehensive unfolding with examples in Python.

? Practical explanation and live coding with Python.

? The concept of colored and black and white images with practice.

? Deep details of Computer Vision with examples of every concept from scratch.

? TensorFlow (Deep learning framework by Google).

? The use and applications of state-of-the-art Computer Vision (with implementations in state-of-the-art framework Numpy and TensorFlow).

? Theory and implementation of Panoramic images.

? Geometric transformations.

? Image Filtering with implementation in Python.

? Edge Detection, Shape Detection, and Corner Detection.

? Object Tracking and Object detection.

? 3D images.

? Building your own applications for change detection in the live feed of cameras by using Computer Vision Techniques using Python.

? Developing a complete project to make a very intelligent and efficient DVR using Python.

Who Should Attend

? 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.

Target Audiences

? 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.

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 Pythoncoursepresents 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. Na?ve 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. Na?ve 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

Course Curriculum

Chapter 1: Introduction to Course and Instructor

Lecture 1: Why Computer Vision

Lecture 2: Introduction to Instructor

Lecture 3: About AI Sciences

Lecture 4: Course Outline (Optional)

Lecture 5: Methodology

Lecture 6: Computer Vision Applications

Lecture 7: Final Project

Lecture 8: Request for Your Honest Review

Lecture 9: Github & OneDrive Link to get the Course Materials

Chapter 2: Introduction to Images

Lecture 1: Github & OneDrive Link to get the Course Materials

Lecture 2: Grayscale Image

Lecture 3: Quiz(Grayscale Image)

Lecture 4: Solution(Grayscale Image)

Lecture 5: Python Warning

Lecture 6: Grayscale Spectrum

Lecture 7: Answer to Question

Lecture 8: Reading, Manipulating and Saving Grayscale Image using Matplotlib Python

Lecture 9: Quiz(Reading, Manipulating and Saving Grayscale Image using Matplotlib Python)

Lecture 10: Solution(Reading, Manipulating and Saving Grayscale Image using Matplotlib Python)

Lecture 11: Reading, Manipulating and Saving Grayscale Image using OpenCV Python

Lecture 12: Introduction to RGB Images

Lecture 13: Quiz(Introduction to RGB Images)

Lecture 14: Solution(Introduction to RGB Images)

Lecture 15: RGB Color Images Matplotlib and OpenCV

Lecture 16: Quiz(RGB Color Images Matplotlib and OpenCV)

Lecture 17: Solution(RGB Color Images Matplotlib and OpenCV)

Lecture 18: RGB to HSV theory and Algorithm

Lecture 19: RGB to HSV Algorithm Implementation using Python

Lecture 20: Quiz(RGB to HSV Algorithm Implementation using Python)

Lecture 21: Solution(RGB to HSV Algorithm Implementation using Python)

Lecture 22: Red Rose Extraction or Segmentation using HSV Python

Lecture 23: Quiz(Red Rose Extraction or Segmentation using HSV Python)

Lecture 24: Solution(Red Rose Extraction or Segmentation using HSV Python)

Lecture 25: Hyper Spectral Images

Chapter 3: 2D Scaling Transformations

Lecture 1: Github & OneDrive Link to get the Course Materials

Lecture 2: Introduction to Geometric Transformations

Lecture 3: Scaling Example in OpenCV

Lecture 4: Quiz(Scaling Example in OpenCV)

Lecture 5: Solution(Scaling Example in OpenCV)

Lecture 6: Scaling in Real Space

Lecture 7: Quiz(Scaling in Real Space)

Lecture 8: Solution(Scaling in Real Space)

Lecture 9: Linear Transformation Explained

Lecture 10: Scaling is a Linear Transformations

Lecture 11: Scaling as a Matrix Multiplication Example Python

Lecture 12: Quiz(Scaling as a Matrix Multiplication Example Python)

Lecture 13: Solution(Scaling as a Matrix Multiplication Example Python)

Lecture 14: Image Coordinate System

Lecture 15: Image Copy and Flipping Vertically

Lecture 16: Quiz 01(Image Copy and Flipping Vertically)

Lecture 17: Solution 01(Image Copy and Flipping Vertically)

Lecture 18: Quiz 02(Image Copy and Flipping Vertically)

Lecture 19: Solution 02(Image Copy and Flipping Vertically)

Lecture 20: Continuous Coordinates

Lecture 21: Saturations and Holes

Lecture 22: Image Doubling and Holes using Python

Lecture 23: Inverse Scaling and Quiz

Lecture 24: Solution and Nearest Neighbour Interpolation

Lecture 25: Inverse Scaling Python

Lecture 26: Quiz 01(Inverse Scaling Python)

Lecture 27: Solution 01(Inverse Scaling Python)

Lecture 28: Quiz 02 (Inverse Scaling Python)

Lecture 29: Solution 02(Inverse Scaling Python)

Lecture 30: Nearest Neighbour Interpolation

Lecture 31: Weighted Average vs Simple Average

Lecture 32: Bilinear Interpolation

Lecture 33: Bilinear Interpolation Implementation in Python

Lecture 34: Scaling Transformation with Bilinear Interpolation Implementation

Lecture 35: Scaling Transformation Algorithm(Recap)

Lecture 36: Exam

Lecture 37: Exam Solution 01

Lecture 38: Exam Solution 02

Chapter 4: 2D Geometric Transformations

Lecture 1: Github & OneDrive Link to get the Course Materials

Lecture 2: Rotation Introduction

Lecture 3: Optional Rotation is Linear Transform Proof

Lecture 4: Rotation can Result Negative Coordinates(Problem)

Lecture 5: Rotation Computing Width and Hight of Resultant Image(Solution)

Lecture 6: Rotation Index Shifting

Lecture 7: Quiz(Rotation Index Shifting)

Lecture 8: Solution(Rotation Index Shifting)

Lecture 9: Rotation Implementation Complete

Lecture 10: Quiz(Rotation Implementation Complete)

Lecture 11: Solution(Rotation Implementation Complete)

Lecture 12: Rotation Implementation(Good Coding Practice)

Lecture 13: Quiz(Rotation Implementation(Good Coding Practice))

Lecture 14: Solution(Rotation Implementation(Good Coding Practice))

Lecture 15: Reflection Introduction

Lecture 16: Quiz(Reflection Introduction)

Lecture 17: Solution(Reflection Introduction)

Lecture 18: Reflection Implementation

Lecture 19: Quiz 01(Reflection Implementation)

Lecture 20: Solution 01(Reflection Implementation)

Lecture 21: Quiz 02(Reflection Implementation)

Lecture 22: Solution 02(Reflection Implementation)

Lecture 23: Shear Introduction

Lecture 24: Shear Implementation and Quiz

Instructors

AI Sciences
AI Experts & Data Scientists |4+ Rated | 168+ Countries

AI Sciences Team
Support Team AI Sciences

Rating Distribution

1 stars: 7 votes

2 stars: 5 votes

3 stars: 24 votes

4 stars: 82 votes

5 stars: 140 votes

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