Computer Vision Theory and Projects in Python for Beginners

1. Introduction to the Course

This video introduces you to Computer Vision and its importance.

2. Introduction to Instructor

Let's get introduced to the instructors.

3. About AI Sciences

This video provides an overview of AI Sciences.

4. Course Outline (Optional)

This video provides an overview of the entire course.

5. Computer Vision Applications

Learn the different applications of computer vision in this video.

6. Final Project

This video provides an overview of the final project.

1. Grayscale Image

This video explains digital grayscale image data structure in details.

2. Quiz (Grayscale Image)

It's time for a short quiz on Grayscale image.

3. Solution (Grayscale Image)

Let's discuss the solution to the quiz on Grayscale image.

4. Grayscale Spectrum

In this lesson, you will learn about Grayscale spectrum.

5. Reading, Manipulating, and Saving Grayscale Image using Matplotlib Python

In this session, you will learn how to read, manipulate, and save grayscale image using Matplotlib Python package.

6. Quiz (Reading, Manipulating, and Saving Grayscale Image using Matplotlib Python)

It's time for a short quiz on reading, manipulating, and saving grayscale image using Matplotlib Python package.

7. Solution (Reading, Manipulating, and Saving Grayscale Image using Matplotlib Python)

Let's discuss the solution to the quiz on reading, manipulating, and saving grayscale image using Matplotlib Python package.

8. Reading, Manipulating, and Saving Grayscale Image using OpenCV Python

In this session, you will learn how to read, manipulate, and save grayscale image using open CV Python package.

9. Introduction to RGB Images

In this session, you will learn about the image data structure of colored RGB images.

10. Quiz (Introduction to RGB Images)

It's time for a short quiz on RGB images.

11. Solution (Introduction to RGB Images)

Let's discuss the solution to the quiz on RGB images.

12. RGB Color Images Matplotlib and OpenCV

In this session, we will explore RGB color images and manipulate them using Matplotlib and OpenCV.

13. Quiz (RGB Color Images Matplotlib and OpenCV)

It's time for a short quiz on RGB color image manipulation using Matplotlib and OpenCV.

14. Solution (RGB Color Images Matplotlib and OpenCV)

Let's discuss the solution for the quiz from our previous session.

15. RGB to HSV theory and Algorithm

Learn the theory an algorithm for converting RGB to HSV.

16. RGB to HSV Algorithm Implementation using Python

In this session, we will convert RGB to HSV by algorithm implementation using Python.

17. Quiz (RGB to HSV Algorithm Implementation using Python)

It's time for a short quiz on RGB to HSV algorithm implementation using Python.

18. Solution (RGB to HSV Algorithm Implementation using Python)

Let's discuss solution for the quiz on RGB to HSV algorithm implementation using Python.

19. Red Rose Extraction or Segmentation using HSV Python

Learn the process of extracting or segmenting specific image elements using by turn in this session.

20. Quiz (Red Rose Extraction or Segmentation using HSV Python)

It's time for a short quiz on Red Rose extraction or segmentation using Python.

21. Solution (Red Rose Extraction or Segmentation using HSV Python)

Let's discuss the solution to the quiz on Red Rose extraction or segmentation using Python.

22. Hyper Spectral Images

In this lesson, you will learn about Hyperspectral images.

1. Introduction to Geometric Transformations

This video introduces the concept of geometric spatial transformations.

2. Scaling Example in OpenCV

Let's take a look at an example of scaling or image resizing using open CV.

3. Quiz (Scaling Example in OpenCV)

It's time for a quiz on scaling using open CV.

4. Solution (Scaling Example in OpenCV)

Let's discuss the solution to the quiz on scaling using OpenCV.

5. Scaling in Real Space

Learn the underlying theory behind scaling in real space in this video.

6. Quiz (Scaling in Real Space)

It's time for a short quiz on scaling in real space.

7. Solution (Scaling in Real Space)

Let's discuss the solution to the quiz on scaling in real space.

8. Linear Transformation Explained

In this session, we will discuss about linear transformations in depth. Scaling is an example of linear transformation.

9. Scaling is a Linear Transformation

In the previous session, the concept of scaling being a linear transformation was introduced. Let's dive deeper into it.

10. Scaling as a Matrix Multiplication Example Python

In this video, let's learn about scaling as a matrix multiplication with the help of an example in Python.

11. Quiz (Scaling as a Matrix Multiplication Example Python)

It's time for a short quiz on scaling as a matrix multiplication example using Python.

12. Solution (Scaling as a Matrix Multiplication Example Python)

Let's discuss the solution to the quiz on scaling as a matrix multiplication example in this session.

13. Image Coordinate System

This video provides a detailed explanation about the image coordinate system. To understand the concept of image coordinate system better, we will look at an example.

14. Image Copy and Flipping Vertically

We will copy the image and flip it vertically in this session.

15. Quiz 01 (Image Copy and Flipping Vertically)

It's time for a short quiz on copying image and flipping it vertically.

16. Solution 01 (Image Copy and Flipping Vertically)

Let's discuss the solution for the person in each copy and flipping vertically.

17. Quiz 02 (Image Copy and Flipping Vertically)

Here is another quiz on coping image and flipping it.

18. Solution 02 (Image Copy and Flipping Vertically)

Let's discuss the solution for the quiz on image copying and flipping.

19. Continuous Coordinates

A major complication that arises when we transform the images is continuous coordinates. let's explore this complication in this session.

20. Saturations and Holes

In this session, we will discuss about complications such as saturations and holes that arise when image transformation is performed. For small scaling factors, there may be saturation and for large scaling factors, there may be holes.

21. Image Doubling and Holes using Python

Let's explore image doubling and holes using Python in this session.

22. Inverse Scaling and Quiz

In this session, you will learn about inverse scaling followed by a short quiz.

23. Solution and Nearest Neighbor Interpolation

Let's check the solution for the quiz from the previous session in this lesson followed by a discussion on nearest neighbor interpolation.

24. Inverse Scaling Python

In this session, we will implement nearest neighbor interpolation an inverse scaling.

25. Quiz 01 (Inverse Scaling Python)

It's time for short quiz on inverse scaling using Python.

26. Solution 01 (Inverse Scaling Python)

Let's discuss the solution to the problem on inverse scaling using Python.

27. Quiz 02 (Inverse Scaling Python)

Let's look at another short quiz on inverse scaling in Python.

28. Solution 02 (Inverse Scaling Python)

Let's discuss the solution to the second quiz on inverse scaling in Python.

29. Nearest Neighbor Interpolation

In this session, we will continue our discussion on nearest neighbor interpolation.

30. Weighted Average Versus Simple Average

In this session, you will learn about weighted average and simple average. Let's understand how simple average is different from weighted average.

31. Bilinear Interpolation

In this session, we will understand the process of bilinear interpolation.

32. Bilinear Interpolation Implementation in Python

Now that you are familiar with the concept of bilinear interpolation, let's go ahead and implement it using Python in this session.

33. Scaling Transformation with Bilinear Interpolation Implementation

In this session, we will implement scaling transformation with bilinear interpolation easy medication.

34. Scaling Transformation Algorithm(Recap)

Let's quickly recap the scaling transformation algorithm in this session.

35. Exam

It's time for a short assessment.

36. Exam Solution 01

Let's take a look at the solution for the first part of the assessment problem.

37. Exam Solution 02

Let's discuss the solution for the second part of the assessment problem in this session.

1. Rotation Introduction

This video introduces the concept of rotation.

2. Optional Rotation is Linear Transform Proof

This video provides mathematical proof showing rotation is a linear transform.

3. Rotation can Result Negative Coordinates (Problem)

In this session, you will learn how to apply rotation resulting in negative coordinates.

4. Rotation Computing Width and Hight of Resultant Image(Solution)

In this lesson, you will see how to implement rotation computing with the width and height of the resultant image.

5. Rotation Index Shifting

In this session, you will understand what rotation index shifting is with the help of an example.

6. Quiz (Rotation Index Shifting)

It's time for a short quiz on rotation index shifting.

7. Solution (Rotation Index Shifting)

Let's discuss the solution for the quiz on rotation index shifting.

8. Rotation Implementation Complete

In this video, we will implement rotation on the image using Python.

9. Quiz (Rotation Implementation Complete)

It's time for a short quiz on Rotation implementation.

10. Solution (Rotation Implementation Complete)

Let's discuss the solution to the quiz and rotation.

11. Rotation Implementation (Good Coding Practice)

In this session, we will write the generic quote for implementing scaling and transformation.

12. Quiz: Rotation Implementation (Good Coding Practice)

It's time for a short quiz on rotation implementation coding.

13. Solution: Rotation Implementation (Good Coding Practice)

Let's discuss the solution to the quiz on rotation implementation coding.

14. Reflection Introduction

This video introduces the concept of reflection.

15. Quiz (Reflection Introduction)

It's time for a short quiz an introduction reflection.

16. Solution (Reflection Introduction)

Let's discuss the solution for the quiz on introduction to reflection.

17. Reflection Implementation

Now that you are familiar with the reflection it's time to implement.

18. Quiz 01 (Reflection Implementation)

It's time for a short quiz on reflection implementation.

19. Solution 01 (Reflection Implementation)

Let's discuss the solution for the quiz on reflection implementation.

20. Quiz 02 (Reflection Implementation)

Here is a second quiz on reflection implementation.

21. Solution 02 (Reflection Implementation)

Let's discuss the solution to the quiz on reflection implementation.

22. Shear Introduction

The next transformation we will be discussing is the Shear transformation. Shear transformation is also an example of linear transformation period.

23. Shear Implementation and Quiz

Let's implement shear transformation in this session followed by a short quiz.

24. Translation and its Nonlinearity (Problem)

In this session, you will learn about translation and its nonlinearity.

25. Homogeneous Coordinates

In this session, you will learn about homogeneous coordinates.

26. Translation as a Matrix (Solution)

In this session, we will learn how to achieve translation as a matrix.

27. Homogeneous Representations of All Transformations

Learn how to homogeneously represent all transformation in this video.

28. Affine Transformation Implementation

In this session, we will learn how to perform affine transformation implementations.

29. Quiz (Affine Transformation Implementation)

It's time for a short quiz on affine transformation implementations.

30. Rotation about Any Point Theory

In this session, you will learn about rotation at any point purity.

31. Rotation about Any Point Implementation

Let's implement rotation about any point theory in this session.

32. Reflection about a Line Quiz

It's time for a short quiz on reflection about a line.

33. Solution (Reflection about a Line)

Let's discuss the solution to the quiz on reflection about a line.

34. Transformation Matrix Properties

This video explains the transformation matrix properties in detail.

35. Transformation Matrix Properties Implementation

In this session, we will implement transformation matrix properties.

36. Affine Transformation Hierarchy

Learn the affine transformation hierarchy in this session.

37. Optional Affine Transformation SVD

This video explains affine transformation factorization SVD in details.

38. Projective Transformation Homography

In this lesson, you will learn about the most generic form of linear transformation which is projective transformation (homography).

39. Projective Transformation Implementation

We will go ahead and implement projective transformation homography in this session.

40. Projective Warping Algorithm

In this video, you will learn about projective warping algorithm.

1. Goal

This video gives you an introduction to geometric transformations in panorama images.

2. Affine Transformation Estimation Introduction

In this video, you will learn how to estimate affine transformation in geometric transformations.

3. Quiz (Affine Transformation Estimation Introduction)

It's time for a short quiz on introduction to affine transformation estimation.

4. Solution (Affine Transformation Estimation Introduction)

Let's discuss the solution to the quiz on introduction to affine transformation estimation.

5. Affine Transformation Estimation Points Correspondences

In this lesson, you will learn about the affine translation estimation points correspondences.

6. Estimation Points Marking using Python and Quiz

In this session, you will learn how to perform estimation points marking using Python followed by a short quiz.

7. Affine Transformation Min Number of Points Needed

Let's discuss what is the minimum number of points needed for affine transformation period

8. Affine Transformation Estimation using Python

Let's perform affine transformation estimation using Python in this session.

9. Affine Transformation Estimation Verification using Python

In this session, you will learn how to perform affine transformation estimation verification using Python.

10. Affine Transformation Estimation with More Than Three Points

Learn how to implement affine transformation estimation with more than three points.

11. Quiz (Affine Transformation Estimation with More Than Three Points)

It's time for a short quiz on affine transformation estimation with more than three points.

12. Solution (Affine Transformation Estimation with More Than Three Points)

Let's discuss the solution to the quiz on affine transformation estimation with more than three points.

13. Affine Transformation Estimation with More Than Three Points Implementation

We will implement affine transformation estimation with more than three points in this session.

14. Quiz (Affine Transformation Estimation with More Than Three Points Implementation)

It's time for a short quiz on affine transformation estimation with more than three points (implementation).

15. Solution (Affine Transformation Estimation with More Than Three Points Implementation)

Let's discuss the solution to the quiz on implementing affine transformation estimation with more than three points.

16. Optional Affine Transformation Estimation with LeastSquared

In this video, session you will learn about affine transformation estimation with LeastSquared.

17. Projective Transformation Estimation Introduction

This video provides introduction to projective transformation estimation.

18. Projective Transformation Estimation First Implementation having Bug

In this session, we will perform the first projective transformation estimation (with bugs).

19. Projective Transformation Estimation Reason of the Bug

In this session, we will try to understand the reason behind the bugs we have encountered from the previous session.

20. Projective Transformation Estimation Removing Scale Factor

In this session, we will remove the scaling factor from the projective transformation estimation code.

21. Projective Transformation Estimation DLT

In this video, you will learn about projective transformation estimation BLT.

22. Projective Transformation Estimation DLT Nullspace and Why Four Points

In this video, you will learn about Projective Transformation estimation DLT Nullspace and why four points.

23. Projective Transformation Estimation DLT Nullspace Implementation

In this session, you will learn how to perform projective transformation estimation DLT Nullspace implementation.

24. DLT Implementation

This video covers DLT implementation.

25. Quiz (DLT Implementation)

It's time for a short quiz on DLT implementation.

26. Panorama Stitching

In this session, you will learn about panorama stitching.

27. Panorama Stitching Implementation in OpenCV

In this video, you will learn about panorama stitching implementation in OpenCV.

28. How Projective Transformation Helps in Panorama

Understand how projective transformation helps in panorama images in this session.

1. Binary Images Theory

This video introduces you to the binary images theory.

2. Binary Images Python

In this video, we will dive deeper into binary images using Python.

3. Structuring Element Kernel and Sliding Window Theory

In this session, you will learn how to structure element kernel and the sliding window theory.

4. Structuring Element Python

In this video, we will take a look at some kernels or structuring elements in Python.

5. Erosion Theory

In this session, you will learn about the erosion theory, which is a basic operation in binary morphology.

6. Quiz 01 (Erosion Theory)

It's time for a short quiz on erosion theory.

7. Solution 01 (Erosion Theory)

Let's discuss the solution to the quiz on Erosion theory.

8. Quiz 02 (Erosion Theory)

Here is another quiz on Erosion theory.

9. Solution 02 (Erosion Theory)

Let's discuss the solution to the second quiz on Erosion theory.

10. Erosion Python

Let's take a look at an example of erosion in Python.

11. Dilation Theory

In this session, we will discuss the other basic operation in binary morphology, which is the dilation theory.

12. Quiz 01 (Dilation Theory)

It's time for a short quiz on dilation theory.

13. Solution 01 (Dilation Theory)

Let's discuss the solution to the quiz on dilation theory.

14. Quiz 02 (Dilation Theory)

Here is another quiz on dilation theory.

15. Solution 02 (Dilation Theory)

Let's discuss the solution to the second quiz on dilation theory.

16. Dilation Python

In this session, we will implement dilation using OpenCV.

17. Opening Theory

In this session, you will learn about the binary image opening theory. Opening is basically erosion followed by dilation.

18. Opening Python

In this lesson, we will go ahead and apply opening image operation.

19. Closing Theory

In this session, you will learn about closing theory. Closing is dilation followed by ocean.

20. Closing Python

In this mission, we will implement closing in Python.

21. Gradient Morphology

In this lesson, you will learn about gradient morphology.

22. Gradient Morphology Python

In this lesson, we will implement gradient morphology in Python.

23. Top Hat and Black Hat

In this session, you will learn about two operations which are Top hat and Black Hat.

1. Image Blurring 01

This video provides an introduction to image processing techniques. The first image processing technique we will be discussing is image blurring, also known as image smoothing.

2. Image Blurring 02

Let's explore image blurring in more details in this session.

3. General Image Filtering

This really explains the process of general image filtering.

4. Convolution

In this session, you will learn about convolution.

5. Naive Edge Detection

In this lesson, you will learn about an application of convolution, which is Naive Edge Detection.

6. Image Sharpening

In this session, you will learn about image sharpening.

7. Quiz (Image Sharpening)

It's time for a short quiz on image sharpening.

8. Solution (Image Sharpening)

Let's discuss the solution to the quiz on image sharpening.

9. Implementation of Image Blurring, Edge Detection, and Image Sharpening in Python

In this lesson, we will implement image blurring, edge detection, and image sharpening in Python.

10. Low Pass, High Pass, and Band Pass Filters

In this session, you will learn about low pass, high pass, and band pass filters.

1. Canny Edge Detector Algorithm Introduction

This video provides an introduction to the concept of canny edge detection.

2. Canny Edge Detector OpenCV

In this session, we will see the performance of the canny edge detector using OpenCV.

3. Quiz (Canny Edge Detector OpenCV)

It's time for a short quiz on canny edge detector using OpenCV.

4. Solution (Canny Edge Detector OpenCV)

Let's discuss the solution to the quiz on canny edge detector using OpenCV.

5. Gaussian Filter Introduction

This video introduces you to Gaussian filters.

6. Gaussian Filter to Mask Computation

In this lesson, you will learn how to apply Gaussian filters to mask computation.

7. Gaussian Filter Window Size

In this video, we will discuss the answer to two questions. First question is how to choose a sigma and the second one is what should be the appropriate window size for a given sigma.

8. Gaussian Filter Implementation

In this session, we will code a generic function to compute the Gaussian mask.

9. Quiz (Gaussian Filter Implementation)

It's time for a short quiz on Gaussian filter implementation

10. Solution (Gaussian Filter Implementation)

Let's discuss the solution to the quiz on Gaussian filter implementation.

11. Gaussian Filter Smoothing Implementation

Now that we have built the Gaussian Kernel, it's time for a smooth version of the image.

12. Quiz (Gaussian Filter Smoothing Implementation)

It's time for a short quiz on Gaussian filter smoothing implementation.

13. Solution (Gaussian Filter Smoothing Implementation)

Let's discuss the solution to the quiz on Gaussian filter smoothing implementation.

14. Image Gradients Theory

In this session, you will learn all about image gradients.

15. Image Gradients Implementation

In this session, you will learn how to compute image gradients.

16. Image Gradients Implementation Datatype Bug

In this session, we will try to understand the bugs we can encounter while implementing image gradient.

17. Derivative of Gaussian

In this session, we will compute the derivative of Gaussian filter.

18. Derivative of Gaussian Expression

In this session, we will discuss the mathematical expression for the derivative of Gaussian filter.

19. Derivative of Gaussian Implementation

In this session, we will compute the derivative of Gaussian in both x and y with the help of functions.

20. Applying DOG Filters

In this session, you will learn how to apply DOG filters to the image.

21. Gradient Vector

In this session, you will learn about the gradient vector.

22. Gradient Magnitude and Gradient Direction

In this session, we will implement gradient magnitude using x-derivative and y-derivative.

23. Non-Maxima Suppression

In this session, you will learn about non-maxima suppression.

24. Gradient Direction Quantization

In this session, you will learn what is gradient direction quantization and how to perform it.

25. Quiz (Gradient Direction Quantization)

It's time for a short quiz on gradient direction quantization.

26. Solution (Gradient Direction Quantization)

Let's discuss the solution to the quiz on gradient direction quantization.

27. Gradient Direction Quantization Implementation

In this session, we will go ahead and implement gradient direction quantization.

28. Gradient Direction Quantization Implementation Better Way

In this session, we will go ahead and implement gradient direction quantization in a much better way.

29. NMS Implementation

In this session, we will discuss in-depth NMS implementation.

30. Quiz 01 (NMS Implementation)

It's time for a short quiz on NMS implementation.

31. Solution 01 (NMS Implementation)

Let's discuss the solution to the quiz on NMS Implementation.

32. Quiz 02 (NMS Implementation)

Here is another short quiz on NMS Implementation.

33. Solution 02 (NMS Implementation)

Let's discuss the solution to the second quiz on NMS Implementation.

34. Last Step Thresholding

In this session, we will discuss last step thresholding.

35. Hysteresis Thresholding

In this session, you will learn about hysteresis thresholding.

36. Hysteresis Thresholding Implementation

In this session, you will learn how to implement hysteresis thresholding.

1. Shape Detection Introduction

This session provides an introduction to the concept of shape detection.

2. Why Edge Detection is not Enough

In this session, we will discuss why edge detection is not enough in the process of line detection.

3. RANSAC Introduction

This session introduces you to the concept of RANSAC (Random Sampling Consensus).

4. RANSAC For Lines Coordinate Arrays

In this session, you will learn about RANSAC for lines coordinate arrays.

5. RANSAC for Lines Sampling Points Randomly Implementation

In this session, you will learn how to implement random lines sample points.

6. Quiz (RANSAC for Lines Sampling Points Randomly Implementation)

It's time for a short quiz on implementing random lines sample points.

7. Solution (RANSAC for Lines Sampling Points Randomly Implementation)

In this session, we will discuss the solution to the quiz on implementing random lines sample points.

8. RANSAC for Lines - Fitting Line with Two Points

In this session, you will learn about RANSAC for fitting lines with two points.

9. RANSAC for Lines - Fitting Line with Two Points Implementation

In this session, you learn how to implement RANSAC for fitting lines with two points.

10. Quiz (RANSAC for Lines Fitting Line with Two Points Implementation)

It's time for a short quiz on RANSAC for fitting lines with two points.

11. Solution (RANSAC for Lines Fitting Line with Two Points Implementation)

Let's discuss the solution to the quiz on RANSAC for fitting lines with two points.

12. RANSAC for Lines Computing Consistency Score

In this session, we will learn about the consistency score of RANSAC for Lines.

13. RANSAC For Lines Computing Consistency Score Implementation

In this session, we will compute the consistency score of RANSAC for lines.

14. RANSAC for Lines Implementation

In this session, we will implement RANSAC for lines in Python.

15. RANSAC for Lines Implementation Test on Real Image

In this session, we will perform lines implementation test on a real image.

16. Drawback

In this session, we look at a drawback of the lines implementation test on a real image.

17. RANSAC for Lines Implementation Test on Real Image Drawing and Quiz

In this session, we will perform lines implementation test on source image drawing followed by a short quiz.

18. RANSAC for Circles

In this session, you learn about RANSAC for circles.

19. RANSAC for Circles Consistency Score

In this session, we will learn about the consistency score of RANSAC for circles.

20. RANSAC for Circles Implementation

In this session, you learn how to implement RANSAC for circles.

21. RANSAC for Circles Implementation Real Image

In this session, we perform circles implementation test on real image.

22. Drawback

In this session, we look at a drawback of circles implementation test on real image.

23. RANSAC for Circles Implementation Real Image Drawing

In this session, we will perform lines implementation test on source image drawing.

24. RANSAC General

In this session, you learn about RANSAC for general use.

25. RANSAC Quiz

It's time for a short quiz on RANSAC.

26. RANSAC Quiz Solution

Let's discuss the solution to the quiz on RANSAC.

1. Hough Transform Introduction

This video introduces you to the concept of Hough transform.

2. Hough Transform as Voting

In this session, you will learn about Hough transform as voting.

3. Hough Transform as Voting Loop

In this session, you will learn about Hough transform as voting loop.

4. Hough Transform Polar Representation

In this session, you will learn about polar representation of Hough transform.

5. Hough Transform Polar Representation Benefits

In this session, you will learn the benefits of polar representation of Hough transform.

6. Hough Transform Polar Representation Implementation

In this session, you will learn to implement Hough transform polar representation.

7. Hough Transform Lines Implementation Real Image

In this session, you will learn to implement Hough transform polar representation on real images.

8. Hough Transform Lines Parameters Conversion

In this session, we will discuss the mathematical representation of Hough transform lines parameters conversion.

9. Hough Transform Lines Drawing

In this session, you will learn Hough transform lines drawing followed by a short quiz.

10. Solution (Hough Transform Lines Drawing)

In this session, let's discuss the solution for the quiz on Hough transform lines drawing.

11. Hough Transform Fast Version

In this session, let's take a look at Hough transform fast version.

12. Hough Transform Circles

In this session, we will learn about Hough transform in circles.

13. Hough Transform Circles Implementation

In this session, we will implement Hough transform in circles using python.

14. Hough Transform Circles Implementation Drawing

In this session, we will implement Hough transform for circles in drawings, followed by a quiz.

15. Solution (Hough Transform Circles Implementation Drawing)

Let's discuss the solution to the quiz on Hough transform circles implementation drawing.

1. Corner Definition

Let's understand what a corner is before we dive in deeper.

2. Why Corner

In this session, we will try to understand the importance of corners.

3. Corner Measure

In this session, you will learn to build an algorithm to detect a corner.

4. SSD

In this session, we will take a look at an example of shifting a patch followed by computing the patch-to-patch difference in eight different directions.

5. Why SSD to be Muted Somewhere

In this session, we will understand the Sum of Squared Difference (SSD) and why it needs to be muted somewhere.

6. Corner Detection Implementation 01

In this session, we will go ahead with the coding for implementing corner detection.

7. Corner Detection Implementation 02

In this session, we will continue with the coding for implementing corner detection.

8. Corner Detection Implementation 03

In this session, we will wrap up the coding for implementing corner detection.

9. Moravec Corner Detector

In this session, you will learn about the Moravec corner detector algorithm.

10. Scale Space

In this session, you will learn about scale space also known as scale issue.

11. Infinite Directions Towards Harris Corner Detector

In this session, you will learn about the infinite directions towards Harris Corner detector.

12. Harris Corner Detector 01

In this session, you will learn about Harris Corner detection (part 01).

13. Harris Corner Detector 02

In this session, we will continue our discussion on Harris Corner detection (part 02).

14. Harris Corner Detector 03

In this session, we will continue our discussion on Harris Corner detection (part 03).

15. Harris Corner Detector 04 Structure Tensor

In this session, we will discuss about the structure tensor.

16. Harris Corner Detector 05 Final Expression

In this session, we will discuss about the final expression.

17. Harris Corner Detector Implementation Speedup Convolution

In this session, we will implement speedup convolution of the Harris Corner detector.

18. Harris Corner Detector Implementation 01

In this session, we will implement the Harris Corner detector (part 01).

19. Harris Corner Detector Implementation 02

In this session, we will implement the Harris Corner detector (part 02).

20. Harris Corner Detector as Edge Detector

In this session, we will implement the Harris Corner detector as an edge detector.

1. Point Correspondence Introduction

This video provides an outline of the entire section followed by explanations about point correspondences.

2. Point Drawing Implementation

Let's go ahead and implement point drawing in this session.

3. Scale and Orientation Alignment

In this session, we will discuss two important conceptsâEUR"scale alignment and orientation alignment.

4. SIFT and HOG

In this session, we will discuss SIFT and HOG.

5. Points Matching

In this session, you will learn about points matching.

1. Introduction to Object Detection

This video session provides an introduction to object detection.

2. Classification Pipeline

Learn about the classification pipeline in this session.

3. Sliding Window Implementation

In this session, we will discuss about sliding window operation.

4. Shift Scale Rotation Invariance

In this session, you will learn about shift scale rotation invariance.

5. Person Detection

In this session, you will learn about person detection.

6. HOG Features

In this session, you will learn more about HOG and its features.

7. Hand Engineering Versus CNNs

In this session, we will look at the comparison between hand engineering and CNNs.

8. Implementation

In this session, we will implement person detection in OpenCV.

9. Activity

Let's perform an activity based on this module in this session.

1. CNNS Introduction

This video introduces you to Convolutional Neural Networks (CNNs)

2. Face Detection Implementation

Learn how to implement face detection. In this session, you will get introduced to Computer Vision library based on CNNs.

3. YOLO Implementation

In this session, we will take a look at an example of YOLO implementation.

4. YOLO Image Classification Revisited

In this session, we will revisit image classification.

5. YOLO Sliding Window Object Localization

In this session, we will discuss the details of YOLO sliding window object localizations.

6. YOLO Sliding Window Efficient Implementation

In this session, we will discuss the details of YOLO sliding window efficient implementation.

7. YOLO Introduction

This video provides a detailed introduction to You Only Look Once (YOLO).

8. YOLO Training Data Generation

In this session, we will discuss the process of YOLO training data generation.

9. YOLO Anchor Boxes

In this session, we will discuss YOLO anchor boxes.

10. YOLO Algorithm

In this session, we will implement the YOLO algorithm.

11. YOLO Non-Maxima Suppression

In this session, we will discuss YOLO non-maxima suppression.

12. YOLO RCNN

In this session, we will discuss YOLO Region-Based Convolutional Neural Networks (RCNNs).

1. Optical Flow

This video explains what optical flow is. Learn how to compute the motion vector and displacement vector of the source image.

2. BC Assumption

Let's discuss a few BC assumptions about computing the motion vector and displacement vector of the source image in this session.

3. Optical Flow Derivation

Let's take a look at the mathematical representation of optical flow derivation in this session.

1. Tracking by Detection

This video introduces you to the process of object tracking by detection.

2. Tracking by Detection Motion Model Assumption

In this session, you will learn about the motion model assumption applied for object tracking by detection.

3. Tracking KLT TLD

In this session, we will discuss the KLT (Kanade-Lucas-Tomasi) tracking algorithm.

4. Single Object Tracking

In this session, you will learn about single object tracking in videos using OpenCV in Python.

5. Multiple Object Tracking

In this session, you will learn about multiple object tracking in videos using OpenCV in Python.

6. WebCam and Saving Annotations of Multiple Object Tracking

In this session, you will learn about multiple object tracking in a live video stream using a webcam and saving annotations.

1. 3d Reconstruction Introduction

This video introduces you to 3D reconstruction.

2. 3d Motion Capture

Learn how 3D motion capture is performed in this session.

3. Camera

To understand how to reconstruct a 3D from 2D, we have to build the inverse of what happens in a camera. In this session, we will discuss the pinhole model for camera.

4. Camera Matrix

In this session, we will explore the camera matrix.

5. Triangulation

In this session, we will explore another important concept known as triangulation.

6. Camera Matrix Estimation

In this session, you will learn the process of camera matrix estimation.

7. Mocap Revisited

Let's explore motion capture a little more in this session.

1. Introduction to the Project

This video provides an overview of the entire project.

2. Introduction to Data

Let's take a look at the data that we will be using for the project.

3. Reading a Video File

Learn how to read a video file in this session.

4. Change Detection Frame Differencing

In this session, we will work on detecting the change in the frame differencing.

5. Change Detection Frame Differencing Implementation

In this session, we will work on implementing change detection frame differencing.

6. Change Detection Background Subtraction

In this session, we will work on detection of the change in background subtraction.

7. Change Detection Background Subtraction MOG

In this session, we will work on change detection background subtraction MOG.

8. Denoising using Morphology

In this session, we will work on using morphology to perform denoising.

9. Connected Components

In this session, we will work on finding out the larger binary components.

10. Connected Components Filtering

In this session, you will learn about connected components filtering.

11. Tracking Change

In this session, you will learn about tracking changes.

12. Saving Segments

In this session, you will learn about saving segments.

13. Saving and Viewing Segments

In this session, you will learn about saving and viewing segments.

14. Saving and Viewing Segments with Object Detection

In this session, you will learn about saving and viewing segments with object detection.

15. Applications

In this session, you will learn about the applications of Smart CCTV.