بینایی کامپیوتر

1.1. Syllabus

12

2.1. Basic Definitions

20

3.1. Motivations

18

4.1. Conducted Research at IPL

18

1.2. Brief Review on Signals & Systems

19

2.2. Some Simple Image Processing Tasks & Convolution

18

3.2. Fourier Transform 01

15

4.2. Fourier Transform 02

09

5.2. Fourier Transform 03

11

6.2. Fourier Transform 04

06

7.2. Image Filtering 01

16

8.2. Image Filtering 02

11

1.3. Review on Linear Algebra

18

2.3. Parameterizing the Rotation Matrix

07

3.3. Homogeneous Coordinates 01

19

4.3. Homogeneous Coordinates 02

12

5.3. Pinhole Camera Model 01

16

6.3. Pinhole Camera Model 02

11

1.4. Parallel projection 01

11

2.4. Parallel projection 02

05

3.4. Perspective projection

14

4.4. Building simple image mosaics 01

09

5.4. Building simple image mosaics 02

08

6.4. External & internal camera parameters

14

7.4. Homography map 01

10

8.4. Homography map 02

20

9.4. General camera translation

08

1.5. Two calibrated cameras 01

19

2.5. Two calibrated cameras 02

12

3.5. Two calibrated cameras & Calibration using known 3D object

08

4.5. Computing camera parameters from the projection matrix

14

5.5. Computing the projection matrix from E 01

16

6.5. Computing the projection matrix from E 02

21

7.5. Uncalibrated cameras - The fundamental matrix (F) & Projective reconstruction

18

8.5. Computing the projection matrices from F

16

9.5. Rectification using metric information

14

1.6. Surface reconstruction & rendering

17

2.6. Voronoi cells & Delaunay triangulations

06

3.6. Surface mappings, image-based rendering & Planar surfaces, linear mappings

15

1.7. Multiple Cameras 01

18

2.7. Multiple Cameras 02

17

1.8. Some Basic Properties

15

2.8. Derivatives 01

13

3.8. Derivatives 02

05

4.8. Keypoints 01

15

5.8. Keypoints 02

11

6.8. Keypoints 03

07

7.8. Scale Selection

06

8.8. SIFT

14

9.8. HoG

10

10.8. GIST

10

1.9. Keypoint Matching

17

2.9. Template Matching & Chamfer Matching

13

3.9. KD- Tree Search

10

1.10. Model Fitting

15

2.10. M - estimator

06

3.10. Robust Estimation

17

1.11. Data grouping

18

2.11. Learning Types

05

3.11. K-Means clustering

12

4.11. K-nearest neighbor classifier

12

5.11. Expectation-Maximization

12

6.11. Mean shift

17

1.12. Introduction to Deep Learning

22

2.12. Basic Machine Learning Concepts

12

3.12. Deep Learning in Computer Vision

09

4.12. Neural Network Models

17

5.12. Neural Networks

14

6.12. Convolutional Neural Networks

10

7.12. Recurrent Neural Networks

09

8.12. Transformer models

18

9.12. Popular Networks in Computer Vision

17

1.13. Opencv

27

2.13. Edge

24

3.13. Open3d

43

4.13. Homography

31

5.13. Surface Reconstruction

22

6.13. Template Matching 01

26

7.13. Template Matching 02

33

8.13. Sift

26

9.13. Feature Matching 01

23

10.13. Feature Matching 02

13

11.13. Comparison

37

12.13. Scikit Image

22

13.13. HoG

53

14.13. Scikit-learn

07

15.13. Clustering

42

16.13. Pytorch-Introduction

20

17.13. Pytorch-Torchvision

07

18.13. Pytorch-Dataset

31

19.13. Pytorch-Model

25

20.13. Pytorch-Train Loop

30

21.13. Pytorch-Inference

06