بینایی کامپیوتر

1.1. Syllabus

12:37

2.1. Basic Definitions

20:06

3.1. Motivations

18:43

4.1. Conducted Research at IPL

18:03

1.2. Brief Review on Signals & Systems

19:06

2.2. Some Simple Image Processing Tasks & Convolution

18:53

3.2. Fourier Transform 01

15:43

4.2. Fourier Transform 02

09:33

5.2. Fourier Transform 03

11:59

6.2. Fourier Transform 04

06:40

7.2. Image Filtering 01

16:05

8.2. Image Filtering 02

11:54

1.3. Review on Linear Algebra

18:49

2.3. Parameterizing the Rotation Matrix

07:19

3.3. Homogeneous Coordinates 01

19:43

4.3. Homogeneous Coordinates 02

12:40

5.3. Pinhole Camera Model 01

16:15

6.3. Pinhole Camera Model 02

11:22

1.4. Parallel projection 01

11:09

2.4. Parallel projection 02

05:27

3.4. Perspective projection

14:02

4.4. Building simple image mosaics 01

09:28

5.4. Building simple image mosaics 02

08:50

6.4. External & internal camera parameters

14:06

7.4. Homography map 01

10:34

8.4. Homography map 02

20:04

9.4. General camera translation

08:22

1.5. Two calibrated cameras 01

19:39

2.5. Two calibrated cameras 02

12:05

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

08:56

4.5. Computing camera parameters from the projection matrix

14:52

5.5. Computing the projection matrix from E 01

16:11

6.5. Computing the projection matrix from E 02

21:29

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

18:01

8.5. Computing the projection matrices from F

16:39

9.5. Rectification using metric information

14:10

1.6. Surface reconstruction & rendering

17:30

2.6. Voronoi cells & Delaunay triangulations

06:20

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

15:30

1.7. Multiple Cameras 01

18:38

2.7. Multiple Cameras 02

17:00

1.8. Some Basic Properties

15:45

2.8. Derivatives 01

13:58

3.8. Derivatives 02

05:51

4.8. Keypoints 01

15:51

5.8. Keypoints 02

11:07

6.8. Keypoints 03

07:29

7.8. Scale Selection

06:57

8.8. SIFT

14:44

9.8. HoG

10:26

10.8. GIST

10:23

1.9. Keypoint Matching

17:08

2.9. Template Matching & Chamfer Matching

13:41

3.9. KD- Tree Search

10:45

1.10. Model Fitting

15:03

2.10. M - estimator

06:56

3.10. Robust Estimation

17:58

1.11. Data grouping

18:27

2.11. Learning Types

05:12

3.11. K-Means clustering

12:57

4.11. K-nearest neighbor classifier

12:33

5.11. Expectation-Maximization

12:16

6.11. Mean shift

17:27

1.12. Introduction to Deep Learning

22:33

2.12. Basic Machine Learning Concepts

12:59

3.12. Deep Learning in Computer Vision

09:57

4.12. Neural Network Models

17:44

5.12. Neural Networks

14:12

6.12. Convolutional Neural Networks

10:58

7.12. Recurrent Neural Networks

09:40

8.12. Transformer models

18:53

9.12. Popular Networks in Computer Vision

17:48

1.13. Opencv

27:54

2.13. Edge

24:39

3.13. Open3d

43:35

4.13. Homography

31:21

5.13. Surface Reconstruction

22:42

6.13. Template Matching 01

26:26

7.13. Template Matching 02

33:23

8.13. Sift

26:58

9.13. Feature Matching 01

23:58

10.13. Feature Matching 02

13:03

11.13. Comparison

37:32

12.13. Scikit Image

22:06

13.13. HoG

53:02

14.13. Scikit-learn

07:45

15.13. Clustering

42:42

16.13. Pytorch-Introduction

20:17

17.13. Pytorch-Torchvision

07:44

18.13. Pytorch-Dataset

31:28

19.13. Pytorch-Model

25:51

20.13. Pytorch-Train Loop

30:27

21.13. Pytorch-Inference

06:55