Python for Signal and Image Processing Master Class [2023]

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Zeeshan Ahmad

22:56:45

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1. Introduction of the Course

1. Introduction of the Course.mp4

05:52

2. Pace of the Lecture Delivery.mp4

02:43

3.1 Signal and Image Processing With Python.zip

3. Course Material.html

2. Python Crash Course

1. Introduction of the Section.mp4

00:59

2. Python Installment.mp4

04:25

3. Installing Python Packages.mp4

04:25

4. Introduction of Jupyter Notebook.mp4

14:27

5. Arithmetic Operations Part01.mp4

07:54

6. Arithmetic Operations Part02.mp4

09:27

7. Arithmetic Operations Part03.mp4

07:49

8. Dealing With Arrays Part01.mp4

11:09

9. Dealing With Arrays Part02.mp4

11:26

10. Dealing With Arrays Part03.mp4

21:09

11. Plotting and Visualization Part01.mp4

17:51

12. Plotting and Visualization Part02.mp4

15:03

13. Plotting and Visualization Part03.mp4

13:34

14. Plotting and Visualization Part04.mp4

07:35

15. Lists in Python.mp4

20:27

16. For Loop Part01.mp4

21:03

17. For Loop Part02.mp4

20:36

3. Fundamentals of Signal Processing

1. Introduction of the Section.mp4

01:59

2. Basic Elements of Signal Processing.mp4

08:55

3. AD Conversion.mp4

16:52

4. AD Conversion With Python.mp4

10:44

5. Coding the Quantized Signal.mp4

03:50

6. Fundamentals of Continuous time signals.mp4

16:47

7. Continuous time signals in Python.mp4

18:54

8. Fundamentals of Discrete time signals.mp4

08:06

9. Discrete time signals in python.mp4

17:59

10. Sampling and Reconstruction.mp4

10:19

11. Sampling and Reconstruction in Python.mp4

12:41

4. The Convolution

1. Introduction of the Section.mp4

01:46

2. The Convolution Sum.mp4

17:26

3. Numerical Example on Convolution.mp4

18:26

4. Full mode convolution.mp4

02:54

5. Convolution Using For Loop in Python.mp4

24:28

6. Convolution Using Numpy.mp4

05:51

7. Signal Denoising by Convolution.mp4

11:45

8. Edge Detection by Convolution.mp4

06:32

9. The Convolution Theorem.mp4

08:33

5. Signal Denoising

1. Introduction of the Section.mp4

02:12

2. Signal Denoising by Moving Average Filter.mp4

07:33

3. Implementing Moving Average Filter in Python.mp4

13:57

4. Gaussian Mean Filter.mp4

08:45

5. Gaussian Mean Filter With Python.mp4

18:36

6. Median Filter.mp4

06:58

7. Median Filter in Python.mp4

07:21

8. Removing Spiky Noise With Median Filter.mp4

04:47

9. Removing Spiky Noise With Median Filter in Python Part01.mp4

22:01

10. Removing Spiky Noise With Median Filter in Python Part02.mp4

08:25

6. Complex Number Systems

1. Introduction of Complex Numbers.mp4

04:43

2. Complex Numbers in Python.mp4

04:34

3. Mathematical Operations Part01.mp4

02:58

4. Mathematical Operations Part02.mp4

03:41

5. Mathematical Operations in Python.mp4

04:25

6. Magnitude and Phase Calculations.mp4

01:28

7. Magnitude and Phase Calculations in Python.mp4

02:39

8. Complex Sine Wave.mp4

01:35

9. Complex Sine Wave in Python.mp4

04:39

7. Fourier Transform

1. Introduction of the Section.mp4

01:43

2. Combining Sine and Cosine Wave.mp4

11:12

3. Generating Waves in Python.mp4

13:58

4. Mechanism of Fourier Transform.mp4

20:28

5. Step by Step Coding of Fourier Transform.mp4

27:02

6. Fast Fourier Transform.mp4

09:06

7. Fourier Transform of Signal With DC Component.mp4

09:50

8. Amplitude and Power Spectrum.mp4

08:43

9. Inverse Fourier Transform.mp4

06:14

10. Application of Fourier Transform Part01.mp4

05:06

11. Application of Fourier Transform Part02.mp4

05:08

8. FIR Filter Design

1. Introduction of the Section.mp4

02:10

2. Introduction of Digital Filters.mp4

08:20

3. Steps of Designing FIR Filters.mp4

26:50

4. FIR Filter Design by Least Square Method.mp4

13:17

5. FIR Filter Design by Window Method.mp4

07:14

6. FIR Zero Shift Filter.mp4

12:29

7. Low Pass FIR Filter.mp4

08:13

8. Low Pass FIR Filter in Python.mp4

09:52

9. High Pass FIR Filter.mp4

06:20

10. High Pass FIR Filter in Python.mp4

06:41

11. Band Pass FIR Filter.mp4

06:27

12. Band Pass FIR Filter in Python.mp4

07:41

13. Task for Students.mp4

01:30

9. IIR Filter Design

1. Introduction of the Section.mp4

01:30

2. Introduction of IIR Filter.mp4

08:30

3. IIR Butterworth Filter Design in Python.mp4

12:00

4. Low Pass IIR Filter.mp4

06:55

5. High Pass IIR Filter.mp4

06:21

6. Band Pass IIR Filter.mp4

05:56

7. Comparison Between FIR and IIR Filters.mp4

02:27

8. Task for Students.mp4

00:56

10. Introduction of Google Colab

1. Introduction of the Section.mp4

02:23

2. Python Coding in Colab Part01.mp4

13:51

3. Python Coding in Colab Part02.mp4

06:35

4. Python Coding in Colab Part03.mp4

02:48

11. Wavelet Transform of Signals

1. Introduction of the Section.mp4

03:21

2. Limitations of Fourier Transform.mp4

04:47

3. Why Wavelet Transform.mp4

08:16

4. Wavelet Families.mp4

06:08

5. Filter Banks of Discrete Wavelet.mp4

04:12

6. Single Level Decomposition.mp4

05:10

7. Single Level Decomposition With Python.mp4

10:32

8. Multilevel Decomposition.mp4

04:38

9. Multilevel Decomposition With Python.mp4

06:24

10. Time Frequency Analysis.mp4

07:26

11. Time Frequency Analysis With Python.mp4

06:56

12. Fundamentals of Image Processing

1. Introduction of the Section.mp4

02:15

2. Concept of an Image.mp4

06:39

3. How Computer sees the Image.mp4

05:30

4. Digital Image Processing.mp4

05:37

13. Image Fundamentals With NumPy and Matplotlib

1. Introduction of the Section.mp4

02:10

2. Reading Displaying and Saving Image.mp4

12:32

3. Image Formats.mp4

07:54

4. Red Green and Blue Components of Image.mp4

07:11

14. Image Fundamentals With OpenCV

1. Introduction of the Section.mp4

02:45

2. Image Reading and Displaying.mp4

13:09

3. Image Resizing and Flipping.mp4

07:56

15. Arithmetic and Logic Operations on Images

1. Introduction of the Section.mp4

03:14

2. Arithmetic Operations.mp4

07:31

3. Arithmetic Operations With Python.mp4

16:20

4. Logical Operations.mp4

10:59

5. Logical Operations With Python.mp4

09:33

16. Geometric Operations

1. Introduction of the Section.mp4

02:06

2. Translation Rotation and Affine Transformation.mp4

07:01

3. Translation Rotation and Affine Transformation With Python.mp4

10:06

4. Scaling Zooming Shrinking and Cropping.mp4

08:56

17. Gray Level and Point Level Transformation

1. Introduction of the Section.mp4

02:51

2. Negative Point Transformation.mp4

02:27

3. Negative Point Transformation with Python.mp4

03:36

4. Log Transformation.mp4

03:45

5. Log Transformation With Python.mp4

04:39

6. Gamma Transformation.mp4

03:27

7. Gamma Transformation With Python.mp4

03:24

8. Auto-contrast and Piece Wise Linear Contrast Functions.mp4

03:56

9. Contrast Functions With Python.mp4

04:28

18. Histogram Processing

1. Introduction of the Section.mp4

03:45

2. Histogram of an Image.mp4

03:59

3. Histogram of Image With Python Part01.mp4

14:12

4. Histogram of Image With Python Part02.mp4

11:20

5. Histogram Equalization With Numerical Example.mp4

16:10

6. Histogram Equalization With Python.mp4

07:30

19. Spatial Domain Filtering

1. Introduction of the Section.mp4

03:58

2. Neighborhood Processing.mp4

05:35

3. 2D Convolution.mp4

10:56

4. 2D Convolution With Python.mp4

05:41

5. Applications of 2D Convolution.mp4

04:52

6. Applications of 2D Convolution With Python.mp4

07:32

7. Mean Filter.mp4

06:30

8. Mean Filtering of Image With Python.mp4

05:19

9. Gaussian Filter.mp4

05:21

10. Gaussian Filtering of Image With Python.mp4

02:47

11. Median Filter.mp4

04:06

12. Mean Filtering of Image With Python.mp4

04:11

13. The Laplacian.mp4

03:56

14. Laplacian With Python.mp4

06:26

15. High Boost Filter.mp4

02:35

16. High Boost Filtering of Image With Python.mp4

03:20

17. Sobel Filters.mp4

04:27

18. Sobel Filtering of Image With Python.mp4

03:36

19. Canny Edge Detection.mp4

05:50

20. Canny Edge Detection With Python.mp4

02:54

20. Frequency Domain Filtering

1. Introduction of the Section.mp4

01:51

2. 2D Fourier Transform.mp4

05:56

3. 2D Fourier Transform With Python.mp4

05:46

4. Low Pass and High Pass Filters.mp4

07:44

5. Low Pass and High Pass Filters With Python.mp4

06:36

6. High Boost and Other Filters.mp4

03:11

7. Fourier Transform of High Boost Filter.mp4

03:31

21. Morphological Processing

1. Introduction of the Section.mp4

02:48

2. Dilation and Erosion.mp4

05:04

3. Dilation and Erosion With Python.mp4

06:31

4. Morphological Filtering.mp4

03:55

5. Morphological Filtering With Python.mp4

05:04

6. Image Gradient Using Morphology.mp4

01:45

7. Morphological Gradient With Python.mp4

01:38

22. 2D Wavelet Transform

1. Introduction of the Section.mp4

02:26

2. Single Level Decomposition and Reconstruction.mp4

05:22

3. Single Level Decomposition and Reconstruction With Python.mp4

07:26

4. Multi-level Decomposition and Reconstruction.mp4

03:33

5. Multi-level Decomposition and Reconstruction With Python.mp4

04:52

6. Image Denoising Using Wavelet Transform.mp4

02:16

7. Image Denoising Using Wavelet Transform in Python.mp4

03:11

Description

Signal and Image Processing Algorithms : Theory, Intuition, Mathematics, Numerical examples, and Python Implementation

What You'll Learn?

Fundamentals of Signals and Image Processing.
Analog to digital conversion.
Sampling and Reconstruction.
Nyquist Theorem.
Convolution for Signal and Images.
Signal and Image denoising.
Fourier transform of Signals and Images.
Signal filtering by FIR and IIR filters.
Image Filtering in Spatial and Frequency Domain
Wavelet Transform for Signal and Images.
Histogram Processing
Arithmetic, Logic and Point Level Operations on Images
Implementation of all Signal and Image Processing Algorithms in Python
Python Crash Course

Who is this for?

Anyone who wants to learn Signal and Image Processing from scratch using Python.

Anyone who wants to work in Signal and Image Processing area.

Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python.

Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.

Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python.

What You Need to Know?

Basic Programming Skill will be an asset but not necessary. You will learn everything in this course.

More details

Description
This course will bridge the gap between the theory and implementation of Signal and Image Processing Algorithms and their implementation in Python. All the lecture slides and python codes are provided.
Why Signal Processing?
Since the availability of digital computers in the 1970s, digital signal processing has found its way in all sections of engineering and sciences.
Signal processing is the manipulation of the basic nature of a signal to get the desired shaping of the signal at the output. It is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals.
Following areas of sciences and engineering are specially benefitted by rapid growth and advancement in signal processing techniques.
1. Machine Learning.
2. Data Analysis.
3. Computer Vision.
4. Image Processing
5. Communication Systems.
6. Power Electronics.
7. Probability and Statistics.
8. Time Series Analysis.
9. Finance
10. Decision Theory

Why Image Processing?
Image Processing has found its applications in numerous fields of Engineering and Sciences.
Few of them are the following.
1. Deep Learning
2. Computer Vision
3. Medical Imaging
4. Radar Engineering
5. Robotics
6. Computer Graphics
7. Face detection
8. Remote Sensing
9. Agriculture and food industry

Course Outline
Section 01: Introduction of the course
Section 02: Python crash course
Section 03: Fundamentals of Signal Processing
Section 04: Convolution
Section 05: Signal Denoising
Section 06: Complex Numbers
Section 07: Fourier Transform
Section 08: FIR Filter Design
Section 09: IIR Filter Design
Section 10: Introduction to Google Colab
Section 11: Wavelet Transform of a Signal
Section 12: Fundamentals of Image Processing
Section 13: Fundamentals of Image Processing With NumPy and Matplotlib
Section 14: Fundamentals of Image Processing with OpenCV
Section 15: Arithmetic and Logic Operations with Images
Section 16: Geometric Operations with Images
Section 17: Point Level OR Gray level Transformation
Section 18: Histogram Processing
Section 19: Spatial Domain Filtering
Section 20: Frequency Domain Filtering
Section 21: Morphological Processing
Section 22: Wavelet Transform of Images
Who this course is for:
Anyone who wants to learn Signal and Image Processing from scratch using Python.
Anyone who wants to work in Signal and Image Processing area.
Those students who know the Maths of Signal and Image Processing but don't know how to implement with Python.
Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
Students who want to learn data and Time series filtering, Image filtering, Image manipulation and different Image Processing techniques.
Students and practitioners who know implementation of signal and image processing algorithms in MATLAB but want to switch to Python.

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Dr. Zeeshan is PhD in Electrical and Computer Engineering from Ryerson University Toronto. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer and Electrical Engineering. His research interests include Machine learning, Deep learning, Computer vision, Signal and Image processing and multimodal fusion. He has publications in reputed journals and conferences.

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