Python for Signal and Image Processing Master Class
Signal and Image Processing Algorithms : Theory, Intuition, Mathematics, Numerical examples, and Python Implementation
Ratings 4.34 / 5.00
What You Will 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
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 Should Attend!
- 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.