Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
22 Hour(s) 57 Minute(s)
Language
English
Taught by
Zeeshan Ahmad
Rating
Course Overview
Signal and Image Processing Algorithms : Theory, Intuition, Mathematics, Numerical examples, and Python Implementation
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
Course Content
- 10 section(s)
- 183 lecture(s)
- Section 1 Introduction of the Course
- Section 2 Python Crash Course
- Section 3 Fundamentals of Signal Processing
- Section 4 The Convolution
- Section 5 Signal Denoising
- Section 6 Complex Number Systems
- Section 7 Fourier Transform
- Section 8 FIR Filter Design
- Section 9 IIR Filter Design
- Section 10 Introduction of Google Colab
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
Skills covered in this course
Reviews
-
GGemma S. Parra
I like the topics and the easy and clear explanation.
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RRita Osipov
very poor and slow progress you could save me first 30 minutes of the lecture with a reference to the guide
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MMiktat Durmuş
şimdilik her şey çok iyi
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SSHRADDHA PATIL
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