Published 5/2023
Created by Zeeshan Ahmad
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 182 Lectures ( 22h 56m ) | Size: 8.22 GB
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
Requirements
Basic Programming Skill will be an asset but not necessary. You will learn everything in this 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 Processing5. Communication Systems.6. Power Electronics.7. Probability and Statistics.8. Time Series Analysis.9. Finance10. Decision TheoryWhy Image Processing?Image Processing has found its applications in numerous fields of Engineering and Sciences.Few of them are the following.1. Deep Learning2. Computer Vision3. Medical Imaging4. Radar Engineering5. Robotics6. Computer Graphics7. Face detection8. Remote Sensing9. Agriculture and food industryCourse OutlineSection 01: Introduction of the courseSection 02: Python crash courseSection 03: Fundamentals of Signal ProcessingSection 04: ConvolutionSection 05: Signal DenoisingSection 06: Complex NumbersSection 07: Fourier TransformSection 08: FIR Filter DesignSection 09: IIR Filter DesignSection 10: Introduction to Google ColabSection 11: Wavelet Transform of a SignalSection 12: Fundamentals of Image ProcessingSection 13: Fundamentals of Image Processing With NumPy and MatplotlibSection 14: Fundamentals of Image Processing with OpenCVSection 15: Arithmetic and Logic Operations with ImagesSection 16: Geometric Operations with ImagesSection 17: Point Level OR Gray level TransformationSection 18: Histogram ProcessingSection 19: Spatial Domain FilteringSection 20: Frequency Domain FilteringSection 21: Morphological ProcessingSection 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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