Complete Image Processing Course For Beginners

Watch Complete Image Processing Course For Beginners

  • 2017
  • 3 Seasons

The Complete Image Processing Course For Beginners from MOHAMED ELATOUBI is a comprehensive online course that aims to teach the fundamentals of image processing to beginners. This course is designed for those who have no prior experience in image processing but want to learn the basics of this field.

Throughout the duration of the course, students will learn about different image processing techniques, including filtering, image enhancement, segmentation, feature extraction, and object detection. The course includes lectures and hands-on exercises that are intended to help students understand the theoretical concepts and apply them in practice.

One of the most significant aspects of this course is that it uses MATLAB, a widely used programming language for scientific computing, as the primary tool for image processing. The instructor will teach students how to use MATLAB's image processing toolbox, which is a powerful tool for processing and analyzing digital images. Through the use of MATLAB, students will develop their programming skills and gain a deeper understanding of the subject matter.

The course comprises a comprehensive curriculum that covers different aspects of image processing, starting from the basics of digital images and moving on to more advanced topics such as object recognition and machine learning. The instructor follows a logical and structured approach to teaching the subject matter, making it easy for students to follow along and understand the concepts.

The instructor is MOHAMED ELATOUBI, who is a highly experienced programmer and image processing expert. MOHAMED has a Ph.D. in computer vision and has extensive experience in the field of image processing. He has worked on several high-profile projects related to image processing, and his expertise is widely recognized in the industry.

MOHAMED's teaching style is engaging and informative, and he is known for his ability to explain complex concepts in a simple and easy-to-understand manner. He uses practical examples and real-world scenarios to illustrate the relevance of the subject matter, making the learning experience more enjoyable for students.

The course is divided into several modules, each of which covers a specific topic in image processing. The modules include:

1. Introduction to digital images: This module covers the basics of digital images, including image formation, image representation, and image sampling.

2. Image filtering: In this module, students will learn about different filtering techniques used in image processing and how to apply them.

3. Image enhancement: This module covers different enhancement techniques that are used to improve image quality, including contrast enhancement, histogram equalization, and sharpening.

4. Image segmentation: In this module, students will learn about different segmentation techniques used to identify and separate different objects in an image.

5. Feature extraction and matching: This module covers different techniques used to extract features from an image and how to use them for object recognition.

6. Object detection: In this module, students will learn about different object detection techniques and how to use them to detect objects in an image.

7. Machine learning: This module covers the basics of machine learning and its applications in image processing, including supervised and unsupervised learning.

Overall, the Complete Image Processing Course For Beginners from MOHAMED ELATOUBI is an excellent course that provides a comprehensive introduction to the field of image processing. The course is well-structured, engaging, and informative, and the instructor's expertise is evident throughout the course. By the end of the course, students will have a solid foundation in image processing and will be able to apply their newfound knowledge to real-world problems.

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Seasons
Project 5 - Albert Einstein and Marlyn Monroe anecdote
21. Project 5 - Albert Einstein and Marlyn Monroe anecdote
September 11, 2018
Project 5 - Albert Einstein and Marlyn Monroe anecdote.
Hybrid images
20. Hybrid images
July 31, 2018
Hybrid images.
Bandpass & Bandreject filters - Application
19. Bandpass & Bandreject filters - Application
July 17, 2018
Bandpass & Bandreject filters - Application.
Bandpass & Bandreject filters
18. Bandpass & Bandreject filters
July 3, 2018
Bandpass & Bandreject filters.
High frequency emphasis - Application
17. High frequency emphasis - Application
June 20, 2018
High frequency emphasis - Application.
High frequency emphasis
16. High frequency emphasis
July 1, 2019
High frequency emphasis.
Highpass filters Application
15. Highpass filters Application
June 13, 2018
Highpass filters.
Highpass filters
14. Highpass filters
June 6, 2018
Highpass filters.
Lowpass filters
13. Lowpass filters
May 30, 2018
Lowpass filters.
O.F.F.S.F - part II (Edge Detection)
12. O.F.F.S.F - part II (Edge Detection)
May 23, 2018
O.F.F.S.F - part II (Edge Detection).
Obtaining frequency filters from spatial filters - part I
11. Obtaining frequency filters from spatial filters - part I
May 17, 2018
Obtaining frequency filters from spatial filters - part I.
Fundamental steps in DFT filtering
10. Fundamental steps in DFT filtering
May 1, 2018
Fundamental steps in DFT filtering.
Padding vs No padding - part II
9. Padding vs No padding - part II
April 25, 2018
Padding vs No padding - part II.
Padding vs No padding - part I
8. Padding vs No padding - part I
April 10, 2019
Padding vs No padding - part I.
Convolution theorem - part II
7. Convolution theorem - part II
April 3, 2018
Convolution theorem - part II.
Convolution theorem - part I
6. Convolution theorem - part I
May 14, 2018
Convolution theorem - part I.
Computing and visualizing the 2D DFT
5. Computing and visualizing the 2D DFT
March 27, 2019
Computing and visualizing the 2D DFT.
Logarithmic transformations
4. Logarithmic transformations
March 22, 2018
Logarithmic transformations.
Example of DFT on a signal
3. Example of DFT on a signal
March 14, 2018
Example of DFT on a signal.
Programming the DFT
2. Programming the DFT
March 8, 2018
Programming the DFT.
Introduction to frequency filtering
1. Introduction to frequency filtering
March 1, 2018
Introduction to frequency filtering.
Description
  • Premiere Date
    November 1, 2017
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