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Digital Image Processing

Course Reviews

Track :

Computer Science

Lessons no : 42

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What will you learn in this course?
  • Enhance digital image quality using filtering, sharpening, and noise reduction techniques for improved visual clarity and analysis
  • Apply image segmentation and edge detection methods to identify objects and boundaries in digital images effectively
  • Implement color correction, histogram equalization, and contrast enhancement for accurate image representation and visualization
  • Utilize morphological operations to analyze and process binary and grayscale images for pattern recognition tasks
  • Develop skills in image compression and format conversion to optimize storage and transmission efficiency
  • Perform feature extraction and image annotation to facilitate object recognition and classification in computer vision applications
  • Analyze and interpret digital images using Fourier transform and frequency domain techniques for advanced image analysis
  • Design algorithms for image enhancement, restoration, and noise removal to improve image quality in real-world scenarios
  • Apply machine learning and deep learning models for automated image analysis and pattern recognition tasks
  • Utilize software tools like MATLAB, OpenCV, or Python libraries for practical digital image processing implementation
  • Evaluate image processing techniques for specific applications such as medical imaging, remote sensing, and industrial inspection
  • Troubleshoot common issues in digital image processing workflows to ensure accurate and reliable results

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Lessons | 42
Computer Vision and Image Processing – Fundamentals and Applications Intro Video
00:08:02

Computer Vision and Image Processing – Fundamentals and Applications Intro Video
Lec 1 Introduction to Computer Vision
00:46:31

Lec 1 Introduction to Computer Vision
Lec 2 Introduction to Digital Image Processing
00:55:46

Lec 2 Introduction to Digital Image Processing
Lec 3 Image Formation Radiometry
00:41:26

Lec 3 Image Formation Radiometry
Lec 4 Shape From Shading
00:48:53

Lec 4 Shape From Shading
Lec 5 Image Formation Geometric Camera Models I
00:31:16

Lec 5 Image Formation Geometric Camera Models I
Lec 6 Image Formation Geometric Camera Model II
00:21:11

Lec 6 Image Formation Geometric Camera Model II
Lec 7 Image Formation Geometric Camera Model III
01:09:27

Lec 7 Image Formation Geometric Camera Model III
Lec 8 Image Formation in a Stereo Vision Setup
01:01:45

Lec 8 Image Formation in a Stereo Vision Setup
Lec 9 Image Reconstruction from a Series of Projections
00:42:44

Lec 9 Image Reconstruction from a Series of Projections
Lec 10 Image Reconstruction from a Series of Projections
00:32:59

Lec 10 Image Reconstruction from a Series of Projections
Lec 11 Image Transforms I
00:42:34

Lec 11 Image Transforms I
Lec 12 Image Transforms II
00:58:35

Lec 12 Image Transforms II
Lec 13 Image Transforms III
00:48:29

Lec 13 Image Transforms III
Lec 14 Image Transforms IV
01:14:32

Lec 14 Image Transforms IV
Lec 15 Image Enhancement
01:01:29

Lec 15 Image Enhancement
Lec 16 Image Filtering I
01:00:28

Lec 16 Image Filtering I
Lec 17 Image Filtering II
01:04:47

Lec 17 Image Filtering II
Lec 18 Colour Image Processing I
00:41:37

Lec 18 Colour Image Processing I
Lec 19 Colour Image Processing II
01:17:56

Lec 19 Colour Image Processing II
Lec 20 Image Segmentation
00:54:01

Lec 20 Image Segmentation
Lec 21 Image Features and Edge Detection
01:10:18

Lec 21 Image Features and Edge Detection
Lec 22 Edge Detection
00:59:14

Lec 22 Edge Detection
Lec 23 Hough Transform
00:38:22

Lec 23 Hough Transform
Lec 24 Image Texture Analysis I
00:58:23

Lec 24 Image Texture Analysis I
Lec 25 Image Texture Analysis II
00:42:27

Lec 25 Image Texture Analysis II
Lec 26 Object Boundary and Shape Representations I
00:45:50

Lec 26 Object Boundary and Shape Representations I
Lec 27 Object Boundary and Shape Representations II
00:57:55

Lec 27 Object Boundary and Shape Representations II
Lec 28 Interest Point Detectors
00:51:28

Lec 28 Interest Point Detectors
Lec 29 Image Features HOG and SIFT
01:08:24

Lec 29 Image Features HOG and SIFT
Lec 30 Introduction to Machine Learning I
00:40:57

Lec 30 Introduction to Machine Learning I
Lec 31 Introduction to Machine Learning II
00:56:13

Lec 31 Introduction to Machine Learning II
Lec 32 Introduction to Machine Learning III
01:25:03

Lec 32 Introduction to Machine Learning III
Lec 33 Introduction to Machine Learning IV
00:51:14

Lec 33 Introduction to Machine Learning IV
Lec 34 Introduction to Machine Learning V
01:09:35

Lec 34 Introduction to Machine Learning V
Lec 35 Artificial Neural Network for Pattern Classification I
01:00:49

Lec 35 Artificial Neural Network for Pattern Classification I
Lec 36 Artificial Neural Network for Pattern Classification II
01:07:37

Lec 36 Artificial Neural Network for Pattern Classification II
Lec 37 Introduction to Deep Learning
01:10:33

Lec 37 Introduction to Deep Learning
Lec 38 Gesture Recognition
01:18:09

Lec 38 Gesture Recognition
Lec 39 Background Modelling and Motion Estimation
00:45:03

Lec 39 Background Modelling and Motion Estimation
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                           Digital Image Processing course in hindi,
in this course covers techniques to manipulate and analyze digital images for various applications in computer vision and image analysis.