Lectures:
1. Digital image. Sensors, transformation to the digital form.
2. Transformations of brightness. Geometric transformations.
3. Convolution and image filtration.
4. Fourier transform and its application in images.
5. Image compression.
6. Edge detection, corner detection, morphological image processing.
7. Segmentation methods.
8. Advanced segmentation methods.
9. Selection and computation of features for pattern recognition.
10. Classification methods.
11. Analysis of time-varying images. Object tracking.
12. Scene reconstruction from pair of images. Camera calibration.
13. Depth data processing. Object registration.
14. Spare space.
Exercises:
1. Introduction to OpenCV.
2. Gamma correction, histogram equalization.
3. Convolution, convolution masks, image denoising.
4. Discrete Fourier Transform.
5. Morphological image processing. Erosion and dilatation operations.
6. Edge detection.
7. Image segmentation, thresholding, adaptive thresholding.
8. Distance-based image segmentation.
9. Classification methods. Computing moments.
10. Classification using an artificial neural network.
11. Tracking of objects in video sequences. Kalman filtering.
12. Object registration in depth data.
13. Spare space.
14. Credit.
1. Digital image. Sensors, transformation to the digital form.
2. Transformations of brightness. Geometric transformations.
3. Convolution and image filtration.
4. Fourier transform and its application in images.
5. Image compression.
6. Edge detection, corner detection, morphological image processing.
7. Segmentation methods.
8. Advanced segmentation methods.
9. Selection and computation of features for pattern recognition.
10. Classification methods.
11. Analysis of time-varying images. Object tracking.
12. Scene reconstruction from pair of images. Camera calibration.
13. Depth data processing. Object registration.
14. Spare space.
Exercises:
1. Introduction to OpenCV.
2. Gamma correction, histogram equalization.
3. Convolution, convolution masks, image denoising.
4. Discrete Fourier Transform.
5. Morphological image processing. Erosion and dilatation operations.
6. Edge detection.
7. Image segmentation, thresholding, adaptive thresholding.
8. Distance-based image segmentation.
9. Classification methods. Computing moments.
10. Classification using an artificial neural network.
11. Tracking of objects in video sequences. Kalman filtering.
12. Object registration in depth data.
13. Spare space.
14. Credit.