1. Image representation and processing, color models and transformations among them

2. Morphologic operations with image, contours detection and structural analysis

3. Filters and kernels. Edge operators.

4. Blending, seemless cloning, morphing, inpainting

5. Image segmentation. MeanShift filter. GrabCut. Intrinsic image.

6. Image alignment and registration. Phase correlation. ECC. Image features: SIFT, SURF, BRIEF, ORB

7. Camera and video. Optical flow. Stereovision. Camera calibration.

8. Machine learning: PCA, LDA, eigenimages, SVM, cascade regressor and gradient boosting 9. Object detectors. Hough transform. Haar detector. HOG detector. LBPH.

10. Object trackers. Kalman filter. CamShift. MIL tracker. Motion detector.

11. Usage of deep learning models: Colorization, YOLO detectors, vectorization and recognition, EAST text detector, Tesseract OCR, GOTURN. The used programming languages: Python a C++

Type of methodology: Combination of lecture and hands-on

Participants receive the certificate of attendance: Yes

Paid training activity for participants: Yes, for all

Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge