1. SylabUZ
2. Wydział Informatyki, Elektrotechniki i Automatyki
3. semestr zimowy 2022/2023
4. Informatyka - Program Erasmus drugiego stopnia
5. Image recognition

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Image recognition - opis przedmiotu

Cel przedmiotu

To familiarize students with the techniques of image processing, segmentation, recognition and classification.

Wymagania wstępne

Zapisz zmiany

Knowledge of numerical methods, computer graphics, data analysis, operational research and machine learning.

Zakres tematyczny

1. Basic operations: packages and libraries for image processing and recognition, loading and saving images, image types, color spaces and histograms.

2. Image processing: cropping and affine operations, point operators, image intensity transformation, basics of image filtering, morphological operations.

3. Image segmentation: line and edge detection, thresholding methods, watershed method, artificial neural networks.

4. Feature extraction: contour and region descriptors, corner and center detection, SIFT descriptors, neural networks for descriptor generation

5. Object recognition and classification: image classification methods, pattern classification by matching prototypes, using artificial neural networks to detect and classify objects in images

Metody kształcenia

conventional lecture, exercises, project

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

lecture - obtaining a positive grade from the written test

laboratory - obtaining positive grades from laboratory exercises reports

project - obtaining a positive assessment of the completed project

final grade = 30% lecture + 40% laboratory + 30% project

 

Literatura podstawowa

1. Digital Image Processing /Rafael C. Gonzalez, Richard E. Woods/ Pearson, 2018.
2. Computer Vision: Algorithms and Applications /Richard Szeliski / Springer, 2010.
3. Computer Vision Projects with OpenCV and Python 3 /Matthew Rever/ Packt Publishing, 2018.
4. Hands-On Image Processing with Python /Sandipan Dey/ Packt Publishing, 2018
5. Deep Learning with Python /François Chollet/ Manning, 2017.

 

Literatura uzupełniająca

Uwagi

Zmodyfikowane przez dr hab. inż. Marek Kowal, prof. UZ (ostatnia modyfikacja: 20-04-2022 16:19)