1. SylabUZ
2. Wydział Informatyki, Elektrotechniki i Automatyki
3. semestr zimowy 2021/2022
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 recognition: image processing, filtration, segmentation, feature extraction, image 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, centroid method and active contour method, 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: 20-07-2021 10:40)