Image recognition - course description

General information

Course name	Image recognition
Course ID	11.3--INFD-RozObr- Er
Faculty	Faculty of Computer Science, Electrical Engineering and Automatics
Field of study	Computer Science
Education profile	academic
Level of studies	Second-cycle Erasmus programme
Beginning semester	winter term 2021/2022

Course information

Semester	2
ECTS credits to win	4
Course type	obligatory
Teaching language	english
Author of syllabus	prof. dr hab. inż. Andrzej Obuchowicz

Classes forms

The class form	Hours per semester (full-time)	Hours per week (full-time)	Hours per semester (part-time)	Hours per week (part-time)	Form of assignment
Project	15	1	-	-	Credit with grade
Laboratory	30	2	-	-	Credit with grade
Lecture	15	1	-	-	Credit with grade

Aim of the course

To familiarize students with the techniques of image recognition: image processing, filtration, segmentation, feature extraction, image classification.

Prerequisites

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

Scope

Basic operations: packages and libraries for image processing and recognition, loading and saving images, image types, color spaces and histograms.
Image processing: cropping and affine operations, point operators, image intensity transformation, basics of image filtering, morphological operations.
Image segmentation: line and edge detection, thresholding methods, watershed method, centroid method and active contour method, neural networks.
Feature extraction: contour and region descriptors, corner and center detection, SIFT descriptors, neural networks for descriptor generation
Object recognition and classification: image classification methods, pattern classification by matching prototypes, using artificial neural networks to detect and classify objects in images

Teaching methods

conventional lecture, exercises, project

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

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

Notes

Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 20-07-2021 10:40)

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