GIS-E3020 - Digital Image Processing and Feature Extraction D, Lecture, 9.1.2024-20.2.2024

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

To understand principles of sampling and reconstruction of a signal. To understand and apply Fourier transformation of images. To understand and apply linear space-invariant systems with images. To understand and apply basic digital image processing tasks, such as image restoration, image enhancement, image compression, and image correlation. To understand and apply how edges and interesting points can be extracted from images. To understand segmentation of digital images and the possibilities to utilize neural networks.

Credits: 5

Schedule: 09.01.2024 - 20.02.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Petri Rönnholm

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: Finnish, Swedish, English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    The course gives fundamentals of signal processing focusing on digital images. Mathematical principles of image enhancing and restoration are given. The course also illustrates how areas, breaklines and interesting points, such as corners and centers of circles, can be extracted automatically from images.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Examination and assignments

Workload

  • valid for whole curriculum period:

    Lectures, assignments, self-study, preparation for examination + examination

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring III
    2023-2024 Spring III

    Enrollment :

    Registration for the courses via Sisu (sisu.aalto.fi).

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Aalto University Code of Academic Integrity and Handling Violations Thereof