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88003 - Image Processing

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SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being

Academic Year 2021/2022

Learning outcomes

At the end of the course the student will be able to: - describe the basic issues and the principal applications of image processing; - demonstrate a good understanding of the current state-of-the-art image processing methods; - identify, demonstrate and apply his/her knowledge by analyzing image processing problems and recognizing and employing (or proposing) effective solutions; - design and create practical solutions to a range of common image processing problems. The student will also get the basic knowledge on the main algorithms for: - filtering in the spatial and frequency domain; - image segmentation; - object detection and recognition.

Course contents

- Visual perception

- Image formation: geometry and radiometry

- From analog to digital images

- Digital image chain: acquisition, visualization, and processing

- Image processing:

  • Geometric operations
  • Point operations
  • Local operations
  • Image segmentation
  • Fourier analysis

- Image segmentation

- Feature extraction

- Lab sessions with ImageJ

Readings/Bibliography

–Gonzalez R., Woods R.: “Digital Image Processing”, Third Edition, Pearson Prentice-Hall, 2002

 

Teaching methods

Lectures, lab practice.

Assessment methods

In the last weeks of the classe students will tackle a real-word problem (compulsory):

  • Group project (small groups: 4/5 people)
  • Reading scientific paper
  • Finding material (bibliography, images, …)
  • Understanding and implementation of some algorithms
  • Presentation (both written report and oral presentation) of the project and its outcomes

Final examination: discussion of the group project and oral interview.

Teaching tools

Software for image processing: ImageJ.

Office hours

See the website of Nico Lanconelli