The Digital Signal and Image Processing course aims at providing the basic tools for analyzing and processing 1D and 2D signals over time, space and frequency.

The course will consist in classes and guided labs.

The course has a strong applicative connotation. In addition to the labs, the student will work on a project that will require a great deal of autonomy to solve complex problems.

  • Aims and content
    • LEARNING OUTCOMES
      Students will learn basic tools for analysing 1-D and 2-D signals in the space and in the frequency domains. Particular attention will be devoted to filters, to deal with noise attenuation and feature enhancement. Dynamic filters will also be considered. The course will also cover low level vision topics, including image feature extraction, image segmentation,image registration, and image matching. Students will be involved in project activities.
      SYLLABUS/CONTENT


      Syllabus
      Students will learn basic tools for analysing 1-D and 2-D signals in the
      space and in the frequency domains. Particular attention will be devoted
      to filters, to deal with noise attenuation and feature enhancement.
      Dynamic filters will also be considered. The course will also cover low
      level vision topics, including image feature extraction, image
      segmentation,image registration, and image matching.
      Students will be involved in project activities.
      CONTENT:
      Systems
      - Systems: Input/Output signal, Response of a System
      - Systems Properties: Linearity, Time-invariance, Causality
      1D signals
      - Complex numbers, Periodic Functions, Complex Functions,
      Trigonometric Polynomial
      - Fourier Series
      - Fourier Transform
      - Noise
      - Sampling, Sampling Theorem
      - Convolution Theorem
      - Filters
      - Kalman Filter
      - Wavelets
      2D signals
      - Greyscale Images
      - Color images
      - Histogram
      - 2d Fourier Transform
      - Spatial filters
      - Image features (corner, edge, ridge)
      - Image matching
      - Image similarity measures
       

      RECOMMENDED READING/BIBLIOGRAPHY

      Signals and Systems
      Oppenheim et al. - Signals and Systems
      Bertoni et al. - Introduzione all’elaborazione dei segnali (UniMi) (in Italian)

      Digital Signal Processing
      Orfanidis - Introduction to Signal Processing
      Oppenheim et al. - Discrete-Time Signal Processing

      Signal & Image processing
      Gonzales Woods - Digital Image Processing
      Mallat - A wavelet tour of signal processing

  • Who
  • How
    • TEACHING METHODS

      Class (44 hours), lab (12 hours), project (50 hrs) and outside preparation
      Students are required to attend class and lab for a total of 6 hrs/week

      EXAM DESCRIPTION

      The final evaluation will take into account: (1) class attendance, (2) planned homework, (3) project discussion (4) oral dissertation

      ASSESSMENT METHODS

      The project should be written clearly, complemented with working code and it should show that the student has fully understood the topic. Examples on different real scenarios are encouraged.

      The oral examination consists in a discussion of the project and of the topics taught in class. 

  • Where and when
    • OFFICE HOURS FOR STUDENTS
      Annalisa Barla

      Appointment by email

      Alessandro Verri

      Appointment by email

  • Contacts