69661 - Image Processing And Computer Vision M

Course Unit Page


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

Quality education

Academic Year 2021/2022

Learning outcomes

The course aims to introduce basic knowledge about algorithms, tools and systems for the management, processing and analysis of digital images. The main topics of the course are filtering aspects of digital images, algorithms for image processing, algorithms for segmentation and classification of objects in digital images. Theoretical aspects that are introduced in the course are then applied to the design and manufacturing capabilities of simple systems oriented to real world applications. At the end of the course students are able to master basic digital image processing techniques and know potentials of this technology in applicative research and industrial contexts.

Course contents

  1. Introduction – Basic definitions related to image processing and computer vision. An overview across major application domains.
  2. Image Formation and Acquisition – Geometry of image formation. Pinhole camera and perspective projection. Geometry of stereopsis. Using lenses. Field of view and depth of field. Projective coordinates and perspective projection matrix. Camera calibration: intrinsic and extrinsic parameters, lens distortion. Camera calibration based on planar targets and homography estimation (Zhang's algorithm). Image rectification and stereo calibration. Basic notions on image sensing, sampling and quantization.  
  3. Intensity Transformations -  Image Histogram, Linear and non-linear contrast stretching. Histogram equalization.
  4. Spatial Filtering – Linear Shift-invariant operators. Convolution and correlation. Mean and Gaussian filtering.  Median Filtering. Bilateral filtering. Non-local means.
  5. Image Segmentation – Binarization by global thresholding. Automatic threshold estimation. Spatially adaptive binarization.  Colour-based segmentation.
  6. Binary Morphology – Dilation and erosion. Opening and closing- Hit-and-Miss.
  7. Blob Analysis – Distances on the image plane and connectivity. Labeling of connected components. Basic descriptors: area, perimeter, compactness, circularity. Orientation and bounding-box,  form factor and related descriptors. Euler number. Image moments, invariant moments.
  8. Edge Detection -  Image gradient. Smooth derivatives: Prewitt, Sobel, Frei-Chen. Non-maxima suppression. Laplacian of Gaussion. Canny edge detector.
  9. Local Invariant Features – Detectors and descriptors.  Harris Corners.  Scale invariant features. SIFT features. Efficient feature matching by kd-trees. 
  10. Object Detection – Pattern matching by SSD, SAD, NCC and ZNCC. Fast pattern matching. Shape-based mathing. Hough Transform for analystic shapes, Generalized Hough Transform.  Object detection by local invariant features: Hough-based voting, least-squares similarity estimation. 

Theory is complemented by assisted hands-on lab sessions based on Python and the OpenCV library. Lab sessions cover  selected topics such as intensity transformations, spatial filtering, camera calibration, motion estimation and local invariant features.


  • Gonzales R., Woods R. : “Digital Image Processing”, Third Edition, Pearson Prentice-Hall, 2002.
  • D.A. Forsyth, J. Ponce:  “Computer Vision   A Modern Approach”. Pearson Prentice-Hall, 2003.

Teaching methods

Theory taught in lectures is complemented by assisted hands-on lab sessions covering selected topics. Students are provided with the software tools, image/video archives and support that enable practical implementation and testing of most of the topics discussed in classes, so as to deepen significantly their understanding of the course subject matter.

Assessment methods

Students are required to carry out and present a software project related to solving a real-world image processing or computer vision problem. Such a project can be either chosen among a list provided by the teacher through the course web-site or proposed by the student.

The, the exam is oral and comprises both project discussion as well as assessment of theoretical knowledge.

Teaching tools

Available on the course website are:

  • All slides related to lectures and lab sessions.
  • A Software development framework based on OpenCV which allows students to practically implement the algorithms and methods taught in lectures.
  • Images and videos allowing students to easily test their implementations.
  • Links toimage processing and computer vision resources freely available on the web (such as software tools and image/video archives).

Links to further information


Office hours

See the website of Luigi Di Stefano