60/73/3 - IMAGE PROCESSING
Academic Year 2022/2023
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CECILIA DI RUBERTO (Tit.)
- Teaching style
- Lingua Insegnamento
|[60/73] INFORMATICS||[73/00 - Ord. 2017] PERCORSO COMUNE||9||72|
The course aims to examine theories and techniques directed to the acquisition, processing, analysis and understanding of the content of images. There are many applications including surveillance, remote sensing, analysis of documents, biometrics, astronomy, cultural heritage, medicine, bioinformatics, data mining, database of images.
The course explains the fundamental techniques and their use in some major practical applications.
Expected learning outcomes
Knowledge and understanding
The student will acquire the fundamental knowledge related to:
- Acquisition and representation of an image
- Techniques for the improvement of images suffering from various types of noise or lack of contrast
- Frequency analysis of images
- Techniques for segmentation and feature extraction of an image
- The mathematical morphology operators.
Applying knowledge and understanding
Through this knowledge and numerous application examples, the student will be given the opportunity to design a system for processing and analyzing an image by computer.
The student will be able to apply the typical methods of image processing and analysis for the understanding and resolution of new computational problems about the acquisition, processing, analysis and understanding of the content of images. Criticism classroom discussions and laboratory exercises will serve to stimulate and develop making judgements.
The student will acquire the ability to express the fundamental concepts of image processing and analysis with appropriate and rigorous terminology. He will learn to describe problems concerning the processing and the analysis of images and design methods adopted for their solution.
The student will acquire the ability to study and learn new methods and techniques for image processing and analysis in order to independently develop solutions to new problems concerning the treatment of images.
Elements of Mathematical analysis
Linear algebra (vectors and matrices)
Knowledge of data structures
Basics of Probability
Good programming skills in Matlab.
The Digitized Image and its Properties (4 h)
Data Structures for Image Analysis (2 h)
Image Pre-processing (10 h)
Linear Discrete Image Transforms (6 h)
Mathematical Morphology (8 h)
Segmentation (4 h)
Shape Representation and Description (4 h)
Case studies (14 h)
In addition to theoretical lessons, practical exercises in the laboratory and supported by the teacher are provided where the described algorithms will be implemented in MATLAB environment to solve real cases (20 h).
Teaching will be held in the presence. The lessons can be integrated with audiovisual materials and streaming.
Verification of learning
The exam consists of three tests: written, oral and practical. Each test is evaluated in thirtieth and is deemed sufficient if its rating is at least 18.
The written test consists of multiple choice exercises / questions on the theoretical topics and algorithms described in class and lasts approximately 1 hour.
The oral exam, normally following the written test, is composed by further questions on the course program which, at the discretion of the student, may be replaced by an oral relation to deepen a particular study case. The oral test is aimed to ensure: the level of knowledge of the theoretical course content (Dublin descriptor 1), the level of expertise in exposing their argumentation skills (Dublin descriptor 2), their independent judgment (Dublin descriptor 3) to propose the most appropriate approach to argue what is required. The oral examination also aims to verify the student's ability to respond to the proposed questions with properties of language, to support a dialectical relationship during the debate and to show logical-deductive ability and summary exposition (Dublin descriptor 4).
The practical test concludes the exam, if previous tests passed.
This test requires an individual work to be carried out in order to solve a practical problem of image processing and analysis, taking advantage of the knowledge acquired during laboratory exercises, in MATLAB environment. This test is aimed at ascertaining the student's ability to apply knowledge in solving real problems by designing an automatic image processing and analysis system (Dublin descriptor 2).
If passed, it will contribute to define the final grade, given by the average of the marks obtained in the three tests.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Publishing Company, (3rd ed 2007) (4th ed 2018)
M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, PWS, (2nd ed) 1998.
Slides of the lectures and other materials are available on the Moodle elearning platform.