Prerequisites. Fundamentals of math (logarithms, exponents, derivate, integral, series, etc.) are important prerequisites.
Teaching Methods. The course is organised in theoretical lessons, in exercise and lab sessions.
Verification of Learning. The comprehension is verified by means of different intermediate assignments delivered through the e-learning platform of the University of Udine. Such assignments are composed by question to verify the theoretical comprehension and by coding different artificial vision algorithm to solve case study problems. The final verification consists in a project the requires capacities of analysis and judgement in relation to real artificial vision problems. The project require the production of a technical report that must be orally presented to the teacher for the evaluation of the the capabilities of comprehension and application of the knowledge preseted during the course.For the normal exam sessions the verification will be done by means of a writte exam containg questions of theory and exercises.
More Information. The lectures will be recorded and automatically published on the e-learning page of the course. On such a page the registered student will access the video from remote.
The goal of the course is to introduce the student to the fundamentals of the image processing and its evolution to digital video processing. The educational goal is to make the student autonomous with respect to the choice of the image processing algorithm for the extraction of the data useful to describe the content and eventually to modify it. The course will be based by a theoretical section in which classical image processing problems will be analysed and different lab. sessions when methodologies will be applied by using an high level programming language.The student will have to:know the fundamental concepts and algorithms of image and digital video processing and be able to understand the technological innovations that can refer to base algorithms.Know to process and transform a digital image. Know to use MATLAB programming language.Knot to analyse an artificial vision problem and propose a possible solution. Knowledge and comprehensionAcquire specific knowledge of the principal concepts and theoretical basics of the image processing and artificial vision. Know how to use the MATLAB language in order to implement artificial vision algorithms.Capability to apply knowledge and comprehensionKnow to analyse and to understan an image processing algorithm. Know to analyse and interpret an artificial vision problem and to apply the aforementioned knowledge to scompose it in supbroblems. Design the logic architecture of an artificial vision system for real problems. Autonomy of judgementKnow how to evaluate the artificail vision algorithm and make a personal choice about the most proper algorithm for solving a given problem. Know to distinguish among different artificial vision solution by evaluating their performance. Abilità comunicativeKnow how to explain, both written and orally, the techniques related to algorithm and systems of artificial vision with proper logic and terminology. Capacità di apprendimentoKnow how to retrieve and use bibliographic and digital instruments useful for the personal investigation of problems related image processing and artificial vision.
The course will present the fundamentals of the image and video processing techniques. The course will introduce the low level digital techniques for the image processing (histogram operations, linear filters, rank filters, etc.). The logical architecture of a system for the analysis of video sequences will be presented by focusing on the temporal redundancy for the extraction of data of interest. The course will introduce the problems related to the calibration and to the usage of the resource in a smart camera network environment. The course will include exercise sessions as weel lab. ones when specific algorithms will be developed by analyzing main features and limits.
 R.C. Gonzales, R.E. Woods, Elaborazione delle immagini digitali, Prentice Hall, 2008 Richard Szeliski, Computer Vision: Algorithms and Applications, 2008 D. Marini, M. Bertolo, A. Rizzi, Comunicazione Visiva Digitale, Addison-Wesley, 2002. A. Watt, F. Policarpo, The Computer Image, Addison-Wesley, 1998. R. Klette, and P. Zamperoni, Handbook of Image Processing Operators, Wiley, 1996. Notes delivered by the teacher during classes.
Università degli Studi di Udine Dipartimento di Scienze Matematiche, Informatiche e Fisiche (DMIF) via delle Scienze 206, 33100 Udine, Italy Tel: +39 0432 558400 Fax: +39 0432 558499 PEC: email@example.com p.iva 01071600306 | c.f. 80014550307
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