ID:
510792
Durata (ore):
60
CFU:
6
SSD:
SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Anno:
2023
Dati Generali
Periodo di attività
Primo Semestre (02/10/2023 - 19/01/2024)
Syllabus
Obiettivi Formativi
The purpose of the course is to provide the advanced concepts about the
creation, storage and retrieval of digital multimedia data, by accessing to
collections of structured, semi-structured and unstructured data,
containing, in addition to text, still images, video and audio. At the end of the course the student will be able to understand the differences between the management of textual data and multimedia data, to identify which are the solved
problems and which are still open, with the analysis of algorithms and techniques
available today.
creation, storage and retrieval of digital multimedia data, by accessing to
collections of structured, semi-structured and unstructured data,
containing, in addition to text, still images, video and audio. At the end of the course the student will be able to understand the differences between the management of textual data and multimedia data, to identify which are the solved
problems and which are still open, with the analysis of algorithms and techniques
available today.
Prerequisiti
The course assumes the knowledge of the basic concepts of textual
retrieval and database management (relational database, DBMS, definitions and usage). Among the prerequisites there are also the knowledge of
SQL language, and of the fundamentals of programming.
retrieval and database management (relational database, DBMS, definitions and usage). Among the prerequisites there are also the knowledge of
SQL language, and of the fundamentals of programming.
Metodi didattici
The course is composed of 38 hour of theory lesson and 22 hours of laboratory.
The concepts are explained during the theory lessons by the support of
Powerpoint slides and reading and comments on research papers, and encouraging discussions among students. In the hours of laboratory the knowledge will be increased with
the aid of coding activities developed in Matlab; in this way the student can easily connect the theory to results in different fields of application, for example in image and audio processing.
Part of the laboratory hours will be developed by analyzing case studies and encouraging students to develop personal solutions, also in team working.
In its session of 23 March 2023, the Academic Senate approved the “Indications for innovative teaching a.y. 2023/2024” which include both a general project to support innovative teaching and inclusive methods in favor of particular categories of students (including working students, students with health problems or disabilities, athletes, .).
The students of these categories and all the students who decide not attend in presence can follow these inclusive educational modalities:
Inclusive modalities for not face-to-face attending students
Lessons and laboratory hours in class can be replaced by:
For theory lessons:
The study of the sources (book chapters, pdf file of papers, videos) listed for each topic of the course at this page http://csu.unipv.it/lucidimoda/
For the laboratory:
1) Dowload the educational license of Matlab following the instructions posted on csu.unipv.it website.
2) Implement an individual programming activity (in Matlab) of the "Exercises in Matlab" proposed in classroom to attending students.
This part can be integrated with the solutions of the most significant exercises proposed in the videolessons on KIRO platform. Videolessons will be available during all the period October 2 – December 13, 2023 (later removed).
For further information, office hours will be delivered on line on Zoom platform. To ask for a meeting on Zoom, please send an e-mail to the teacher.
The concepts are explained during the theory lessons by the support of
Powerpoint slides and reading and comments on research papers, and encouraging discussions among students. In the hours of laboratory the knowledge will be increased with
the aid of coding activities developed in Matlab; in this way the student can easily connect the theory to results in different fields of application, for example in image and audio processing.
Part of the laboratory hours will be developed by analyzing case studies and encouraging students to develop personal solutions, also in team working.
In its session of 23 March 2023, the Academic Senate approved the “Indications for innovative teaching a.y. 2023/2024” which include both a general project to support innovative teaching and inclusive methods in favor of particular categories of students (including working students, students with health problems or disabilities, athletes, .).
The students of these categories and all the students who decide not attend in presence can follow these inclusive educational modalities:
Inclusive modalities for not face-to-face attending students
Lessons and laboratory hours in class can be replaced by:
For theory lessons:
The study of the sources (book chapters, pdf file of papers, videos) listed for each topic of the course at this page http://csu.unipv.it/lucidimoda/
For the laboratory:
1) Dowload the educational license of Matlab following the instructions posted on csu.unipv.it website.
2) Implement an individual programming activity (in Matlab) of the "Exercises in Matlab" proposed in classroom to attending students.
This part can be integrated with the solutions of the most significant exercises proposed in the videolessons on KIRO platform. Videolessons will be available during all the period October 2 – December 13, 2023 (later removed).
For further information, office hours will be delivered on line on Zoom platform. To ask for a meeting on Zoom, please send an e-mail to the teacher.
Verifica Apprendimento
The 6 credits can be obtained by:
1) a written test on the entire program (theory lessons + laboratory)
2) Two projects developed during the first semester (RESERVED ONLY TO the students who attend in presence (face-to-face-attendance) STARTING AT LEAST FROM the end of the first month of the lessons). If the mark of the projects is not sufficient or is rejected, the student must pass the written test as at point 1.
The written test will be based on open/closed questions or exercises, whose number is calibrated to make all the tests homogenous in difficulties and complexity. In each test, the relative contribution of each question/exercise is clearly reported. The evaluation is in 30/30.
1) a written test on the entire program (theory lessons + laboratory)
2) Two projects developed during the first semester (RESERVED ONLY TO the students who attend in presence (face-to-face-attendance) STARTING AT LEAST FROM the end of the first month of the lessons). If the mark of the projects is not sufficient or is rejected, the student must pass the written test as at point 1.
The written test will be based on open/closed questions or exercises, whose number is calibrated to make all the tests homogenous in difficulties and complexity. In each test, the relative contribution of each question/exercise is clearly reported. The evaluation is in 30/30.
Testi
Gonzalez R., Woods R.. Digital Image processing, 3d edition Pearson, chapter 1, 3, 6, 7, 8.
H. R. Wu, K. R. Rao. Digital Video Image Quality and Perceptual Coding. Taylor and Francis, 2006.
H. Blanken, A. P. de Vries, H. E. Blok, L. Fengs: Multimedia Retrieval, Springer, 2007.
H. R. Wu, K. R. Rao. Digital Video Image Quality and Perceptual Coding. Taylor and Francis, 2006.
H. Blanken, A. P. de Vries, H. E. Blok, L. Fengs: Multimedia Retrieval, Springer, 2007.
Contenuti
Introduction to multimedia data: what does it mean in the modern media and its difference with the digital textual data. Collections of structured, semi-structured and unstructured data. Relationship between data, information and knowledge.
An outstanding class of digital data: the images. Taxonomy of digital images for the purpose of storage and retrieval. Image quality: subjective and objective metrics and computational algorithms.
The compression of digital data: techniques for compressing images. Wavelet Transform. Compression standards: JPEG and JPEG2000.
Collections of audio data: the semantic meaning of the audio data. Search by audio fingerprinting techniques. Digital music and MIDI format.
The research data in digital media. Types of search. The search by metadata. The indexing. The search for content in digital images: for shape, for color and texture.
The search for similarities: the approach of metric space. Distance measurements. Centralized indexes. Parallel Index (hints).
Convergence between search engines and databases: the Search Based Applications (SBA).
Problems in searching on Web (social media, SEO, project of Web Site for an efficient ranking in Google)
Case study: searching in audio: the Shazam software.
Case study: the digital data behind social media.
Case study: examples of search by shape and colour. Search and retrieval in biometrics (collections of fingerprints, irises, faces).
Case study: Data analysis and retrieval for environmental sustainability (biodiversity preservation, land use estination)
An outstanding class of digital data: the images. Taxonomy of digital images for the purpose of storage and retrieval. Image quality: subjective and objective metrics and computational algorithms.
The compression of digital data: techniques for compressing images. Wavelet Transform. Compression standards: JPEG and JPEG2000.
Collections of audio data: the semantic meaning of the audio data. Search by audio fingerprinting techniques. Digital music and MIDI format.
The research data in digital media. Types of search. The search by metadata. The indexing. The search for content in digital images: for shape, for color and texture.
The search for similarities: the approach of metric space. Distance measurements. Centralized indexes. Parallel Index (hints).
Convergence between search engines and databases: the Search Based Applications (SBA).
Problems in searching on Web (social media, SEO, project of Web Site for an efficient ranking in Google)
Case study: searching in audio: the Shazam software.
Case study: the digital data behind social media.
Case study: examples of search by shape and colour. Search and retrieval in biometrics (collections of fingerprints, irises, faces).
Case study: Data analysis and retrieval for environmental sustainability (biodiversity preservation, land use estination)
Lingua Insegnamento
INGLESE
Altre informazioni
The students can find at http://csu.unipv.it/didattica/
all details and further information for the study of the course
all details and further information for the study of the course
Corsi
Corsi
COMPUTER ENGINEERING
Laurea Magistrale
2 anni
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Persone
Persone
Docente
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