Course program Week 1 Section Introduction to recognition and indexing of visual data
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[C] Christopher Bishop, Pattern Recognition and Machine Learning, Springer 2006
 for fundamentals of pattern recognition
Course short description This course addresses Multimedia Recognition and Indexing, reviewing recent advances in Computer Vision, Pattern Recognition and Multimedia Retrieval. Includes image, video and 3D media content description and matching for the purpose of recognition and classification. Covers matching in small and large scale datasets and over the Internet, with advanced algorithms and recent solutions. Most of the course content includes solid scientific results and achievements from 2004 to this point Instructor Office Hours: working days 09-11, Dipartimento Sistemi e Informatica S. Marta 3 (week of instruction) Prof. Alberto del Bimbo http://www.micc.unifi.it/delbimbo/ Tutors Office Hours: working days 10-13, MICC Media Integration and Communication Center, Viale Morgagni 65 Marco Bertini Andy Bagdanov Lorenzo Seidenari Lamberto Ballan Giuseppe Lisanti Giuseppe Serra Credits 9 Class Schedule Frontal lessons: Facoltà Ingegneria, Via S. Marta 3, Room 205-206 Tuesday 2 - 5 pm Thursday 8 – 11 am Laboratory: MICC Media Integration and Communication Center, Viale Morgagni 65, Basement Tuesday 2 - 5 pm Thursday 8 – 11 am and other weekdays at student’s wishes Modalities Class participation (optional); Laboratories (mandatory); Final project development (mandatory); Review/presentation (mandatory). Class partecipation includes attending frontal lessons by the instructor Laboratory includes development of exercise work (Laboratory exercises are held at MICC or under request at your home under tutor supervision) Final project; the following options are available: small-scale (approx 1 man-month) for the Course exam only medium scale (approx 3-4 man-months) for the Course exam and the Master Thesis Final projects are held at MICC, or at industry companies that cooperate with MICC, and developed under tutor supervision (cooperating companies year 2011: Thales Italia SpA, Selex Communications SpA, Magenta SrL) Exam Grading 60% class participation and laboratories, 20% final project, 20% Review/presentation Prerequisites Students are expected to have basic familiarity with background in image analysis and pattern recognition. Programming skills in Matlab or C, C++ language are highly useful. Download 304.14 Kb. Share with your friends:
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