Umberto Castellani
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Department of Computer Science
- Qualification
- Assistant Professor
- Disciplinary sector
- INF/01 - Computing
- Office
- Ca' Vignal 2, Floor 1, Room 47
- Telephone
- +39 045 802 7988
- Fax
- +39 045 802 7068
- umberto
castellani
univr
it
- Personal web page
- http://profs.sci.univr.it/~castella
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Il ricevimento puo' essere svolto anche in altri momenti anche presso la facolta' di lettere ma su appuntamento (scrivere a: umberto.castellani@univr.it) |
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|---|---|---|
| Place | day | Timetable |
| Ca' Vignal 2, floor 1, room 47 | Tuesday | 11:00 AM - 1:00 PM |
| Name | Description |
|---|---|
| Visione ed elaborazione delle immagini e suoni (VIPS - Vision, Image Processing & Sound) | Computer Vision and Pattern Recognition, Image Processing, and Sound Analysis & Processing |
| Topic | Description | Research area |
|---|---|---|
| Competenza da inserire 1 | Da inserire |
Machine intelligence
Computing methodologies - Artificial intelligence |
| Competenza da inserire 1 | da inserire |
Machine intelligence
Computing methodologies - Machine learning |
| Computer graphics | Shape analysis: development of local and global shape descriptors, extraction of curve skeletons from 3D meshes, mesh processing tools. Applications to human body analysis from whole body scanner data. Interactive visualization using advanced displays. |
Machine intelligence
Computing methodologies - Computer graphics |
| Image processing and computer vision | Basic image processing, e.g. image enhancement, interpolation. Image processing applied to medical images, 2D and 3D segmentation, feature extraction, texture analysis. Optical flow extraction and processing. | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |
| Pattern Recognition | The main focus is on the study and development of automatic techniques and models able to extract information from real world data, typically in terms of classes or clusters. Special attention is on probabilistic models - like Hidden Markov Models, Mixtures, Topic Models - and on kernel machines - like Support Vector Machines. In these contexts the interest is in designing novel models/methodologies, like hybrid generative-discriminative methods, generative embeddings and kernels, novel classification or clustering schemes, model selection techniques and others. The focus is on reasoning on representation issues (how to extract features, how to process the original problem space) as well as on unconventional employment of standard techniques (like boosting or SVM for clustering). Another field of interest is the processing of sequential data (using for example Hidden Markov Model). |
Machine intelligence
Computing methodologies - Machine learning |
| Department/Faculty | Name | Total credits | Online | Teacher credits | Modules offered by this teacher |
|---|---|---|---|---|---|
| Department Computer Science | Computer Vision (2012/2013) | 6 | 6 | ||
| Department Philology, Literature, and Linguistics | Informatica e produzione multimediale (m) (2012/2013) | 12 | 6 | I MODULO PARTE (I) | 6 | II MODULO PARTE (P) |
| Department Philology, Literature, and Linguistics | Informatica e produzione multimediale (m) (2011/2012) | 12 | 6 | I MODULO PARTE (I) | 6 | II MODULO PARTE (P) |
| Name | Online |
|---|---|
| Insegnamento “Introduzione a un ambiente per il calcolo scientifico" (22° ciclo - Doctoral Program in Education and Lifelong Learning Science) | |
| Insegnamento “Introduzione a un ambiente per il calcolo scientifico" (22° ciclo - PhD Program in Applied Biotechnologies) |