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https://olympias.lib.uoi.gr/jspui/handle/123456789/10809Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Loutas, E. | en |
| dc.contributor.author | Pitas, I. | en |
| dc.contributor.author | Nikou, C. | en |
| dc.date.accessioned | 2015-11-24T17:00:45Z | - |
| dc.date.available | 2015-11-24T17:00:45Z | - |
| dc.identifier.issn | 1051-8215 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10809 | - |
| dc.rights | Default Licence | - |
| dc.subject | Gaussian processes | en |
| dc.subject | face recognition | en |
| dc.subject | image sequences | en |
| dc.subject | object detection | en |
| dc.subject | optical tracking | en |
| dc.subject | parameter estimation | en |
| dc.subject | probability | en |
| dc.subject | Gaussian temporal model | en |
| dc.subject | entropy measures | en |
| dc.subject | feature point sets | en |
| dc.subject | illumination changes | en |
| dc.subject | likelihood estimation | en |
| dc.subject | multiple face detection | en |
| dc.subject | multiple face tracking | en |
| dc.subject | partial occlusion | en |
| dc.subject | prior probability estimation | en |
| dc.subject | probabilistic face detection | en |
| dc.subject | probabilistic face tracking | en |
| dc.subject | statistical training | en |
| dc.title | Probabilistic multiple face detection and tracking using entropy measures | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | 10.1109/tcsvt.2003.819178 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2004 | - |
| heal.abstract | A joint probabilistic face detection and tracking algorithm, combining likelihood estimation and a prior probability, is proposed. The likelihood estimation scheme is based on the statistical training of sets of automatically generated feature points and a mutual information tracking cue, while the prior probability estimation is based on a Gaussian temporal model. The likelihood estimation process is the core of a multiple face detection scheme used to initialize the tracking process. The resulting system has been tested on real image sequences and is robust to significant partial occlusion and illumination changes. | en |
| heal.journalName | Circuits and Systems for Video Technology, IEEE Transactions on | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| nikou-2004-Probabilistic Multiple Face Detection and Tracking Using Entropy Measures.pdf | 477.59 kB | Adobe PDF | View/Open Request a copy |
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