01123nam a2200253 i 450099100387545970753620020509133627.0970318s1995 it ||| | ita 8814052344b11227825-39ule_instPARLA190363ExLDip.to Scienze dell'AntichitàitaCardilli, Riccardo235892L'obbligazione di 'Praestare' e la responsabilità contrattuale in diritto romano :2. sec. a.C.-2. sec. d.C. /Riccardo CardilliMilano :Giuffrè,1995XIV, 524 p. ;24 cm.Seconda Università degli studi di Roma. Pubblicazioni della Facoltà di Giurisprudenza. Sezione di storia e teoria del diritto ;1Diritto romano.b1122782523-02-1701-07-02991003875459707536LE015 340 - 271LE015N-6971le007-E0.00-l- 00000.i1138281801-07-02Obbligazione di 'Praestare' e la responsabilità contrattuale in diritto romano867853UNISALENTOle00701-01-97ma -itait 2102336oam 2200409zu 450 991014100140332120241212220051.01-4244-8834-6(CKB)2670000000082935(SSID)ssj0000668748(PQKBManifestationID)12278218(PQKBTitleCode)TC0000668748(PQKBWorkID)10700406(PQKB)11308052(NjHacI)992670000000082935(EXLCZ)99267000000008293520160829d2010 uy engur|||||||||||txtccr2010 IEEE Applied Imagery Pattern Recognition Workshop[Place of publication not identified]IEEE20101 online resource illustrationsBibliographic Level Mode of Issuance: Monograph1-4244-8833-8 Activity recognition has been applied to many varied applications ranging from surveillance to medical analysis. Interpreting human actions is often a complex problem for computer vision. Actions can be classified through shape, motion or region based algorithms. While all have their distinct advantages, we consider a feature extraction approach using convexity defects. This algorithmic approach offers a unique method for identifying actions by extracting features from hull convexity defects. Specifically, we create a hull around the segmented silhouette of interest in which the regions that exist in the hull are recognized. A feature database is created through a dataset of features for multiple individuals. These feature points are registered between progressive frames and then normalized for analysis. Using Principal Component Analysis (PCA), the feature points are classified to different poses. From there testing and training is performed to observe the classification into major human activities. This approach offers a robust and accurate method to identify actions and is invariant to size and human shape.Optical pattern recognitionCongressesOptical pattern recognition006.42IEEE StaffPQKBPROCEEDING99101410014033212010 IEEE Applied Imagery Pattern Recognition Workshop2496190UNINA