CAD, CAE and rapid prototyping methods applied in long bones orthopaedics

U radu su prikazane metode za analizu ljudskih koštanih zglobova. Prvo, upotrebom CT slika, definisani su 'čvrsti' delovi kao glavne komponente kosti i 'meki' delovi kao što su ligamenti ili meniskusi. Ove komponente uvoze se u modul za montažu parametrizovanog okruženja i dobija se biomehanički model ljudskog hoda, koji se izvozi u kinematsko simulaciono okruženje i koristi za analizu konačnim elementima, gde se prvo definišu kinematski parametri. Sa ovako definisanim parametrima može se izvršiti zamena kinematskih i dinamičkih simulacija podsistema klasičnim, normalnim kretanjem. Nakon interpretacije rezultata, mogu se modifikovati početni parametri biomehaničkih podsistema. U sledećoj fazi, komponente podsistema su podeljene sukcesivno i dobijena je struktura konačnih elemenata za ceo biomehanički sistem spojeva koji učestvuju u ljudskoj lokomociji.The paper presents some methods used to analyze human bone joints. First, there were defined the 'hard' parts as the main bone components and 'soft' parts as ligaments or menisci using CT images. These components are imported into a parameterized environment assembly module and a biomechanical model of human walking is being obtained, which is exported to a kinematic simulation environment and finite element analysis, where first the kinematic parameters are defined. With these defined parameters, the kinematic and dynamic simulation of the subsystems for classical, normal motion can be switched. Following the interpretation of the results, the initial parameters of the biomechanical subsystems may be modified. In the next phase, the components of the subsystems are divided successively and the finite element structure is obtained for the entire biomechanical system of the joints that participate in human locomotion

CAD, CAE and rapid prototyping methods applied in long bones orthopaedics

U radu su prikazane metode za analizu ljudskih koštanih zglobova. Prvo, upotrebom CT slika, definisani su 'čvrsti' delovi kao glavne komponente kosti i 'meki' delovi kao što su ligamenti ili meniskusi. Ove komponente uvoze se u modul za montažu parametrizovanog okruženja i dobija se biomehanički model ljudskog hoda, koji se izvozi u kinematsko simulaciono okruženje i koristi za analizu konačnim elementima, gde se prvo definišu kinematski parametri. Sa ovako definisanim parametrima može se izvršiti zamena kinematskih i dinamičkih simulacija podsistema klasičnim, normalnim kretanjem. Nakon interpretacije rezultata, mogu se modifikovati početni parametri biomehaničkih podsistema. U sledećoj fazi, komponente podsistema su podeljene sukcesivno i dobijena je struktura konačnih elemenata za ceo biomehanički sistem spojeva koji učestvuju u ljudskoj lokomociji.The paper presents some methods used to analyze human bone joints. First, there were defined the 'hard' parts as the main bone components and 'soft' parts as ligaments or menisci using CT images. These components are imported into a parameterized environment assembly module and a biomechanical model of human walking is being obtained, which is exported to a kinematic simulation environment and finite element analysis, where first the kinematic parameters are defined. With these defined parameters, the kinematic and dynamic simulation of the subsystems for classical, normal motion can be switched. Following the interpretation of the results, the initial parameters of the biomechanical subsystems may be modified. In the next phase, the components of the subsystems are divided successively and the finite element structure is obtained for the entire biomechanical system of the joints that participate in human locomotion

A facial expression recognition with automatic segmentation of face regions

This paper proposes a facial expression recognition algorithm, which automatically detects and segments the face regions of interest (ROI) such as the forehead, eyes and mouth, etc. Proposed scheme initially detects the image face and segments it in two regions: forehead/eyes and mouth. Next each of these regions is segmented into N × M blocks which are characterized using 54 Gabor functions that are correlated with each one of the N × M blocks. Next the principal component analysis (PCA) is used for dimensionality reduction. Finally, the resulting feature vectors are inserted in a proposed classifier based on clustering techniques which provides recognition results closed to those provided by the support vector machine (SVM) with much less computational complexity. The experimental results show that proposed system provides a recognition rate of about 98% when only one ROI is used. This recognition rate increases to about 99% when the feature vectors of all ROIs are concatenated. This fact allows achieving recognition rates higher than 97%, even when one of the two ROI are totally occluded