Mean value coordinates–based caricature and expression synthesis

We present a novel method for caricature synthesis based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face pair for frontal and 3D caricature synthesis. This technique only requires one or a small number of exemplar pairs and a natural frontal face image training set, while the system can transfer the style of the exemplar pair across individuals. Further exaggeration can be fulfilled in a controllable way. Our method is further applied to facial expression transfer, interpolation, and exaggeration, which are applications of expression editing. Additionally, we have extended our approach to 3D caricature synthesis based on the 3D version of MVC. With experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized

Document Collection Visualization and Clustering Using An Atom Metaphor for Display and Interaction

Visual Data Mining have proven to be of high value in exploratory data analysis and data mining because it provides an intuitive feedback on data analysis and support decision-making activities. Several visualization techniques have been developed for cluster discovery such as Grand Tour, HD-Eye, Star Coordinates, etc. They are very useful tool which are visualized in 2D or 3D; however, they have not simple for users who are not trained. This thesis proposes a new approach to build a 3D clustering visualization system for document clustering by using k-mean algorithm. A cluster will be represented by a neutron (centroid) and electrons (documents) which will keep a distance with neutron by force. Our approach employs quantified domain knowledge and explorative observation as prediction to map high dimensional data onto 3D space for revealing the relationship among documents. User can perform an intuitive visual assessment of the consistency of the cluster structure

Validation of the automatic tracking for facial landmarks in 3D motion captured images

Aim: The aim of this study was to validate the automatic tracking of facial landmarks in 3D image sequences captured using the Di4D system (Dimensional Imaging Ltd., Glasgow, UK). MATERIALS AND METHODS: 32 subjects (16 males; 16 females) range 18-35 years were recruited. 23 facial landmarks were marked on the face of each subject with a 0.5 mm non-permanent ink. The subjects were asked to perform three facial animations from the rest position (maximal smile, lip purse and cheek puff). Each animation was captured by a 3D stereophotogrammetry video system (Di4D). A single operator digitized landmarks on captured 3D models and the manual digitised landmarks were compared with the automatic tracked landmarks. To investigate the accuracy of manual digitisation, the same operator re-digitized 2 subjects (1 male and 1 female). RESULTS & CONCLUSION: The discrepancies in x, y and z coordinates between the manual digitised landmarks and the automatic tracked facial landmarks were within 0.5 mm and the mean distance between the manual digitisation and the automatic tracking of corresponding landmarks using tracking software was within 0.7 mm which reflects the accuracy of the method( p value was very small). The majority of these distances were within 1 mm. The correlation coefficient between the manual and the automatic tracking of facial landmarks was 0.999 in all x, y, and z coordinates. In conclusion, Automatic tracking of facial landmarks with satisfactory accuracy, would facilitate the analysis of the dynamic motion during facial animations