Accessing movies based on emotional impact

Emotions play a central role in our daily lives, influencing the way we think and act, our health and sense of well-being, and films are by excellence the form of art that exploits our affective, perceptual and intellectual activity, holding the potential for a significant impact. Video is becoming a dominant and pervasive medium, and online video a growing entertainment activity on the web and iTV, mainly due to technological developments and the trends for media convergence. In addition, the improvement of new techniques for gathering emotional information about videos, both through content analysis or user implicit feedback through user physiological signals complemented in manual labeling from users, is revealing new ways for exploring emotional information in videos, films or TV series, and brings out new perspectives to enrich and personalize video access. In this work, we reflect on the power that emotions have in our lives, on the emotional impact of movies, and on how to address this emotional dimension in the way we classify and access movies, by exploring and evaluating the design of iFelt in its different ways to classify, access, browse and visualize movies based on their emotional impac

Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant’s pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant’s pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant’s main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant’s pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67±34μm and 108μm, and angular misfits of 0.15±0.08º and 1.4º, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants’ pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.