1,721,307 research outputs found
Reducing the number of implants in the prosthetic rehabilitation of edentulous patients: Presentation of two cases
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MULTIDISCIPLINARY APPROACH IN THE MANAGEMENT OF COMPLEX CASES: IMPLANT-PROSTHETIC REHABILITATION OF A PERIODONTAL SMOKING PATIENT WITH PARTIAL EDENTULISM, MALOCCLUSION AND AESTHETIC PROBLEMS
No full textThe aim of this case report is to describe how the multidisciplinary approach is the best way to resolve the cases of complex prosthetic rehabilitation. In this report we described how to solve with fixed prostheses a smoker patient who presents active periodontitis, multiple edentulous areas, dental malocclusion (anterior open bite, tooth extrusion and rotation), and severe aesthetic problems (multiple diastema, tooth discoloration, gingival recession). The whole programming of this specific clinical case was performed in a team including a periodontist, an oral implant surgeon, an orthodontist and a prosthodontist: All together specialists have carefully examined the list of the problems and after consideration of the potential rehabilitation programs, together drew up the best suited for the patient
Synergistic change detection and tracking
No full textVisual tracking in image streams acquired by static cameras is usually based on change detection and recursive Bayesian estimation, such an approach laying at the core of many practical applications. Yet, the interaction between the change detector and the Bayesian filter is typically designed heuristically. Differently, this paper develops a sound framework to model and implement a bidirectional communication flow between the two processes. In our Bayesian loop, change detection provides well-defined observation likelihood to the recursive filter and the filter prediction provides an informative prior to the change detector, which deploys Bayesian reasoning alike. The loop is developed for the two major variants of Bayesian filters used in tracking, namely the Kalman filter and the particle filter. Experiments on publicly available videos and a novel challenging data set show that the proposed interaction scheme outperforms several state-of-the-art trackers
Keypoint detection by wave propagation
Full text linkWe propose to rely on the wave equation for the detection of repeatable keypoints invariant up to image scale and rotation and robust to viewpoint variations, blur, and lighting changes. The algorithm exploits the properties of local spatial–temporal extrema of the evolution of image intensities under the wave propagation to highlight salient symmetries at different scales. Although the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to poorly textured objects, which are hardly dealt with by current pipelines based on local invariant features. The impact on the overall algorithm of different numerical wave simulation schemes and their parameters is discussed, and a pyramidal approximation to speed-up the simulation is proposed and validated. Experiments on publicly available datasets show that the proposed algorithm offers state-of-the-art repeatability on a broad set of different images while detecting regions that can be distinctively described and
robustly matched
Constrained TVp - l2 Model for Image Restoration
No full textThe popular total variation (TV) model for image restoration (Rudin et al. in Phys D 60(1–4):259-268, 1992) can be formulated as a Maximum A Posteriori estimator which uses a half-Laplacian image-independent prior favoring sparse image gradients. We propose a generalization of the TV prior, referred to as TVp, based on a half-generalized Gaussian distribution with shape parameter p. An automatic estimation of p is introduced so that the prior better fits the real images’ gradient distribution; we will show that, in general, the estimated p value does not necessarily require to be close to zero. The restored image is computed by using an alternating directions methods of multipliers procedure. In this context, a novel result in multivariate proximal calculus is presented which allows for the efficient solution of the proposed model. Numerical examples show that the proposed approach is particularly efficient and well suited for images characterized by a wide range of gradient distributions.The popular total variation (TV) model for image restoration (Rudin et al. in Phys D 60(1–4):259-268, 1992) can be formulated as a Maximum A Posteriori estimator which uses a half-Laplacian image-independent prior favoring sparse image gradients. We propose a generalization of the TV prior, referred to as TVp, based on a half-generalized Gaussian distribution with shape parameter p. An automatic estimation of p is introduced so that the prior better fits the real images’ gradient distribution; we will show that, in general, the estimated p value does not necessarily require to be close to zero. The restored image is computed by using an alternating directions methods of multipliers procedure. In this context, a novel result in multivariate proximal calculus is presented which allows for the efficient solution of the proposed model. Numerical examples show that the proposed approach is particularly efficient and well suited for images characterized by a wide range of gradient distributions
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