Role of Computer Technology in Changing Smile

In the ever changing complex society, where success has become a mantra for both young and old, facial appearance that includes smile, plays a pivotal role. Among many attractive components in a person’s face, smile reflects the persona of an individual. In the present day next gen age, science in unison with technology and techniques which are rapidly getting ingrained into day to day dental practice, has changed the perception of healthy smile by more effective and less invasive approach. In this scenario, the onus lies on the dentist to give that perfectionist touch to a customised smile using computer aided software and hardware apparatus, and to avail the best state of the art material, equipment and techniques. Hence, it is imperative for us to understand and inculcate the role of modern cutting edge computer-aided technologies used in designing and changing the smile of an individual

Optical Impression in Restorative Dentistry

Intraoral scanners are responsible for data acquisition in digital workflow, which represents the first step in restorative dentistry. The present chapter aimed to investigate the various methods for acquiring oral information, diverse clinical applications based on optical impression technique, use of intraoral scan data according to the need for model, and the various considerations regarding the selection of intraoral scanners suitable for clinical goals. The acquired optical impression data can be sent anywhere in the world, which offers the advantage of overcoming any temporal or spatial constraints. The purpose of this chapter is to understand digital workflow using optical impression and to learn how to use it effectively in clinical practice

Accuracy of MAP segmentation with hidden Potts and Markov mesh prior models via Path Constrained Viterbi Training, Iterated Conditional Modes and Graph Cut based algorithms

In this paper, we study statistical classification accuracy of two different Markov field environments for pixelwise image segmentation, considering the labels of the image as hidden states and solving the estimation of such labels as a solution of the MAP equation. The emission distribution is assumed the same in all models, and the difference lays in the Markovian prior hypothesis made over the labeling random field. The a priori labeling knowledge will be modeled with a) a second order anisotropic Markov Mesh and b) a classical isotropic Potts model. Under such models, we will consider three different segmentation procedures, 2D Path Constrained Viterbi training for the Hidden Markov Mesh, a Graph Cut based segmentation for the first order isotropic Potts model, and ICM (Iterated Conditional Modes) for the second order isotropic Potts model. We provide a unified view of all three methods, and investigate goodness of fit for classification, studying the influence of parameter estimation, computational gain, and extent of automation in the statistical measures Overall Accuracy, Relative Improvement and Kappa coefficient, allowing robust and accurate statistical analysis on synthetic and real-life experimental data coming from the field of Dental Diagnostic Radiography. All algorithms, using the learned parameters, generate good segmentations with little interaction when the images have a clear multimodal histogram. Suboptimal learning proves to be frail in the case of non-distinctive modes, which limits the complexity of usable models, and hence the achievable error rate as well. All Matlab code written is provided in a toolbox available for download from our website, following the Reproducible Research Paradigm

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

Phenomenological modeling of image irradiance for non-Lambertian surfaces under natural illumination.

Various vision tasks are usually confronted by appearance variations due to changes of illumination. For instance, in a recognition system, it has been shown that the variability in human face appearance is owed to changes to lighting conditions rather than person\u27s identity. Theoretically, due to the arbitrariness of the lighting function, the space of all possible images of a fixed-pose object under all possible illumination conditions is infinite dimensional. Nonetheless, it has been proven that the set of images of a convex Lambertian surface under distant illumination lies near a low dimensional linear subspace. This result was also extended to include non-Lambertian objects with non-convex geometry. As such, vision applications, concerned with the recovery of illumination, reflectance or surface geometry from images, would benefit from a low-dimensional generative model which captures appearance variations w.r.t. illumination conditions and surface reflectance properties. This enables the formulation of such inverse problems as parameter estimation. Typically, subspace construction boils to performing a dimensionality reduction scheme, e.g. Principal Component Analysis (PCA), on a large set of (real/synthesized) images of object(s) of interest with fixed pose but different illumination conditions. However, this approach has two major problems. First, the acquired/rendered image ensemble should be statistically significant vis-a-vis capturing the full behavior of the sources of variations that is of interest, in particular illumination and reflectance. Second, the curse of dimensionality hinders numerical methods such as Singular Value Decomposition (SVD) which becomes intractable especially with large number of large-sized realizations in the image ensemble. One way to bypass the need of large image ensemble is to construct appearance subspaces using phenomenological models which capture appearance variations through mathematical abstraction of the reflection process. In particular, the harmonic expansion of the image irradiance equation can be used to derive an analytic subspace to represent images under fixed pose but different illumination conditions where the image irradiance equation has been formulated in a convolution framework. Due to their low-frequency nature, irradiance signals can be represented using low-order basis functions, where Spherical Harmonics (SH) has been extensively adopted. Typically, an ideal solution to the image irradiance (appearance) modeling problem should be able to incorporate complex illumination, cast shadows as well as realistic surface reflectance properties, while moving away from the simplifying assumptions of Lambertian reflectance and single-source distant illumination. By handling arbitrary complex illumination and non-Lambertian reflectance, the appearance model proposed in this dissertation moves the state of the art closer to the ideal solution. This work primarily addresses the geometrical compliance of the hemispherical basis for representing surface reflectance while presenting a compact, yet accurate representation for arbitrary materials. To maintain the plausibility of the resulting appearance, the proposed basis is constructed in a manner that satisfies the Helmholtz reciprocity property while avoiding high computational complexity. It is believed that having the illumination and surface reflectance represented in the spherical and hemispherical domains respectively, while complying with the physical properties of the surface reflectance would provide better approximation accuracy of image irradiance when compared to the representation in the spherical domain. Discounting subsurface scattering and surface emittance, this work proposes a surface reflectance basis, based on hemispherical harmonics (HSH), defined on the Cartesian product of the incoming and outgoing local hemispheres (i.e. w.r.t. surface points). This basis obeys physical properties of surface reflectance involving reciprocity and energy conservation. The basis functions are validated using analytical reflectance models as well as scattered reflectance measurements which might violate the Helmholtz reciprocity property (this can be filtered out through the process of projecting them on the subspace spanned by the proposed basis, where the reciprocity property is preserved in the least-squares sense). The image formation process of isotropic surfaces under arbitrary distant illumination is also formulated in the frequency space where the orthogonality relation between illumination and reflectance bases is encoded in what is termed as irradiance harmonics. Such harmonics decouple the effect of illumination and reflectance from the underlying pose and geometry. Further, a bilinear approach to analytically construct irradiance subspace is proposed in order to tackle the inherent problem of small-sample-size and curse of dimensionality. The process of finding the analytic subspace is posed as establishing a relation between its principal components and that of the irradiance harmonics basis functions. It is also shown how to incorporate prior information about natural illumination and real-world surface reflectance characteristics in order to capture the full behavior of complex illumination and non-Lambertian reflectance. The use of the presented theoretical framework to develop practical algorithms for shape recovery is further presented where the hitherto assumed Lambertian assumption is relaxed. With a single image of unknown general illumination, the underlying geometrical structure can be recovered while accounting explicitly for object reflectance characteristics (e.g. human skin types for facial images and teeth reflectance for human jaw reconstruction) as well as complex illumination conditions. Experiments on synthetic and real images illustrate the robustness of the proposed appearance model vis-a-vis illumination variation. Keywords: computer vision, computer graphics, shading, illumination modeling, reflectance representation, image irradiance, frequency space representations, {hemi)spherical harmonics, analytic bilinear PCA, model-based bilinear PCA, 3D shape reconstruction, statistical shape from shading

Robotics in Dentistry : A Narrative Review

Background: Robotics is progressing rapidly. The aim of this study was to provide a comprehensive overview of the basic and applied research status of robotics in dentistry and discusses its development and application prospects in several major professional fields of dentistry. Methods: A literature search was conducted on databases: MEDLINE, IEEE and Cochrane Library, using MeSH terms: [“robotics” and “dentistry”]. Result: Forty-nine articles were eventually selected according to certain inclusion criteria. There were 12 studies on prosthodontics, reaching 24%; 11 studies were on dental implantology, accounting for 23%. Scholars from China published the most articles, followed by Japan and the United States. The number of articles published between 2011 and 2015 was the largest. Conclusions: With the advancement of science and technology, the applications of robots in dental medicine has promoted the development of intelligent, precise, and minimally invasive dental treatments. Currently, robots are used in basic and applied research in various specialized fields of dentistry. Automatic tooth-crown-preparation robots, tooth-arrangement robots, drilling robots, and orthodontic archwire-bending robots that meet clinical requirements have been developed. We believe that in the near future, robots will change the existing dental treatment model and guide new directions for further development

Are different generations of CAD/CAM milling machines capable to produce restorations with similar quality?

Background: Different CAD/CAM machinesâ generation may impact the restoration overall quality. The present study evaluated the marginal fit of CAD/CAM restorations manufactured with different generations of CEREC milling unit systems. Material and Methods: Sixteen typodont teeth were divided into two groups (n=8) according to the machineâ s generation assigned. These are control group (G1): Cerec AC with Bluecam/Cerec 3 milling unit and (G2): Cerec AC with Bluecam/MC XL Premium Package milling unit. Scanning of the preparation were performed and crowns were milled using the Vita Mark II blocks. Blocks were cemented using epoxy glue on the pulpal floor only and finger pressure applied for 1 min. Upon completion of the cementation step, misfits between the restoration and abutment were measured by microphotography and the silicone replica technique using light body silicon material on Mesial (M) and Distal (D) surfaces. Results: Mean and SDs of marginal gaps in micrometers were: G1/M: 94.90 (±38.52), G1/D: 88.53 (±44.87), G2/M: 85.65 (±29.89), G2/D: 95.28 (±28.13). Two-way ANOVA indicated no significant differences among different groups (P>0.05); surface area (P>0.05) and the interaction (P>0.05). Overall, G2 had greater margin gaps than G1, however, without statistical difference (P>0.05). Conclusions: Difference in milling unit generation did not significantly affect the marginal fit. Marginal gap means were in the range of the clinical acceptance levels for both generations of Cerec milling units, regardless the teeth site area