Information theory tools for viewpoint selection, mesh saliency and geometry simplification

In this chapter we review the use of an information channel as a unified framework for viewpoint selection, mesh saliency and geometry simplification. Taking the viewpoint distribution as input and object mesh polygons as output vectors, the channel is given by the projected areas of the polygons over the different viewpoints. From this channel, viewpoint entropy and viewpoint mutual information can be defined in a natural way. Reversing this channel, polygonal mutual information is obtained, which is interpreted as an ambient occlusion-like quantity, and from the variation of this polygonal mutual information mesh saliency is defined. Viewpoint entropy, viewpoint Kullback-Leibler distance, and viewpoint mutual information are then applied to mesh simplification, and shown to compare well with a classical geometrical simplification method

p38γ is essential for cell cycle progression and liver tumorigenesis

The cell cycle is a tightly regulated process that is controlled by the conserved cyclin-dependent kinase (CDK)–cyclin protein complex1. However, control of the G0-to-G1 transition is not completely understood. Here we demonstrate that p38 MAPK gamma (p38γ) acts as a CDK-like kinase and thus cooperates with CDKs, regulating entry into the cell cycle. p38γ shares high sequence homology, inhibition sensitivity and substrate specificity with CDK family members. In mouse hepatocytes, p38γ induces proliferation after partial hepatectomy by promoting the phosphorylation of retinoblastoma tumour suppressor protein at known CDK target residues. Lack of p38γ or treatment with the p38γ inhibitor pirfenidone protects against the chemically induced formation of liver tumours. Furthermore, biopsies of human hepatocellular carcinoma show high expression of p38γ, suggesting that p38γ could be a therapeutic target in the treatment of this disease

Tsallis Entropy for Geometry Simplification

This paper presents a study and a comparison of the use of different information-theoretic measures for polygonal mesh simplification. Generalized measures from Information Theory such as Havrda–Charvát–Tsallis entropy and mutual information have been applied. These measures have been used in the error metric of a surface simplification algorithm. We demonstrate that these measures are useful for simplifying three-dimensional polygonal meshes. We have also compared these metrics with the error metrics used in a geometry-based method and in an image-driven method. Quantitative results are presented in the comparison using the root-mean-square error (RMSE)This work was supported by the Spanish Ministry of Science and Innovation (Project TIN2010-21089-C03-03 and TIN2010-21089-C03-01) and Feder Funds, Bancaixa (Project P1.1B2010-08), Generalitat Valenciana (Project PROMETEO/2010/028) and Project 2009-SGR-643 of Generalitat de Catalunya (Catalan Government

Reducing complexity in polygonal meshes with view-based saliency

Salient features in 3D meshes such as small high-curvature details in the middle of largely flat regions are easily ignored by most mesh simplification methods. Nevertheless, these features can be perceived by human observers as perceptually important in CAD models. Recently, mesh saliency has been introduced to identify those visually interesting regions. In this paper, we apply view-based mesh saliency to a purely visual method for surface simplification from two approaches. In the first one, we propose a new simplification error metric that considers polygonal saliency. In the second approach, we use viewpoint saliency as a weighting factor of the quality of a viewpoint in the simplification algorithm. Our results show that saliency can improve the preservation of small but visually significant surfaces even in visual algorithms for surface simplification. However, this comes at a price, because logically some other low-saliency regions in the mesh are simplified further

Viewpoint-driven simplification using mutual information

In this paper, a new viewpoint-based simplification approach is proposed for polygonal meshes. This approach is driven by an information-theoretic measure, viewpoint mutual information. Our algorithm applies the best half-edge collapse as a decimation criterion and uses the variation in mutual information to measure the collapse error. Compared to purely geometric simplification algorithms, the models produced by our method are closer to the original model as far as visual similarity is concerned. Our method also achieves a higher simplification in hidden interiors by being able to remove them leaving the visible surfaces of the mesh intact. Models generated by CAD applications can benefit from this feature, since these models are usually constructed by assembling smaller objects that can become partially hidden during joining operations. The main application of our approach is for video games where models come from CAD applications in which visual similarity is the most important requirement. © 2008 Elsevier Ltd. All rights reserved

Viewpoint-based simplification using f-divergences

We propose a new viewpoint-based simplification method for polygonal meshes, driven by several f-divergences such as Kullback-Leibler, Hellinger and Chi-Square. These distances are a measure of discrimination between probability distributions. The Kullback-Leibler distance between the projected and the actual area distributions of the polygons in the scene already has been used as a measure of viewpoint quality. In this paper, we use the variation in those viewpoint distances to determine the error introduced by an edge collapse. We apply the best half-edge collapse as a decimation criterion. The approximations produced by our method are close to the original model in terms of both visual and geometric criteria. Unlike many pure visibility-driven methods, our new approach does not completely remove hidden interiors in order to increase the visual quality of the simplified models. This makes our approach more suitable for applications which require exact geometry tolerance but also require high visual quality. © 2008 Elsevier Inc. All rights reserved

Self-narrative reconstruction after dilemma-focused therapy for depression: a comparison of good and poor outcome cases

Objective: The aim of this study is to improve the understanding of self-changes after an intervention for depression focused on implicative dilemmas, a type of cognitive conflict related to identity. As recent research has highlighted the relevance of identity-related dilemmas in clients with depression, we sought to assess the way in which clients resolve such inner conflicts after a tailored dilemma-focused intervention and how this is reflected in the clients’ self-narratives. Method: We used three instruments to observe differences between good (n = 5) and poor (n = 5) outcome cases: (i) the Repertory Grid Technique to track the resolution of dilemmas, (ii) the Change Interview to compile clients’ accounts of changes at posttreatment, and (iii) the Innovative Moments Coding System to examine the emergence of clients’ novelties at the Change Interview. Results: Groups did not differ in terms of the number and relevance of client-identified significantly helpful events. However, between-group differences were found for the resolution of dilemmas and for the proportion of high-level innovative moment (IM) types. Furthermore, a greater self-narrative reconstruction was associated with higher levels of symptom improvement. Conclusions: Good outcome cases seem to be associated with the resolution of conflicts and high-level IMs.This study has been partially supported by the Portuguese Foundation for Science and Technology (PTDC/ PSI-PCL/121525/2010) and the Spanish Ministry of Economy and Competitiveness (PSI2011–23246)

Esquemas de fingerprinting para la protección de derechos de distribución

Postprint (published version

Simplification method for textured polygonal meshes based on structural appearance

This paper proposes an image-based simplification method for textured triangle meshes that preserves the structural appearance of textured models. Models used in interactive applications are usually composed of textured polygonal meshes. Since textures play an important role in the final appearance of the simplified model, great distortions can be obtained if texture information is not considered in the simplification process. Our method is based on an information channel created between a sphere of viewpoints and the texture regions. This channel enables us to define both the Shannon entropy and the mutual information associated with each viewpoint, and their respective generalizations based on Harvda–Charvát–Tsallis entropy. Several experiments show that great visual distortions are avoided when textured models are simplified using our method