A unified information-theoretic framework for viewpoint selection and mesh saliency

Viewpoint selection is an emerging area in computer graphics with applications in fields such as scene exploration, image-based modeling, and volume visualization. In particular, best view selection algorithms are used to obtain the minimum number of views (or images) in order to understand or model an object or scene better. In this article, we present a unified framework for viewpoint selection and mesh saliency based on the definition of an information channel between a set of viewpoints (input) and the set of polygons of an object (output). The mutual information of this channel is shown to be a powerful tool to deal with viewpoint selection, viewpoint stability, object exploration and viewpoint-based saliency. In addition, viewpoint mutual information is extended using saliency as an importance factor, showing how perceptual criteria can be incorporated to our method. Although we use a sphere of viewpoints around an object, our framework is also valid for any set of viewpoints in a closed scene. A number of experiments demonstrate the robustness of our approach and the good behavior of the proposed measures.

[1]  Dimitri Plemenos,et al.  Methods and data structures for virtual world exploration , 2006, The Visual Computer.

[2]  Mateu Sbert,et al.  Automatic View Selection Using Viewpoint Entropy and its Application to Image‐Based Modelling , 2003, Comput. Graph. Forum.

[3]  Silvia Biasotti,et al.  What’s in an image? , 2005, The Visual Computer.

[4]  Naftali Tishby,et al.  Document clustering using word clusters via the information bottleneck method , 2000, SIGIR '00.

[5]  Sergey Zhukov,et al.  An Ambient Light Illumination Model , 1998, Rendering Techniques.

[6]  Hans-Peter Seidel,et al.  Towards Stable and Salient Multi-View Representation of 3D Shapes , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).

[7]  Miquel Feixas,et al.  An Information-Theory Framework for the Study of the Complexity of Visibility and Radiosity in a , 2002 .

[8]  Naftali Tishby,et al.  Agglomerative Information Bottleneck , 1999, NIPS.

[9]  Mateu Sbert,et al.  Importance-Driven Focus of Attention , 2006, IEEE Transactions on Visualization and Computer Graphics.

[10]  T. Nishita,et al.  Locating Optimal Viewpoints for Volume Visualization , 2005 .

[11]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[12]  Han-Wei Shen,et al.  View selection for volume rendering , 2005, VIS 05. IEEE Visualization, 2005..

[13]  David W. Jacobs,et al.  Mesh saliency , 2005, ACM Trans. Graph..

[14]  Pierre Baldi,et al.  Bayesian surprise attracts human attention , 2005, Vision Research.

[15]  Mateu Sbert,et al.  Fast, realistic lighting for video games , 2003, IEEE Computer Graphics and Applications.

[16]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[17]  C H Merriam Hibernation of bats , 1886, Science.

[18]  Yuriko Takeshima,et al.  A feature-driven approach to locating optimal viewpoints for volume visualization , 2005, VIS 05. IEEE Visualization, 2005..

[19]  Pere Pau Vázquez Alcocer,et al.  Automatic indoor scene exploration , 2003 .

[20]  Han-Wei Shen,et al.  Dynamic View Selection for Time-Varying Volumes , 2006, IEEE Transactions on Visualization and Computer Graphics.

[21]  H H Bülthoff,et al.  How are three-dimensional objects represented in the brain? , 1994, Cerebral cortex.

[22]  Mateu Sbert,et al.  Viewpoint Selection using Viewpoint Entropy , 2001, VMV.

[23]  M J Tarr,et al.  What Object Attributes Determine Canonical Views? , 1999, Perception.

[24]  Mateu Sbert,et al.  An Information Theory Framework for the Analysis of Scene Complexity , 1999, Comput. Graph. Forum.

[25]  M. Tarr,et al.  To What Extent Do Unique Parts Influence Recognition Across Changes in Viewpoint? , 1995 .

[26]  Mateu Sbert,et al.  Viewpoint Entropy-Driven Simplification , 2007 .

[27]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[28]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[29]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[30]  Dimitri Plemenos,et al.  Viewpoint Quality: Measures and Applications , 2005, CAe.

[31]  C. R. Rao,et al.  On the convexity of some divergence measures based on entropy functions , 1982, IEEE Trans. Inf. Theory.

[32]  Dimitri Plemenos Exploring virtual worlds: current techniques and future issues , 2003 .

[33]  Mateu Sbert,et al.  Realtime automatic selection of good molecular views , 2006, Comput. Graph..

[34]  Dimitri Plemenos,et al.  Scene understanding techniques using a virtual camera , 2000, Eurographics.

[35]  Pere-Pau Vázquez,et al.  Way‐Finder: guided tours through complex walkthrough models , 2004, Comput. Graph. Forum.

[36]  Daniel Cohen-Or,et al.  Salient geometric features for partial shape matching and similarity , 2006, TOGS.

[37]  Erik Reinhard,et al.  Artistic Composition for Image Creation , 2001, Rendering Techniques.

[38]  Amitabh Varshney,et al.  Saliency-guided Enhancement for Volume Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

[39]  David S. Ebert,et al.  Volume Composition Using Eye Tracking Data , 2006, EuroVis.