Fused Visualization with Non-uniform Transparent Surface for Volumetric Data Using Stochastic Point-Based Rendering

Hasegawa, Kyoko; Fujimoto, Yuta; Xu, Rui; Tateyama, Tomoko; Chen, Yen‐Wei; Tanaka, Satoshi

doi:10.1007/978-3-319-39687-3_23

Cited by 1 publication

(1 citation statement)

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“…Hence there is a need of simplest transfer function for making the volume data visible. Using well-known TF requirement technique's applications to intestinal information sets achieve with MR, CT, and PET shows that prepared volume improvement efficiently creates higher standard of 3D rendering Kyoko Hasegawa et al [23]. Sorting of transparent object in unity is a problem associated with the method of transparent-rendering.…”

Section: Related Workmentioning

confidence: 99%

Optimal View Point Selection in Direct Volume Rendering for 3D Visualization

Gunasekaran¹,

MeenakshiSundram²

2017

IJIES

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3D visualization is the recent research need to be able to view the model from various angles so as to see data that is obscured by other data. While visualizing the data, view point selection is one of the major challenge which define the viewpoint information to select best viewpoints. This paper introduces teaching learning opacity based optimization (TLOO) and Kalman filter for selecting optimal viewpoint. The structural features of 3D volume dataset such as opacity and luminance is extracted for selecting optimal viewpoint position from that structural features. TLOO optimization algorithm identifies the voxels and standard information using view point evolution function. Then, kalman filter steers the optimization process with the help of cost function. The proposed flow of the technique is simple and very effective for selecting optimal viewpoints in 3D images. Computational complexity and the quality metrics are analyzed that provided maximum output efficiency.

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Section: Related Workmentioning

confidence: 99%