Authors: Enya Shen Yunhai Wang Sikun Li
Publish Date: 2015/04/21
Volume: 19, Issue: 1, Pages: 157-168
Abstract
This paper proposes spatiotemporal volume saliency to detect and explore salient regions in timevarying volume data Based on the centersurround hypothesis that the salient region stands out from its surroundings we extend the spatial saliency to time domain and introduce temporal volume saliency It is defined as a centersurround operator on Gaussianweighted mean attribute gradient between steps in a scaleindependent manner By combing spatial saliency and temporal saliency together our spatiotemporal volume saliency is effective in detecting changes of salient regions We demonstrate its utility in this regard by automating transfer function design and selecting key frames for timevarying volume data
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