A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments
ACM Transactions on Graphics (TOG)
Physically Based Rendering: From Theory to Implementation
Physically Based Rendering: From Theory to Implementation
ACM SIGGRAPH 2005 Papers
A GPU based saliency map for high-fidelity selective rendering
AFRIGRAPH '06 Proceedings of the 4th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
Real-Time Tracking of Visually Attended Objects in Virtual Environments and Its Application to LOD
IEEE Transactions on Visualization and Computer Graphics
Image retargeting using mesh parametrization
IEEE Transactions on Multimedia
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
A model of dynamic visual attention for object tracking in natural image sequences
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
IEEE Transactions on Visualization and Computer Graphics
An efficient color representation for image retrieval
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum
IEEE Transactions on Multimedia
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In this work, we propose a novel graphic saliency detection method to detect visually salient objects in images rendered from 3D geometry models. Different from existing graphic saliency detection methods, which estimate saliency based on pixel-level contrast, the proposed method detects salient objects by computing object-level contrast. Given a rendered image, the proposed method first extracts dominant colors from each object, and represents each object with a dominant color descriptor (DCD). Saliency of each object is then calculated by measuring the contrast between the DCD of the object and the DCDs of its surrounding objects. We also design a new iterative suppression operator to enhance the saliency result. Compared with existing graphic saliency detection methods, the proposed method can obtain much better performance in salient object detection. We further apply the proposed method to selective image rendering and achieve better performance over the relevant existing algorithm.