Rgb-D Saliency Detection: Dataset And Algorithm For Robot Vision
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO)(2018)
摘要
Saliency detection is an active research field in computer vision in recent years. As RGB-D sensors are more and more widely used in a robot system, the demand of corresponding saliency detection datasets and algorithms are growing rapidly. In this paper, we built a RGB-D saliency detection dataset NJUSTDS1000 contains 1000 real RGB-D scenes. The labeling of saliency ground truth of this dataset is based on color and depth fixation map. Then we propose a spectral and spatial analysis based RGB-D saliency detection model. It uses quaternion to present multi-channel features and dose fast saliency detection based on spectral analysis. Then a two-steps scale adaptive saliency fusion process is carried out in spatial domain, which are scale adaptive superpixel based saliency smoothing and multi-layer cellular automata based saliency maps fusion. We validate the proposed model on NJUSTDS 1000 and MSRA10K.
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关键词
RGB-D sensors,two-steps scale adaptive saliency fusion process,scale adaptive superpixel based saliency smoothing,multilayer cellular automata,spectral analysis,multichannel feature,spatial analysis,color fixation map,depth fixation map,computer vision,robot vision,saliency detection datasets
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