A framework for improvement of importance map for image retargeting assisted by light field images

Kazu Mishiba, Y. Oyamada, K. Kondo
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Abstract

Image retargeting methods change the size of images to an arbitrary resolution while protecting visually important regions from distortion. Since retargeting methods deform contents of an image based on these importance, importance calculation methods suitable for image retargeting is needed. In this paper, we propose a framework to improve an importance map by using depth and segmentation information obtained from light field images. Depth information is used for considering the distance of objects from a camera. Segmentation information is used for maintaining visual consistency. Experimental results show that importance improved by our framework leads to better retargeting results.
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一种改进光场图像重定位重要性图的框架
图像重定向方法将图像的大小更改为任意分辨率,同时保护视觉上重要的区域免受失真。由于重定向方法会根据这些重要度使图像的内容发生变形,因此需要适合图像重定向的重要度计算方法。本文提出了一种利用光场图像的深度和分割信息来改进重要性图的框架。深度信息用于考虑物体到相机的距离。分割信息用于保持视觉一致性。实验结果表明,我们的框架提高了重要性,导致了更好的重定向结果。
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