论文标题
从单个360°图像检测基于显着的多个区域
Saliency-based Multiple Region of Interest Detection from a Single 360° image
论文作者
论文摘要
360°图像内容丰富 - 它包含相机周围的全向视觉信息。但是，覆盖360°图像的区域比人类的视野大得多，因此在不同视图方向上的重要信息很容易被忽略。为了解决这个问题，我们提出了一种使用视觉显着性作为线索来预测单个360°图像的最佳感兴趣区域（ROI）的方法。为了处理现有的单个360°图像显着性预测数据集的稀缺，有偏见的训练数据，我们还提出了基于球形随机数据旋转的数据增强方法。从预测的显着图和冗余候选区域，我们可以获得最佳的ROI集合，考虑到区域内的显着性和区域之间的相互作用（IOU）。我们进行主观评估，以表明所提出的方法可以选择正确汇总输入360°图像的区域。
360° images are informative -- it contains omnidirectional visual information around the camera. However, the areas that cover a 360° image is much larger than the human's field of view, therefore important information in different view directions is easily overlooked. To tackle this issue, we propose a method for predicting the optimal set of Region of Interest (RoI) from a single 360° image using the visual saliency as a clue. To deal with the scarce, strongly biased training data of existing single 360° image saliency prediction dataset, we also propose a data augmentation method based on the spherical random data rotation. From the predicted saliency map and redundant candidate regions, we obtain the optimal set of RoIs considering both the saliency within a region and the Interaction-Over-Union (IoU) between regions. We conduct the subjective evaluation to show that the proposed method can select regions that properly summarize the input 360° image.
