基于多角度分割的360全景图的显著性检测

Salient Detection of 360 Panorama Based on Multi-Angle Segmentation

与常规2D图像不同,360全景图包含当前空间的全部视觉信息,因而在视频监控和虚拟现实等领域有着广泛的应用,然而用户在某一时刻只能观看到一定的视角,因此,360全景图的显著性区域检测对于视角预测至关重要。为此,提出了多角度分割的360全景图的显著性检测。首先将全景图进行多角度分割,将分割结果分别投影到立方体上以去除一定畸变;然后对每个立方体面通过稠密稀疏重建进行显著性计算;最后再将每个面的显著图投影到经纬映射方式的矩形上,进行多角度融合以获得最终的显著图。通过人工标注的全景图显著区域进行实验对比,结果表明该算法可以准确检测出360全景图的显著区域,并优于当前先进算法。

Unlike conventional 2D images,360 panorama contains all the visual information of the current space,so it has a wide range of applications in video surveillance and virtual reality.However,a certain angle is available at a certain time.Therefore,the significant region detection of the 360 panorama is very important to visual angle prediction.To solve this problem,we propose a multi-angle segmentation based 360 panoramic image saliency detection.Firstly,the panoramic images are cut at multiple angles,and the segmentation results are projected to the cube to remove certain distortion.Then,the salient calculation is conducted for each cube surface through dense and sparse reconstruction.Finally,the saliency images of each surface are projected to the rectangular of the warp and weft mapping,and multi-angle fusion is made to obtain the final salient figure.The results of the 360 panorama test by manual annotation show that the algorithm can accurately detect the saliency and is better than the other methods for the saliency detection of the 360 panorama.