三维全景给用户提供360°视角的同时给人以强烈的三维立体真实感,虽然近年来研究人员开发了大量的算法来检测二维及三维图像中的显著区域,但针对立体全景图像显著性检测的研究较少。考虑到全景图像的投影特点、立方体投影(CMP)图像有助于消除顶部与底部引起的扭曲及边框效果,利用等矩形投影(ERP)图像中所有可用的上下文信息,以ERP作为全局信息、CMP作为局部信息,融合了全局和局部的视觉显著图。提出的立体全景显著性检测模型由颜色相似度算法和区域对比度算法两部分组成。首先,对图像进行多尺度线性迭代聚类超像素分割,根据像素块的颜色差异得到颜色对比特征图;然后依据空间分布紧凑性计算区域对比度;根据颜色对比特征和区域对比度特征得到图像显著图。通过结合赤道偏移并且融入深度信息得到最终的立体全景显著图。最后,将所得结果在公开的立体全景图像数据库ODI中进行了对比验证,实验结果表明,所提方法得到的显著结果具有较高的准确率、召回率和F-measure值,其综合性能优于6种经典的显著预测算法。所提模型既能够充分利用图像信息,又能有效地抑制复杂的背景区域,可得到更加符合视觉感知的显著图。

A three-dimensional (3D) panorama provides a 360° perspective for users, giving them a strong 3D sense of reality. Although researchers have developed a large number of algorithms to detect salient areas in two-dimensional and 3D images in recent years, there are few studies on the saliency detection of stereoscopic panoramic images. All available context information in an equirectangular projection (ERP) image which is used as the global information is used, the cubic projection (CMP) image is used as local information, and global and local visual saliency maps are integrated, taking into account the projection characteristics of the panoramic image and the CMP image which helps to eliminate the distortion and frame effect caused by the top and bottom. In this work, the proposed stereo panoramic saliency detection model is composed of two parts, i.e., color similarity and regional contrast methods. First, multi-scale linear iterative clustering superpixel segmentation is carried out on the images, and the color contrast feature map is obtained according to the color difference of pixel blocks. Then, the regional contrast is calculated according to the compactness of spatial distribution. The saliency map is obtained based on feature maps of color contrast and regional contrast. The final stereoscopic panoramic saliency map is obtained by combining equatorial migration and fusing depth information. Finally, the results obtained are compared and verified in the public stereo panoramic image database ODI. Experimental results show that the saliency maps obtained by the proposed method have high precision, recall rate, and F-measure value, and the comprehensive performance of the proposed method is better than that of the six classical saliency prediction algorithms. The proposed model not only makes full use of the image information, but also effectively suppresses the complex background area, so as to obtain the saliency map that is more consistent with the visual perception.