结合立体视觉舒适度的立体图像显著性检测

针对先前的立体图像显著性检测模型未充分考虑立体视觉舒适度和视差图分布特征对显著区域检测的影响,提出了一种结合立体视觉舒适度因子的显著性计算模型.该模型在彩色图像显著性提取中,首先利用SLIC算法对输入图像进行超像素分割,随后进行颜色相似区域合并后再进行二维图像显著性计算;在深度显著性计算中,首先对视差图进行预处理;然后基于区域对比度进行显著性计算;最后,结合立体视觉舒适度因子对二维显著图和深度显著图进行融合,得到立体图像显著图.在不同类型立体图像上的实验结果表明,该模型获得了85%的准确率和78%的召回率,优于现有常用的显著性检测模型,并与人眼立体视觉注意力机制保持良好的一致性.

Saliency Detection for Stereoscopic Images by Considering Stereo Visual Comfort

In view of the fact that the previous saliency detection models fail to fully consider the effect of stereo visual comfort and the distribution features of disparity values, a saliency computation model considering stereo visual comfort is proposed. In the extraction of color image''s saliency, the model first segments an input image into super-pixel regions by using SLIC algorithm, and merges the regions according to color similarity among adjacent regions. After that, the computation of 2D image''s saliency is conducted. In the computation of depth saliency, the model first preprocesses the disparity map, and then a regional disparity contrast-based saliency analysis is applied to compute the salient region of the depth map. Finally, the stereo visual comfort factor is embedded into the fusion of the 2D saliency map and depth map to obtain a final stereoscopic saliency image. We evaluated the proposed model for stereoscopic images with various scenarios. The experimental results indicate that the proposed model outperforme existing saliency detection models, yielding an 85% precision and 78% recall rate. Moreover, the saliency region distributions fit well with the human binocular visual attention.