背景与前景融合的RGB-D图像显著性检测

RGB-D图像显著性检测是指在传统的2D图像中附加深度信息从而提取显著对象,但是现有的显著性检测模型,大多数只关注显著物体本身,却忽略了背景信息。因此,提出了一个新颖的显著性检测模型,将深度信息同时考虑到背景和前景中提取出显著区域。首先,通过图像边界信息的背景测量机制来去除前景噪声并从边界超像素中选择背景种子,从而计算出基于背景的显著图;其次,将输入的图像构造成图,并将深度信息引入到图形结构中,利用颜色、深度、位置等线索获取前景种子,从而计算出基于前景的显著图;最后,将背景图和前景图融合获得初始显著图,再加以元胞优化,迭代传播后得到最终的显著图。在三个RGB-D图像显著性检测数据集LFSD、NJU-400、NJU-2000上进行对比实验,实验结果表明,该方法具备有效性,同时也提高了检测准确率。

RGB-D Image Saliency Detection via Background and Foreground Fusion

RGB-D image saliency detection refers to the addition of depth information in traditional 2D images to extract significant objects.However,for current saliency detection models,most of them focus on the saliency objects themselves,but ignore the background content.Therefore,this paper proposes a novel saliency detection model that takes depth information into consideration of both background and foreground to extract salient area.Firstly,the foreground noise is removed by the background measurement mechanism of the image boundary information and the background seed is selected from the boundary superpixels to calculate a background-based saliency map.Secondly,the input image is constructed into a graph,and the depth information is introduced into the graph.The foreground seeds are obtained by using clues such as color,depth and position,and the foreground-based saliency map is calculated.Finally,the background and foreground map are merged to obtain the initial saliency map,the cell optimization is performed,and the iterative propagation is performed to obtain the final saliency map.The comparison experiments are carried out on three RGB-D image saliency detection datasets LFSD,NJU-400 and NJU-2000.The experimental results show that the proposed method is effective and improves the accuracy.