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Establishment method of 3D saliency model based on prior knowledge and depth weight

机译:基于先验知识和深度权重的3D显着性模型建立方法

摘要

A method of establishing a 3D saliency model based on 3D contrast and depth weight, includes dividing left view of 3D image pair into multiple regions by super-pixel segmentation method, synthesizing a set of features with color and disparity information to describe each region, and using color compactness as weight of disparity in region feature component, calculating feature contrast of a region to surrounding regions; obtaining background prior on depth of disparity map, and improving depth saliency through combining the background prior and the color compactness; taking Gaussian distance between the depth saliency and regions as weight of feature contrast, obtaining initial 3D saliency by adding the weight of the feature contrast; enhancing the initial 3D saliency by 2D saliency and central bias weight.
机译:一种基于3D对比度和深度权重的3D显着性模型的建立方法,包括通过超像素分割方法将3D图像对的左视图划分为多个区域,通过颜色和视差信息合成一组特征以描述每个区域,以及使用色彩紧凑度作为区域特征分量的视差权重,计算区域与周围区域的特征对比度;在视差深度图上获取背景先验,并通过结合背景先验和色彩紧实度来提高深度显着性;将深度显着性与区域之间的高斯距离作为特征对比的权重,通过增加特征对比的权重获得初始3D显着性;通过2D显着性和中心偏差权重来增强初始3D显着性。

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