基于空间金字塔匹配的单目热成像深度估计

热成像能够反映场景的温度分布,对热成像进行深度估计,可以恢复出场景的三维温度场,在故障诊断、夜视导航等领域具有重要意义。本文提出一种面向单目热成像深度估计的非参深度采样方法。为了克服热像纹理缺乏、轮廓模糊的缺点,使用了空间金字塔匹配(Spatial Pyramid Matching,SPM)来进行热像的特征分析。首先,基于SPM特征匹配,从数据库中筛选出与待估计深度的热像具有相似场景的候选热像；然后,采用SIFT Flow变形算法对候选热像的深度图进行采样,并将深度信息传递给待估计的热像。实验结果表明,这种方法能够对单目热像进行有效的深度估计,与同类算法相比具有明显优势。

Depth estimation of monocular thermal images based on spatial pyramid matching

Thermal imaging can reflect the temperature distributions of the scenes. The depth estimation of thermal imaging can reconstruct 3D temperature field of the scene,so it is significant in fault diagnosis and night vision navigation. A non-parametric depth sampling method for depth estimation for monocular thermal image is proposed. To overcome the lack of textures and blurry contours,a spatial pyramid matching method was used to analyze the features of thermal images. Firstly,based on SPM feature matching method,candidate images that have similar scene to the image of estimating depth were selected from the database. Then,a SIFT Flow algorithm was used to sample the depth maps of the candidate thermal images,and the depth information was transferred to the image of estimating depth. The experimental results show that this method can estimate the depth of monocular thermal image effectively,and it is better than other similar algorithms.