基于目标特征的植株深度图像修复

Plant Depth Maps Recovery Based on Target Features

针对植株深度图像的像素错误和缺失、常见的滤波方法无法准确修复植株深度图像的问题,提出一种基于目标特征的植株深度图像修复方法。首先基于颜色和空间信息的图像分割算法对植株彩色图像进行目标分割,再检索每个目标的外轮廓,并对外轮廓进行多边形拟合;其次,基于目标区域搜索深度图像中具有正确深度值的像素作为目标区域采样点,并对叶片区域的图像进行归一化;最后,利用空间拟合法计算各目标区域的方程,修复区域内小面积错误和缺失的深度值,同时采用支持向量机和空间变换运算对大面积错误和缺失深度值的叶片区域进行修复。实验结果表明,该方法能够准确地修复植株深度图像中错误、缺失的深度数据,且能够有效地保护目标区域的边缘信息。

Considering the depth images of plants provided by depth camera are incomplete,and common filtering methods cannot inpaint the plant depth images accurately,we propose a plant depth images inpainting method which is based on target features.Firstly,targets of plant color images are segmented by using a color image segmentation algorithm based on color and spatial information,then the outer contour of each target is retrieved,and polygon for each outer contour is fitted.Secondly,the pixels with correct depth value in the depth images are searched to act as sampling points,and meanwhile the leaf maps are normalized.Finally,using spatial fitting method to calculate every target area’s equation to correct the small area’s depth pixels which need to be corrected.In the meantime,support vector machine and spatial transformation are used to get the accurate large area’s depth pixels which need to be corrected.The experiments show that the proposed method achieves better performance for plant depth image inpainting,and protects targets’edge information.