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Position: Home > Articles > Extraction Method of Shape Feature for Vegetables Based on Depth Image Transactions of the Chinese Society for Agricultural Machinery 2012,43 (1) 242-245

基于深度图像的蔬果形状特征提取

作  者：
李长勇;曹其新
单  位：
上海交通大学机器人研究所
关键词：
番茄;机器视觉;特征提取;形状;深度图;傅里叶变换
摘  要：
针对蔬果二维投影图像含形状信息量少而影响蔬果分级精度的问题,提出一种基于深度图像的蔬果形状特征描述方法,以番茄形状特征提取为例,对该方法进行了探讨。首先利用彩色图像信息将番茄从背景中分割出;其次通过三维机器视觉测量设备获取番茄的点云数据,并对待检测番茄的点云数据深度进行归一化处理;然后通过关联被分割出的番茄区域信息与深度信息得到了番茄的深度图,并对该深度图进行极坐标采样。通过在笛卡尔直角坐标下对采样结果进行傅里叶变换,获得了基于深度图像的通用傅里叶形状描述子,该描述子不仅能有效地描述番茄在深度和横向上的形状特征,同时还具有平移、旋转和缩放的不变性。将基于深度图的通用傅里叶描述子和基于一般二维投影图像的通用傅里叶描述子先后用于番茄的分级实验中,结果表明前者平均分级精度达到92%,精度高于后者。
译  名：
Extraction Method of Shape Feature for Vegetables Based on Depth Image
作  者：
Li Changyong Cao Qixin(Research Institute of Robotics,Shanghai Jiaotong University,Shanghai 200240,China)
关键词：
Tomato,Machine vision,Feature extraction,Shape,Depth image,Fourier transform
摘  要：
The method of shape feature extraction based on depth image for the classification of tomatoes shape was proposed.Firstly,the shape of tomatoes was separated from the background through the segmentation of image in color space.Secondly,the point cloud of tomatoes was obtained by unitizing a 3-D machine vision measuring device.In order to implement the shape feature extraction of tomatoes in the same scale,the depth values of tomatoes were normalized.The depth map of tomatoes was formed according to the result of segment and the depth information of tomato.Further the depth map was sampled in polar coordinates and the sampling data was re-plotted in Cartesian coordinates.Finally,the depth image was re-plotted in the form of the Fourier transform in the Cartesian coordinates.The generic Fourier descriptor(GFD)was calculated based on depth map.The descriptor was characterized by the invariance of transformation of translation,rotation and scaling.The GFD based on depth image and the general GFD were successively used in the experiment of tomato grading.The result showed that the mean accuracy of the former classification was up to 92% and higher than the latter.

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