李长勇,曹其新.基于深度图像的蔬果形状特征提取[J].农业机械学报,2012,43(Z1):242-245.
Li Changyong,Cao Qixin.Extraction Method of Shape Feature for Vegetables Based on Depth Image[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(Z1):242-245.
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基于深度图像的蔬果形状特征提取   [下载全文]
Extraction Method of Shape Feature for Vegetables Based on Depth Image   [Download Pdf][in English]
  
DOI:10.6041/j.issn.1000-1298.2012.S0.050
中文关键词:  番茄  机器视觉  特征提取  形状  深度图  傅里叶变换
基金项目:国家高技术研究发展计划(863计划)资助项目(2012AA100906)
作者单位
李长勇 上海交通大学 
曹其新 上海交通大学 
中文摘要: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。
Li Changyong  Cao Qixin
Shanghai Jiaotong University;Shanghai Jiaotong University
Key Words:Tomato  Machine vision  Feature extraction  Shape  Depth image  Fourier transform
Abstract: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.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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