农业工程学报Issue(7)：190-196,7.DOI:10.3969/j.issn.1002-6819.2014.07.022
基于序列图像三维重建的稻种品种识别
Variety identification of rice seed based on three-dimensional reconstruction method of sequence images
摘要
Abstract
Rice seed surface morphology is an important aspect of seed purity identification and recognition. Considering that artificial recognition and identification methods have some faults, which including low efficiency, high labor costs, and poor accuracy. So scientifically selecting quality rice seeds by using computer vision methods is important. Different models and methods have been established in the field of crop seed identification. Studies on rice seed speciation analysis methods indicate that the current detection methods in computer vision mainly analyze 2D information and that the use of 3D models is lacking. This paper proposes a 3D rice seed reconstruction system which can be used to measure the morphology of rice seed, with more accurate shape measure parameters and more comprehensive appearance characteristics and defect expression. <br> In this paper, a new crop seed reconstruction system that supports fast and accurate recognition was designed to build a 3D surface morphology. The depth-from-focus (DFF) method was applied in the analysis of crop surface morphology. Image sequences were acquired by using a specific vision device through setting different distances between the camera lens and the rice seed. High-pass filtering was used to extract pixels and analyze strength value changes in the frequency domain. The second-order differential was employed to strengthen the value in the frequency domain by using the improved Laplacian operator. The threshold statistical analysis was conducted in pixel windows, by which each pixel generated a value which showed the focusing condition. The focusing measure of the image sequence effectively determined the estimated depth value of a pixel, and a focusing pixel stack could be defined based on these values. Using the characteristics of the Gaussian distribution of the focal depth estimation value, the Gaussian interpolation was calculated to obtain a more precise surface morphology depth value. As a result, a depth image collected based on the estimated depth value of the pixel was developed. Finally, through depth image smoothing and edge pixel processing, a 3D point cloud could be produced. Thus, a rice seed reconstruction system which can be used in rice seed identification and recognition was designed. This novel system supports three main patterns, namely, shape, texture, and 3D recognition. <br> Through further calculations, the surface morphology characteristics of seed are obtained. The new 3D surface morphology reconstruction system can effectively overcome the deficiencies of traditional seed speciation analysis methods and can be served as an important reference for researchers. Finally, the BP neural network model was constructed to support the variety identification. Suitable neural network algorithm was selected for five different sorts of rice seed, and the final identification rate is 90%. The research can provide a reference for study of three-dimension shape and texture in automation crops variety identification field.关键词
机器视觉/神经网络/图像识别/序列图像/三维重建/聚焦测度法DFF/品种/稻种Key words
computer vision/neural networks/image recognition/sequence image/three-dimensional reconstruction/depth from focus (DFF)/variety/rice seed分类
农业科技引用本文复制引用
钱燕,尹文庆,林相泽,丁永前,冯学斌..基于序列图像三维重建的稻种品种识别[J].农业工程学报,2014,(7):190-196,7.基金项目
江苏省农机三项工程项目（NJ2010-02）；南京农业大学青年科技创新基金（KJ2010032）；中央高校基本科研业务费 （）
