基于Zernike矩的马铃薯薯形检测

目前基于机器视觉的马铃薯薯形检测的形状特征单一,相关研究较少,为了进一步探索合适的形状特征参数及检测方法,该文将Zernike矩作为特征参数并利用支持向量机实现了马铃薯薯形的检测分类,准确度较高。首先用截取最佳图像的方法对马铃薯图像进行归一化,使得归一化后的图像具有平移和尺度不变性,然后从归一化的图像中计算具有旋转不变性的Zernike矩参数,通过特征筛选确定分类的19个Zernike特征参数,最后将这些特征输入到支持向量机中,用高斯径向基核函数（RBF）和Sigmoid核函数构建混合核函数,完成马铃薯薯形检测分类,对薯形良好和畸形的检测准确率达93%和100%,能够准确剔除畸形马铃薯并满足实际检测的要求。     

基于灰度截留分割与十色模型的马铃薯表面缺陷检测方法

为探索基于计算机视觉的马铃薯表面缺陷检测新方法,该研究提出能将马铃薯表面疑似缺陷一次性分离出来的快速灰度截留分割方法和用于缺陷识别的十色模型。选择面积比率和十色比率作为缺陷判别特征,对分割出来的深色部位采用阈值法进行缺陷识别。采用基于快速G与亮度截留分割的2种方法对发芽进行识别。通过对326个马铃薯样本的652幅正反面图像进行试验,基于十色模型的缺陷识别方法对分割出来的深色区域的正确识别率为93.6%,基于快速G与亮度截留分割2种方法结合对有芽体图像的正确识别率为97.5%,马铃薯表面缺陷正确检测率为95     

基于模糊模式的蝗虫图像识别方法

蝗灾是中国农业病虫害防治的重点,采用低空机载蝗虫预警系统技术,对拍摄图像中蝗虫识别和计数,实现蝗虫监测,可以弥补蝗虫地面人工监测的不足。以广东省清远市英德区域农田为试验区,采用数码相机采集蝗虫图像,对蝗虫区域和背景的RGB分量平均值进行对比分析,选用超G绝对值法进行灰度转换,实现蝗虫与背景分离。通过面积统计对比,确定单个蝗虫的面积和周长特征,建立单个蝗虫模糊集和粘连重叠蝗虫区域模糊集,采用最大隶属度原则可以判定蝗虫连通区域为单个蝗虫或是存在图像粘连重叠。用模糊识别方法对单个和粘连重叠的区域分别计算数量,准     

神经网络对水稻种子颖壳闭合的识别研究

基于神经网络的基本原理,利用种子的七个特征参数作为输入层,三种类型（正常、微裂、裂颖）种子作为输出层,设计出识别水稻种子颖壳闭合的网络结构。通过大量实验研究表明该神经网络结构能够很好地识别出裂开程度不同类型的种子。     

Pattern recognition of geophysical data

A new rock classification method for ground penetrating radar (GPR) data is presented for cases where no additional geological information is available from boreholes. There are non-linear relationships between petrophysical properties of rocks and electromagnetic waves which can be handled using two methods derived from statistical learning theory on pattern recognition. An investigation was carried out looking at proving the feasibility of the method in principle for use on synthetic models as well as measurement data. The different learning methods were also compared.The method is based on multivariate statistical learning algorithms for the discrimination of layer boundaries between different rocks. The discrimination developed works with artificial neural networks (ANN) and support vector machines (SVM). The processing procedure starts with geological models with varying petrophysical rock parameters, which are to be sought in the measurement data. The models are used to generate synthetic radargrams from which rock properties can be derived using wave attributes. The calculated values of the wave attributes are stored in a multivariate data pool. This data pool is used to train the ANN and the SVM. The same wave attributes are derived from the GPR data and also saved in a data pool. This generates two data sets for pattern recognition with which to directly classify rock layers. Wave attributes can therefore be used to derive the non-linear correlative relationships between rock properties and GPR data by the weighted matrices of ANN and SVM.The presented method can be used to match reflections in the GRR data directly with the layer boundaries of rock formations. The classification of a boundary horizon between rock salt and anhydrite is demonstrated on synthetic GPR traces and measurement data from a rock salt mine. The advantage of this method is that rock classification is not a priori dependent on borehole data.     

Research on Accounting Recognition and Measurement of Forest Ecological Assets

Current accounting standards and systems do not include the accounting of forest ecological assets.With the increasing attention that people pay to forest ecological issues and the needs for accounting information disclosure,its value accounting has become a forefront issue and hot topic in the accounting profession.The profound issues about its particularities,accounting recognition and measurements are to be solved. Based on the analysis of the features of forest ecological assets and its impacts on accounting recognition and measurement,this paper proposes that accounting recognition should be based on the appraisal and a fair value model is suggested for initial and further measurement of forest ecological assets.For the fair value measurement, the appraisal techniques of non-market values should be given more consideration.And its value appraisal methods should refer to the existing mature ecological economic or environmental economic appraisal methods.     

黄顶菊种子的图像分割及特征提取

针对黄顶菊种子人工识别时存在的主观性强、效率低、可重复性差等缺点,提出利用计算机视觉和图像处理技术提取黄顶菊种子特征参数的方法,研究了黄顶菊种子图像的分割方法、目标提取及种子图像中形态和颜色特征的提取方法,分析了各形态特征参数的相关性,筛选出了能精确反映黄顶菊种子几何特征的参数,验证了各特征参数提取方法的有效性,为后期的种子自动识别提供了依据。     

射频识别技术在粮食供应链体系中的应用研究

将射频识别技术应用于粮食供应链系统中,力求实现供应链体系运行的快速与高效.介绍了射频识别技术的基本原理及特点,在此基础上构建了粮食供应链管理系统的系统模型,并给出了粮食供应链管理系统的系统架构,以及基于射频识别技术的实现过程,最后对现存的问题和相应对策进行了概述.     

A Self-Learing Chinese Character Recognition System

A method using possibility theory and fuzzy logic is introduced. Possibility functions of each feature are determined during training. Methods of fuzzy logic are used to combine the possibility of different features. This Chinese character recognition system consists of a pre-classifier and a two-level fine classifier. In order to handle deviations of the characters,a fuzzification of features is introduced. This method is used to develop single-font classifiers. The whole system consists of a font-classifiers and four single-font-classifiers.     

BP Neural Networks and Its Application in Lung Cancer Intelligent Diagnosis System

The learning algorithm of networks is discussed. The programming example of 3 layer BP networks is given with Visual C++6.0 program langue. Based on this model, a lung cancer intelligent diagnosis system is successfully implemented. Furthermore, the paper introduces network's structure design, preferencesand the source of stylebookdatum in factual applications. The ameliorative arithmetic is applied to the study of networks and BP dynamic evolving process is designed. The experiments indicatecell images are recognized and classified by the trained neural network. The study illustrates the system has feasibility and clinical value in lung cancer diagnosis.