The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network

Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixel labeling was used to segment image and extract feature. (3) A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.

A chaotic neural network mimicking an olfactory system and its application on image recognition

Based on the research of a biological olfactory system, a novel chaotic neural network model - K set model has been es- tablished. This chaotic neural network not only simulates the real brain activity of an olfactory system, but also presents a novel chaotic concept for signal processing and pattern recognition. The characteristics of the K set models are investigated and show that a KIII model can be used for image pattern classification.

Prediction of Pitting Corrosion Mass Loss for 304 Stainless Steel by Image Processing and BP Neural Network

Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.

Leaf recognition using BP-RBF hybrid neural network

Plant recognition has great potential in forestry research and management.A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples.The process was carried out in three steps:image pretreatment,feature extraction,and leaf recognition.In the image pretreatment processing,an image segmentation method based on hue,saturation and value color space and connected component labeling was presented,which can obtain the complete leaf image without veins and back-ground.The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recogni-tion.The recognition accuracy of different classifiers was used to compare classification performance.The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%,highest among all the classifiers.

Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition

The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.

Research on Handwritten Chinese Character Recognition Based on BP Neural Network

The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before.Handwritten Chinese character recognition,as a hot research object in image pattern recognition,has many applications in people’s daily life,and more and more scholars are beginning to study off-line handwritten Chinese character recognition.This paper mainly studies the recognition of handwritten Chinese characters by BP(Back Propagation)neural network.Establish a handwritten Chinese character recognition model based on BP neural network,and then verify the accuracy and feasibility of the neural network through GUI(Graphical User Interface)model established by Matlab.This paper mainly includes the following aspects:Firstly,the preprocessing process of handwritten Chinese character recognition in this paper is analyzed.Among them,image preprocessing mainly includes six processes:graying,binarization,smoothing and denoising,character segmentation,histogram equalization and normalization.Secondly,through the comparative selection of feature extraction methods for handwritten Chinese characters,and through the comparative analysis of the results of three different feature extraction methods,the most suitable feature extraction method for this paper is found.Finally,it is the application of BP neural network in handwritten Chinese character recognition.The establishment,training process and parameter selection of BP neural network are described in detail.The simulation software platform chosen in this paper is Matlab,and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition.Design the GUI interface of human-computer interaction based on Matlab,show the process and results of handwritten Chinese character recognition,and analyze the experimental results.

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.

基于BP神经网络的桥梁施工线形相机测量标定

机器视觉位移测量技术为大跨桥梁线形控制提供新解,而确保高精度的二维到三维坐标转换至关重要。对此,提出一种基于改进遗传算法BP神经网络的提升双目相机标定精度的方法,通过改进传统神经网络中的交叉及变异概率函数,提高标定效率及准确性。经相应试验算例验证,采取传统张氏标定法测量坐标的均方差误差为4.67 mm,应用该方法标定后测量坐标的均方差误差为0.82 mm,标定精度提高,能够满足桥梁施工线形的监控要求。

基于图像处理和BP神经网络的森林防火无人机系统

对无人机设计方案、图像处理和火焰分割算法的技术原理进行了介绍,并利用BP神经网络对图像中的火焰面积变化率和火焰尖角等特征进行识别,实现了对森林火灾的快速监测。实验结果表明:系统的准确率为98.5%,比普通神经网络的84.5%更高;耗时仅22 s,比普通神经网络159 s缩短很多。这表明,BP神经网络是更可靠且更有效率的火灾识别方案。

基于BP神经网络的智慧车牌识别系统

车牌识别系统在我国智慧交通系统中起着重要作用。为解决识别不精准、易受环境影响等问题,本文设计一种基于BP神经网络的智慧车牌识别系统,该系统主要包含车牌图像预处理模块、BP神经网络模块和GUI用户界面模块。通过MATLAB仿真,得出该系统可以在较为复杂的环境中识别车牌,并实现了在可视化用户界面进行数字图像的可视化操作。

水果分拣机械手控制系统研究——基于BP神经网络和图像识别

首先,构建了水果分拣机械手系统整理框架,并对工业相机的选型和相机标定进行分析研究;然后,基于BP神经网络实现了对目标水果的识别;最后,利用图像处理的模板匹配对目标进行实时定位,实现了水果分拣。试验结果表明:系统可以实现对多种水果的分类,准确性非常高,具有可行性。

基于BP的食品外观品质检测可视化系统研究

为进一步加快大米加工的智能化水平,结合图像处理技术和智能算法,提出一种基于图像处理和BP算法的可视化识别方法。其中,通过图像灰度化、图像去噪和图像分割等手段,以提高后期识别精度;然后采用BP神经网络对完善米、垩白粒、黄米粒、碎米粒图像进行识别;最后用测试集图像对模型进行验证。结果表明,该方法的识别准确率超过90%,且识别时间在2~3 s。由此表明可视化设计方案可行。

Intelligent Information Processing in Imaging Fuzes

In order to study the problem of intelligent information processing in new types of imaging fuze, the method of extracting the invariance features of target images is adopted, and radial basis function neural network is used to recognize targets. Owing to its ability of parallel processing, its robustness and generalization, the method can realize the recognition of the conditions of missile-target encounters, and meet the requirements of real-time recognition in the imaging fuze. It is shown that based on artificial neural network target recognition and burst point control are feasible.

融合遗传算法和BP神经网络的光斑定位方法

针对振动环境中传统光斑中心定位算法存在的处理时间长、精度低等问题,本文提出一种基于遗传算法优化BP神经网络的光斑定位方法。使用BP神经网络对光斑位置进行预测,并通过遗传算法对神经网络进行优化。构建BP神经网络模型,将使用质心、形心、高斯拟合等方法求出的光斑中心位置以及形心法求出的光斑半径作为输入,对光斑真实中心位置进行预测。并使用遗传算法优化神经网络的权值和阈值,以增强预测效果。实验过程中,通过对光学系统外加干扰模拟振动环境,采集数据用于神经网络训练和算法验证。实验结果表明,优化前后的标定测试迭代次数分别为55和29,平均误差分别为0.81像素和0.45像素。由本文结果可知,在遗传算法的优化下,神经网络算法的迭代速度和预测精度均有所提高。

基于GA-BP神经网络的尾煤水灰分视觉检测方法研究

针对浮选回归模型精度和适应性较差的问题,提出了一种基于遗传算法优化反向传播神经网络(GA-BP)的尾煤水灰分视觉检测方法。对尾煤水图像进行预处理,在去除主要噪声干扰和保证一定彩色特征完整的前提下,提取不同颜色空间的彩色特征、灰度特征以及浓度特征值;以上述特征值为输入变量,以尾煤水灰分作为输出变量,建立基于遗传算法优化BP神经网络(GA-BP)的回归模型。该模型较好地实现了尾煤水灰分的在线检测,预测精度达97.3%,均方误差降低至0.23,提高了精煤产率和经济效益。

Single-Choice Aided Marking System Research Based on Back Propagation Neural Network

In the field of educational examination,automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor force.At present,the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and universities.The traditional marking system based on Optical Mark Reader technology can no longer meet the requirements of liberating the labor force of teachers in small and medium-sized examinations.With the development of image processing and artificial neural network technology,the recognition of handwritten character in the field of pattern recognition has attracted the attention of many researchers.In this paper,filtering and de-noise processing and binary processing are used as preprocessing methods for handwriting recognition.Extract the pixel feature of handwritten characters through digital image processing of handwritten character pictures,and then,get the feature vector from these feature fragments and use it as the description of the character.The extracted feature values are used to train the neural network to realize the recognition of handwritten English letters and numerical characters.Experimental results on Chars74K and MNIST data sets show that the recognition accuracy of some handwritten English letters and numerical characters can reach 90%and 99%,respectively.

基于卷积神经网络与可视图像的类滑动放电模式识别

为了提高机器学习算法对类滑动放电模式识别的准确率,提出了一种基于卷积神经网络(convolutional neuralnetworks,CNN)与可视图像识别电晕放电、弥散放电和类滑动放电等模式的方法。通过选取气体体积流量0~16 L/min、电极间隙2~10 mm、脉冲频率0.5~3 kHz等不同条件下的类滑动放电图像构建图像库,搭建CNN模型并优化影响CNN识别性能的超参数,包括网络层数、全连接层(full connected layer,FC)神经元数、卷积核尺寸以及激活函数类型,最后比较了CNN与决策树(decision tree,DT)算法和随机森林(random decision forests,RF)算法的识别效果。结果表明,CNN识别准确率为100%,高于传统机器学习方法。此外,本文还给出了放电模式及条件参数,通过基于反向传播神经网络(back propagation neural networks,BPNN)的聚类分析算法识别弥散放电和类滑动放电,并且准确率为100%。

Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.

Star pattern recognition method based on neural network

Star sensor is an avionics instrument used toprovide the absolute 3-axis attitude of a spacecraft by utiliz-ing star observations. The key function is to recognize theobserved stars by comparing them with the reference cata-logue. Autonomous star pattern recognition requires thatsimilar patterns can be distinguished from each other with a small training set. Therefore, a new method based on neural network technology is proposed and a recognition systemcontaining parallel backpropagation (BP) multi-subnets isdesigned. The simulation results show that the method per-forms much better than traditional algorithms and the pro-posed system can achieve both higher recognition accuracyand faster recognition speed.