A real-time mathematical computer method for potato inspection using machine vision

Detection of external defects on potatoes is the most important technology in the realization of automatic potato sorting stations. This paper presents a hierarchical grading method applied to the potatoes. In this work a potato defect detection combining with size sorting system using the machine vision will be proposed. This work also will focus on the mathematics methods used in automation with a particular emphasis on the issues associated with designing, implementing and using classification algorithms to solve equations. In the first step, a simple size sorting based on mathematical binarization is described, and the second step is to segment the defects; to do this, color based classifiers are used. All the detection standards for this work are referenced from the United States Agriculture Department, and Canadian Food Industries. Results show that we have a high accuracy in both size sorting and classification. Experimental results show that support vector machines have very high accuracy and speed between classifiers for defect detection.     

Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic

There are a significant number of high fall risk individuals who are susceptible to falling and sustaining severe injuries. An automatic fall detection and diagnostic system is critical for ensuring a quick response with effective medical aid based on relative information provided by the fall detection system. This article presents and evaluates an accelerometer-based multiple classifier fall detection and diagnostic system implemented on a single wearable Shimmer device for remote health monitoring. Various classifiers have been utilised within literature, however there is very little current work in combining classifiers to improve fall detection and diagnostic performance within accelerometer-based devices. The presented fall detection system utilises multiple classifiers with differing properties to significantly improve fall detection and diagnostic performance over any single classifier and majority voting system. Additionally, the presented multiple classifier system utilises comparator functions to ensure fall event consistency, where inconsistent events are outsourced to a supervisor classification function and discrimination power is considered where events with high discrimination power are evaluated to further improve the system response. The system demonstrated significant performance advantages in comparison to other classification methods, where the proposed system obtained over 99% metrics for fall detection recall, precision, accuracy and F-value responses.     

Assessing machine learning classifiers for the detection of animals’ behavior using depth-based tracking

There is growing interest in the automatic detection of animals’ behaviors and body postures within the field of Animal Computer Interaction, and the benefits this could bring to animal welfare, enabling remote communication, welfare assessment, detection of behavioral patterns, interactive and adaptive systems, etc. Most of the works on animals’ behavior recognition rely on wearable sensors to gather information about the animals’ postures and movements, which are then processed using machine learning techniques. However, non-wearable mechanisms such as depth-based tracking could also make use of machine learning techniques and classifiers for the automatic detection of animals’ behavior. These systems also offer the advantage of working in set-ups in which wearable devices would be difficult to use. This paper presents a depth-based tracking system for the automatic detection of animals’ postures and body parts, as well as an exhaustive evaluation on the performance of several classification algorithms based on both a supervised and a knowledge-based approach. The evaluation of the depth-based tracking system and the different classifiers shows that the system proposed is promising for advancing the research on animals’ behavior recognition within and outside the field of Animal Computer Interaction.     

Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.     

Optimization techniques for improving power quality data mining using wavelet packet based support vector machine

This paper aims at automatic classification of power quality events using Wavelet Packet Transform (WPT) and Support Vector Machines (SVM). The features of the disturbance signals are extracted using WPT and given to the SVM for effective classification. Recent literature dealing with power quality establishes that support vector machine methods generally outperform traditional statistical and neural methods in classification problems involving power disturbance signals. However, the two vital issues namely the determination of the most appropriate feature subset and the model selection, if suitably addressed, could pave way for further improvement of their performances in terms of classification accuracy and computation time. This paper addresses these issues through a classification system using two optimization techniques, the genetic algorithms and simulated annealing. This system detects the best discriminative features and estimates the best SVM kernel parameters in a fully automatic way. Effectiveness of the proposed detection method is shown in comparison with the conventional parameter optimization methods discussed in literature like grid search method, neural classifiers like Probabilistic Neural Network (PNN), fuzzy k-nearest neighbor classifier (FkNN) and hence proved that the proposed method is reliable as it produces consistently better results.     

集成分类器对脑卒中患者脑电的分类

脑卒中患者意识障碍的检查和检测耗时耗力且非连续,采集脑卒中患者的脑电信号,以研究有意识障碍与无意识障碍的脑卒中患者的自动分类。对脑卒中患者的脑电图提取多达9种定量脑电特征,构建脑网络,将这些脑网络的连通性特征输入到分类器中,实现对脑卒中患者是否有意识障碍的分类。为解决非平衡数据集分类时严重偏向多数类的问题,设计集成支持向量机分类器。实验结果显示基于现有分类器的脑卒中意识障碍的分类正确率在70%左右,敏感度在40%以下;而基于集成支持向量机分类器的分类准确性可达96.79%,同时敏感度和特异性分别为95.45%和100%。实验结果表明集成支持向量机分类器对非平衡数据集的脑电分类准确率显著提升,并促进脑卒中患者意识障碍的自动识别。     

基于支持向量机的纸张缺陷图像分类识别

根据支持向量机（SVM）在小样本、高维模式分类中具有的优良分类性能,提出将支持向量机应用于实际的纸张缺陷分类。针对三种现场易出现的缺陷,通过对缺陷图像进行预处理、特征选择,再利用SVM进行分类,利用交叉验证进行参数和模型选取,取得了较好的分类效果,为纸张缺陷的分类指出一种可行的方法。     

Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.     

磁瓦表面缺陷机器视觉检测与识别方法

针对磁瓦生产过程中表面缺陷检测的重要性和人工检测的弊端,研究基于机器视 觉的磁瓦表面缺陷自动检测与识别方法。为解决磁瓦表面缺陷种类多、对比度低、图像中存在 磨痕纹理背景和整体亮度不均匀等难点,定义扫描线梯度,其标准差与扫描线灰度标准差构成 特征向量,提出基于两类支持向量机的图像分割方法来判别和提取缺陷;并提出一种改进的多 类支持向量机方法,对缺陷进行分类识别,解决了多类支持向量机存在不可分区域的问题,提 高了分类器的准确性和有效性。实验结果表明,该方法能准确快速地提检测磁瓦表面各区域的 各类缺陷,检出率可达到96%以上,识别率超过91%。     

An approach to the automatic design of multiple classifier systems

Multiple classifier systems (MCSs) based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them are accurate and make different errors. Therefore, the fundamental need for methods aimed to design “accurate and diverse” classifiers is currently acknowledged. In this paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed at selecting the subset made up of the most accurate and diverse classifiers. A proof of the optimality of the proposed design approach is given. Reported results on the classification of multisensor remote sensing images show that this approach allows the design of effective multiple classifier systems.     

Classification of EEG signals using neural network and logistic regression

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.     

一种脆弱线路缺陷的图像智能检测算法设计

配电线路稳定运行可以有效提升电力系统有序性,脆弱线路缺陷是引起配电网连锁故障停电的主要原因。以人工为主的识别方法存在明显缺陷,在无人机的辅助下,设计了一种脆弱线路缺陷图像自动检测方法。通过构建脆弱线路数据集,以输电线路的脆弱性综合指标为依据,辨识配电网脆弱线路。建立配电网脆弱线路缺陷特征分类标准,利用图像增强技术提升脆弱线路缺陷图像成像效果。采用对比度受限自适应直方图均衡方法均衡脆弱线路缺陷图像的色彩和反差,结合小波变换对均衡后的脆弱线路缺陷图像进行降噪处理。运用卷积神经网络将降噪后的脆弱线路缺陷图像输入至卷积层完成脆弱线路缺陷自动检测。通过实验测试发现：提出方法的召回率最高为89.32%,精确率最高为98.20%,错检率最低为0.98%,能够最小范围识别脆弱线路缺陷,充分证实了提出算法检测效率较高。     

Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes

This paper introduces different classification systems based on artificial neural networks for the automatic detection of epileptic spikes in electroencephalogram records. Different multilayer perceptron networks are constructed and trained with different algorithms. The inputs of the networks consist of either raw data or extracted features. To improve the generalization performance of the classifiers, “training with noise” method is used whereby new training data is constructed by adding uncorrelated Gaussian noise to real data. The performances of the constructed classifiers are examined and compared both with each other and with other similar systems found in literature based on sensitivity, specificity and selectivity measures.     

Hierarchical classification and feature reduction for fast face detection with support vector machines

We present a two-step method to speed-up object detection systems in computer vision that use support vector machines as classifiers. In the first step we build a hierarchy of classifiers. On the bottom level, a simple and fast linear classifier analyzes the whole image and rejects large parts of the background. On the top level, a slower but more accurate classifier performs the final detection. We propose a new method for automatically building and training a hierarchy of classifiers. In the second step we apply feature reduction to the top level classifier by choosing relevant image features according to a measure derived from statistical learning theory. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 335 with similar classification performance.     

基于机器学习的刀具表面缺陷检测及分类方法

刀具在生产的过程中,由于人员、机器、环境等多方面原因,刀具的表面会出现各种缺陷,如划痕、碰撞凹坑、涂层剥落和边缘豁口;这些缺陷会严重影响刀具的质量和外观,对于刀具的缺陷检测,目前主要采用人工目检的方式,人工检测方法效率和准确率都比较低;为解决上述问题,提出一种刀具缺陷的自动化检测及分类算法;针对刀具图像的预处理,提出了一种基于双边滤波的降噪方法和基于差分的对比度增强算法;对于刀具的缺陷检测任务,提出了基于图像差分的缺陷检测算法;对于缺陷的分类任务,提出了一种基于SVM的分类算法,即通过提取缺陷区域的形状、纹理等特征来训练SVM分类器;最后对提出的缺陷检测及分类算法进行实验,结果表明算法的缺陷检出率达97.2%,分类准确率可达94.3%;算法能够很好地满足工业需求,可以替代人工实现刀具缺陷的自动化和高效率检测。     

基于视觉显著度的皮革表面瑕疵检测

针对汽车内饰皮革的瑕疵检测易受皮革自身纹理干扰、检测难度较大的问题,发现瑕疵存在于均匀变化图像中局部变化明显的区域,符合人眼注意机制,故提出了基于视觉显著模型的皮革瑕疵检测方法。首先提取皮革图像的颜色和亮度特征,然后利用中心周围差算子分别计算特征显著图,再融合成最终显著图,最后在此基础上利用区域生长方法对瑕疵区域进行分割,以实现瑕疵的准确定位。实验结果表明,与FCM聚类分割法、阈值分割法及SVM分类法相比,本文提出的方法具有较高的检测精度及较快的检测速度,解决了皮革瑕疵检测过程中受纹理干扰严重等问题,能有效应用于皮革瑕疵的机器自动检测中。     

基于分词与词性标注的汉语逗号自动分类

近年来,标点符号作为篇章的重要部分逐渐引起研究者的关注。然而,针对汉语逗号的研究才刚刚展开,采用的方法也大多都是在句法分析的基础上,尚不存在利用汉语句子的表层信息开展逗号自动分类的研究。提出了一种基于汉语句子的分词与词性标注信息做逗号自动分类的方法,并采用了两种有监督的机器学习分类器,即最大熵分类器和CRF分类器,来完成逗号的自动分类。在CTB 6.0语料上的实验表明,CRF的总体结果比最大熵的要好,而这两种分类器的分类精度都非常接近基于句法分析方法的分类精度。由此说明,基于词与词性做逗号分类的方法是可行的。     

Computer-aided detection of lung nodules based on decision fusion techniques

We adopted decision fusion techniques to develop a computer-aided detection (CAD) system for automatic detection of pulmonary nodules in low-dose CT images. Two distinct phases, aimed, respectively, at detecting volumes of interests (VOIs) within the CT scan, and at classifying VOIs into nodules and non-nodules, were considered. Three algorithms, namely thresholding, region growing and robust fuzzy clustering, were used as VOI detectors. For the classification phase, we built multi-classifier systems, which aggregate the decisions of three statistical classifiers, a neural network and a decision tree. Finally, the receiver operating characteristic convex hull method was used to build the final classifier, which results to be the aggregation of the best local behaviors of both classifiers and combiners. All the CAD modules were tested on CT scans analyzed by two expert radiologists. In the experiments, we achieved a sensitivity of 92.5% against a specificity of 83.5%.     

Improving the industrial classification of cork stoppers by using image processing and Neuro-Fuzzy computing

This paper presents a solution to a problem existing in the cork industry: cork stopper/disk classification according to their quality using a visual inspection system. Cork is a natural and heterogeneous (remarkable variability among different samples, being impossible to find two samples with the same morphological distribution in its defects) material; therefore, its automatic classification (seven quality classes exist) is very difficult. The solution proposed in this paper evaluates the following procedures: quality discriminatory features extraction and classifiers analysis. Each procedure focused on the study of aspects that could influence cork quality. Experiments show that the best results are obtained by system specific features: cork area occupied by defects (after thresholding), size of the biggest defect within the cork area (morphological operations), and the Laws TEMs E5L5TR, E5E5TR, S5S5TR, W5W5TR, all working on a Neuro-Fuzzy classifier. In conclusion, the results of this study represent an important contribution to improve quality control in the cork industry.     

基于小样本学习的LCD产品缺陷自动检测方法

针对高分辨率液晶显示器产品(liquid crystal display, LCD)质量在线检测需求,基于深度学习提出一种LCD缺陷自动检测方法。通过设计自适应浅层特征提取层,并引入稀疏卷积结构,多维度、多尺度的提取深层特征,采用迁移学习和深度卷积生成对抗生网络扩充数据强化训练,构建基于小样本学习的LCD表面缺陷检测模型。其特征在于,采用设计的自动分割与定位预处理软件将高分辨率图像划分成适于卷积神经网络学习的图像子块,并根据模型对图像子块的判定类别和定位坐标,同时获取多类型缺陷检测结果。实验结果表明,本文模型可以有效提高检出率,并减少漏检率。