Defective kernel detection using a linear colour CCD

Abstract

This study was aimed at detecting defective wheat (Triticum durum Desf) with a machine vision system of linear colour charge-coupled device. One thousand one hundred and sixty-nine images were captured for sound kernels, 710 for black germ kernels and 627 for broken kernels. A software package was developed to extract various morphological, colour and texture features from the images captured. Then the experimental data were subjected to multivariate analysis. Principal component analysis was employed to differentiate samples from different categories. Partial least square discriminant analysis and venetian blinds cross-validation were used to develop classification models. The best detection accuracies of samples were 92·7, 88·0 and 89·6% for black germ kernels, broken kernels and sound kernels. The results have proved that it is feasible and effective to employ partial least square discriminant analysis for feature selection and defective kernel detection.     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析(KPCA)在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。     

基于SVM的高通量dPCR基因芯片荧光图像分类研究

目的 为了实现高通量dPCR基因芯片荧光图像的亮点分类与计数,提出一种基于支持向量机（SVM）的荧光图像分类与计数方法。方法 首先对荧光图像进行去噪、对比度增强等图像预处理,对预处理后荧光图像进行亮点区域提取标注,去除背景与暗点的冗余信息,利用方向梯度直方图（Histogram of Oriented Gradient, HOG）提取鉴别特征,计算合并所有样本的亮点特征得到HOG特征向量,根据已得到的HOG特征向量创建一个线性SVM分类器,利用训练好的SVM分类器对荧光图像亮点进行分类与计数。结果 对比传统算法,文中算法具有较高的分类识别精度,平均准确率高达98%以上,可以很好地实现荧光图像亮点分类与计数。结论 在有限的小样本标注数据下,文中算法具有良好的分类性能,能够有效识别荧光图像中的亮点,对其他荧光图像分类研究也具有一定参考价值。     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

基于支持向量机的印品缺陷分类方法

目的研究印品图像的各类形状缺陷,建立基于支持向量机(Support vector machine,SVM)的印品形状缺陷分类模型。方法对印品进行符合人眼视觉特性的缺陷识别,并对提取缺陷进行特征分析。将特征数据导入支持向量机进行训练学习,SVM分类器对缺陷图像进行测试。结果分类器对点缺陷和面缺陷的识别率为100%,对线缺陷的分类准确率达93.94%。结论基于SVM的缺陷分类方法能较好地满足印品质量检测的需求。     

Magnetic resonance brain image classification based on weighted‐type fractional Fourier transform and nonparallel support vector machine

To classify brain images into pathological or healthy is a key pre‐clinical state for patients. Manual classification is tiresome, expensive, time‐consuming, and irreproducible. In this study, we aimed to present an automatic computer‐aided system for brain‐image classification. We used 90 T2‐weighted images obtained by magnetic resonance images. First, we used weighted‐type fractional Fourier transform (WFRFT) to extract spectrums from each magnetic resonance image. Second, we used principal component analysis (PCA) to reduce spectrum features to only 26. Third, those reduced spectral features of different samples were combined and were fed into support vector machine (SVM) and its two variants: generalized eigenvalue proximal SVM and twin SVM. A 5 × 5‐fold cross‐validation results showed that this proposed “WFRFT + PCA + generalized eigenvalue proximal SVM” yielded sensitivity of 99.53%, specificity of 92.00%, precision of 99.53%, and accuracy of 99.11%, which are comparable with the proposed “WFRFT + PCA + twin SVM” and better than the proposed “WFRFT + PCA + SVM.” Besides, all three proposed methods were superior to eight state‐of‐the‐art algorithms. Thus, WFRFT is effective, and the proposed methods can be used in practical. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 317–327, 2015     

Automated diagnosis system for age‐related macular degeneration using hybrid features set from fundus images

Impairment to macula can cause loss of central vision. There are various macular disorders that can affect macular region and if not treated at an early stage can cause irreversible central vision loss. Age‐related macular degeneration (AMD) disorder is one of the most threading macular disorder. Bright lesion, drusens presence in macular region is known as the hallmark of AMD disorder. This bright lesion differentiation from other bright lesion like exudates is important for accurate diagnosis of AMD. Focus of this article is automated diagnosis of affected macular region by applying a hybrid features set containing textural, color, and structural/shape features for more accurate detection of AMD at an early stage using fundus images. These features also help to distinguish drusens from exudates. The proposed algorithm at first stage, detect macular region from input fundus image and then perform features extraction based on textural pattern, edge, and structural properties of macular region to classify abnormal macula from normal macula. For classification, we have used support vector machine (SVM), K‐nearest neighbor and neural networks but SVM classifier achieves high accuracy. The proposed algorithm is tested on publicly available STARE and locally available AFIO datasets. Attained sensitivity, specificity, and accuracy of our proposed system are 97.5%, 95% and 95.45%, respectively, when applied on STARE dataset. When we have applied our proposed system on AFIO dataset, we have attained sensitivity, specificity, and accuracy of 93.3%, 92% and 92.34%, respectively.     

Earlier detection of cancer regions from MR image features and SVM classifiers

In this article, we examine the use of several segmentation algorithms for medical image classification. This work detects the cancer region from magnetic resonance (MR) images in earlier stage. This is accomplished in three stages. In first stage, four kinds of region‐based segmentation techniques are used such as K‐means clustering algorithm, expectation–maximization algorithm, partial swarm optimization algorithm, and fuzzy c‐means algorithm. In second stage, 18 texture features are extracting using gray level co‐occurrence matrix (GLCM). In stage three, classification is based on multi‐class support vector machine (SVM) classifier. Finally, the performance analysis of SVM classifier is analyzed using the four types of segmentation algorithm for a group of 200 patients (32—Glioma, 32—Meningioma, 44—Metastasis, 8—Astrocytoma, 72—Normal). The experimental results indicate that EM is an efficient segmentation method with 100% accuracy. In SVM, quadratic and RBF (σ = 0.5) kernel methods provide the highest classification accuracy compared to all other SVM kernel methods. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 196–208, 2016     

一种基于差影法及SVM的在线纸病检测分类方法

目的 解决目前纸病分类算法存在的实时性差、难以适应生产线在线检测要求等问题。方法 提出一种基于差影法和支持向量机的在线纸病检测分类方法。首先使用差影法来判断纸张是否含有纸病;对含有纸病的纸张进行打标机打标,同时存储图像,提取纸病区域外接矩形的特征向量;最后使用支持向量机对纸病进行分类。结果 将该方法与已有的BP神经网络以及朴素贝叶斯方法进行对比可知,分类正确率高于目前已有的分类方法,对于4种纸病的分类正确率均在90%以上,而且实时性好,更加适合于在线检测。结论 该方法可以有效地对纸病进行分类,满足生产线实时检测分类的要求。     

Plant Disease Diagnosis and Image Classification Using Deep Learning

Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest.     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.     

ECG Classification Using Deep CNN Improved by Wavelet Transform

Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.     

Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to develop a system for predicting COVID-19. In the first scenario, the Gaussian filter was applied to remove noise from the chest X-ray radiograph images, and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs. After segmentation, a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19. These features were processed using SVM and K-NN. In the second scenario, a CNN transfer model (ResNet 50) was used to detect COVID-19. The system was examined and evaluated through multiclass statistical analysis, and the empirical results of the analysis found significant values of 97.14%, 99.34%, 99.26%, 99.26% and 99.40% for accuracy, specificity, sensitivity, recall and AUC, respectively. Thus, the CNN model showed significant success; it achieved optimal accuracy, effectiveness and robustness for detecting COVID-19.     

Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Content-based video retrieval system aims at assisting a user to retrieve targeted video sequence in a large database. Most of the search engines use textual annotations to retrieve videos. These types of engines offer a low-level abstraction while the user seeks high-level semantics. Bridging this type of semantic gap in video retrieval remains an important challenge. In this paper, colour, texture and shapes are considered to be low-level features and motion is a high-level feature. Colour histograms convert the RGB colour space into YcbCr and extract hue and saturation values from frames. After colour extraction, filter mask is applied and gradient value is computed. Gradient and threshold values are compared to draw the edge map. Edges are smoothed for sharpening to remove the unnecessary connected components. These diverse shapes are then extracted and stored in shape feature vectors. Finally, an SVM classifier is used for classification of low-level features. For high-level features, depth images are extracted for motion feature identification and classification is done via echo state neural networks (ESN). ESN are a supervised learning technique and follow the principle of recurrent neural networks. ESN are well known for time series classification and also proved their effective performance in gesture detection. By combining the existing algorithms, a high-performance multimedia event detection system is constructed. The effectiveness and efficiency of proposed event detection mechanism is validated using MSR 3D action pair dataset. Experimental results show that the detection accuracy of proposed combination is better than those of other algorithms     

CCPSO优化支持向量机的鸟声识别技术研究

鸟类是生态系统中的重要组成部分，鸟类物种的多样性对生态环境有重要作用。所以，通过鸟声信号来识别鸟类从而对其进行保护有现实意义。文章对鸟声信号采用双参数的双门限法进行分段，从鸟声信号中寻找出声音的起始点和终止点的具体帧，进一步进行特征提取，提取每段鸟声信号中的短时能量和短时平均幅度，短时语谱图中的平均值、对比度、熵，共5种特征，采用优化参数的支持向量机进行鸟类物种分类。结果表明，基于混沌云粒子群优化(Chaos Cloud Particle Swarm Optimization, CCPSO)的支持向量机对比普通支持向量机的分类准确度得到提升，可有效地识别鸟类。利用该方法实现鸟类物种保护和生态系统管理的目的。     

Brain tumor severity analysis using modified multi‐texton histogram and hybrid kernel SVM

Magnetic resonance image (MRI) segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumor detection techniques are presented in the literature. In this article, we have developed an approach to brain tumor detection and severity analysis is done using the various measures. The proposed approach comprises of preprocessing, segmentation, feature extraction, and classification. In preprocessing steps, we need to perform skull stripping and then, anisotropic filtering is applied to make image suitable for extracting features. In feature extraction, we have modified the multi‐texton histogram (MTH) technique to improve the feature extraction. In the classification stage, the hybrid kernel is designed and applied to training of support vector machine to perform automatic detection of tumor region in MRI images. For comparison analysis, our proposed approach is compared with the existing works using K‐cross fold validation method. From the results, we can conclude that the modified multi‐texton histogram with non‐linear kernels has shown the accuracy of 86% but the MTH with non‐linear kernels shows the accuracy of 83.8%.     

Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features

Glaucoma is an eye disease in which the retinal nerve fibers are irreversibly damaged. Early identification of glaucoma is essential because it may slow the progression of the illness. The clinical treatments and medical imaging methods that are currently available are all manual and require expert supervision. An automated glaucoma diagnosis system that is fast, accurate, and helps to reduce the load on professionals is necessary for mass screening. In our proposed work, a novel approach based on bit-plane slicing (BPS), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) is used. First, fundus images are separated into channels like red, green, and blue, and these separated channels are split into plans using BPS. Then, LBP images are obtained from selected green channel images. Second, we extract features based on GLCM from LBP images. Finally, using a least-squares support vector machine classifier, the higher ranked features are employed to classify glaucoma stages. According to the findings of the experiments, our model outperformed state-of-the-art approaches for glaucoma classification. Using 10-fold cross-validation, this model achieved an improved classification accuracy of 95.04%, specificity of 96.37%, and sensitivity of 93.77%. We conducted many relative experiments with deep learning and traditional machine learning-based models to test our proposed methodology. Compared to existing glaucoma classification approaches, the new method has been shown to be more efficient.     

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％.