Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

Fault detection in flotation processes based on deep learning and support vector machine

Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

Saudi License Plate Recognition Algorithm Based on Support Vector Machine

-License plate recognition （LPR） is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine （SVM） algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Computer‑aided CT image processing and modeling method for tibia microstructure

We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.

利用遥感技术对湿地变化进行绿色金融分析

利用卫星遥感技术对哈尔滨市群力外滩生态湿地进行了信息提取和分析,评估了该湿地近几年水体和绿地面积的变化情况,探讨了开展绿色金融对该湿地生态保护发展的影响。通过目视解译与实地考察获取训练样本和验证点,采用最小距离法(Minimum distance,MD)、最大似然法(Maximum likelihood,ML)和支持向量机(Support vector machine,SVM)进行监督分类并对比其精度,选择更适合北方湿地信息提取的支持向量机的方法进行湿地分类。结合该湿地的生态特征,分析了绿色金融对该湿地的发展潜力和影响因素,并提出了具体的绿色金融策略和建议。研究结果表明,群力外滩生态湿地的水体和绿地面积在近几年稳步增长,说明该湿地保护和修复工作效果显著。

基于少量缺陷样本的带钢表面缺陷检测方法

带钢在生产过程中受到工艺或设备等因素的影响,在表面产生了压伤、滴焦油等缺陷。提出了一种只需要少量的缺陷样本来训练生成对抗网络模型。模型通过学习大量的无缺陷图像数据分布特征,再引入少部分的缺陷图像来进行对比,模型可以学习和掌握无缺陷图像和有缺陷图像的分布情况,更有利于缺陷检测。为了使模型稳定,在损失函数中添加抽离项和多样性比因子,并加入优化特征的范数来稀疏模型,从而减少无用特征的干扰。提出的方法对带钢表面缺陷检测的精度达到了95%。

热损伤玉米种子的高光谱无损检测

玉米是世界主要粮食作物之一,使用不符合国家标准的劣质种子将严重影响玉米作物产量,如何快速准确高效鉴别劣质玉米种子亟待解决。采用高光谱图像系统获取900粒“豫安三号”玉米种子的900~1700 nm光谱曲线,其中训练集和测试集比例为3∶2,分别为540粒和360粒。利用电鼓风式烘干箱对种子损伤处理,获得不同损伤程度的玉米种子样本,采集光谱后完成发芽试验,以此判别种子活力。为提高信噪比,截取963.27~1698.75 nm范围内的玉米种子光谱波段作为有效波段;采用标准正态变换(SNV)、多元散射校正(MSC)两种预处理方式对原始光谱数据预处理,并采用连续投影算法(SPA)、竞争性自适应重加权算法(CARS)两种特征波段提取算法对预处理后的光谱数据提取特征波段,波长反射率作为输入矩阵X,预设样本类别作为输出矩阵Y;最后采用支持向量机(SVM)模型建模分析,研究结果表明:MSC-CARS-SVM模型为最佳模型,模型识别成功率为98.33%,其Kappa系数为0.985。在此基础上,采用遗传算法(GA)对SVM中惩罚系数c和核函数参数g寻优,模型准确率提升至100%,可实现对热损伤劣质玉米种子的鉴别。该研究为劣质玉米种子及其他作物种子快速鉴别提供了新思路和方法。

基于改进LeNet-5网络的堆芯燃料组件编码识别

在核电站堆芯核燃料组件水下组装作业中,需要通过视觉技术进行组件编码的识别以便准确定位组件的安装位置。针对水下环境中弱光照等问题导致了图像质量的降低,本文通过乘方增强算法、OSTU算法、CLAHE算法和拉普拉斯变换的方法来实现堆芯燃料组件编码字符水下图像的增强。为了提高编码识别效果,提出了一种整合LeNet-5网络和支持向量机(SVM)的模型,在网络中添加BN(Batch Normalization)层与Dropout层来加速网络的运行速度,并改进Sigmoid函数,增加函数的平滑性,以此来减少梯度消失。实验表明,在自定义数据集上的验证准确率为99.82%,识别率为100%,相比于其他模型有显著的提升。

基于图像处理的钢材表面缺陷检测方法

由于钢材生产过程中因环境光的存在、光源照明不均匀等外界环境的干扰,采集到的图像容易产生光照不均匀、缺陷特征不明显以及噪声大等问题,故提出了一种基于图像处理的钢材表面缺陷检测方法。采用CLAHE算法突出缺陷特征以及双边滤波算法去除噪声;采用改进的Bottom-hat算法,增强缺陷特征与背景的对比度;采用基于LBP与支持向量机SVM相结合的方法进行钢材表面缺陷检测。结果表明,此方法对钢材表面缺陷测试集检测的准确率为81.67%,与改进之前相比提高了9.67%,能够对钢材表面缺陷进行有效检测。

肝豆状核变性患者默认模式网络变化及模型构建

目的:基于独立成分分析(ICA),探讨肝豆状核变性(WD)患者默认模式网络(DMN)功能连接(FC)的改变及其与临床神经精神特征之间的关系。方法:将2021年1月-2021年12月在本院就诊的85例肝豆状核变性患者和年龄、性别相匹配的85例健康志愿者(HC组)纳入本研究。对每例被试采用统一肝豆状核变性评分量表(UWDRS)进行评估,包括神经功能症状(UWDRS-N)和精神症状(UWDRS-P)评分,并根据UWDRS评分将患者分为神经精神症状重度组(>10分)和轻度组(≤10分)。使用3.0T磁共振BOLD序列采集静息态fMRI数据,采用ICA方法提取DMN内各体素的FC值,并在HC组与WD组之间进行比较,对有差异脑区的FC值与临床量表评分进行Pearson相关性分析。以DMN内所有体素的FC值为特征变量采用支持向量机(SVM)的方法构建分类模型,包括正常组与WD组以及轻度与重度WD组。结果:与对照组比较,WD组的DMN内表现出广泛的FC值减低,包括前默认模式网络(aDMN)内的左内侧前额叶皮层(L_MPFC)和左侧前扣带回(L_ACC),以及后DMN(pDMN)内的左侧角回(L_ANG)、楔前叶(PCUN)、左侧顶下小叶(L_IPG)和左侧后扣带回(L_PCC)。aDMN内的L_MPFC、L_ACC和pDMN内的PCUN的FC值与UWDRS-N评分呈负相关,aDMN内的L_MPFC、L_PCC和pDMN内的L_IPG的FC值与UWDRS-P评分呈负相关。采用SVM构建的二分类器,在鉴别WD与HC组时的符合率为80.23%,AUC为0.865;在鉴别轻度与重度WD组的符合率为70.89%,AUC为0.723。结论:WD患者的默认模式网络中存在广泛的功能连接减低,其可能是导致患者出现神经精神症状(如高阶认知障碍)的潜在神经病理机制。基于DMN的FC值构建的SVM分类器可提高对WD疾病及其病情转归的评估效能。

基于机器视觉技术的纸病智能检测算法研究

机器视觉技术在工业领域尤其是缺陷检测方面展现出显著的应用潜力,其不仅能够有效提升检测的精确度和效率,还在降低人力成本方面发挥着不可忽视的作用。为此,通过探索机器视觉技术在纸张缺陷检测上的应用实例,深入分析了其面临的挑战及未来发展方向。详细讨论了基于机器视觉的纸张缺陷智能检测算法,重点涵盖了图像的预处理、特征参数的提取,并对复杂纸病的识别技术进行了深入研究。通过对特定纸病样本的训练与测试,以及对支持向量机多分类器的优化,对几类典型纸病的检测分类实验,不仅证实了所提算法的实用性,还展现了其高效的检测能力。

基于图像处理和SVM的定位基准分类研究

为解决机器人自动化制孔过程中多特征定位基准分类的问题,本文提出一种基于图像处理和SVM的定位基准分类方法,对包含多特征定位基准的图像进行基于全局最优阈值的基准ROI区域分割,计算基准特征的轮廓统计方差、轮廓圆度和特征区域灰度均值,并作为描述定位基准的特征参数,结合SVM实现定位基准的识别分类。结果表明:该方法对定位基准的识别准确率为94.3%,召回率为95.9%,对机器人自动钻孔全流程自动化有一定的应用价值。

近邻密度辅助模糊优化孪生支持向量机的钢板表面缺陷分类

为提升钢板表面缺陷分类精度,提出一种选择性弱化样本的分类模型。首先,在图像预处理阶段引入显著性检测算法来减少二值化后图像出现失真的影响;其次,为了降低不利的边缘样本点对模型的影响,同时又能提高有利的边缘样本点对模型的贡献,构造了一种新的密度模糊隶属度函数对样本进行权重赋值;最后,在孪生支持向量机(TWSVM)的基础上,将构造的密度模糊隶属度函数作为优化条件嵌入模型内,提出了近邻密度辅助模糊优化的TWSVM算法,以提高分类效果。在数据集NEU上的实验结果表明,引入显著性检测算法后,重新设计的特征在整体准确率上提高了1.66%,同时采用优化后的算法进行缺陷分类,准确率达到98.33%,进一步提高了分类性能。

基于图像识别的输电线路状态智能监测研究

为输电线路安全运行提供基础,提出基于图像识别的输电线路状态智能监测方法。利用多尺度Retinex算法对输电线路图像增强处理,提升输电线路图像质量,利用Mallat多尺度小波变换方法提取输电线路图像边缘的小波系数特征,选取模糊支持向量机方法依据小波系数特征进行实现输电线路状态智能监测。实验结果表明,该方法处理后输电线路图像信息熵高,可以有效识别输电线路的绝缘子覆冰、输电导线舞动等异常状态,输电线路状态智能监控效果理想。

Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

Abnormal characterization of dynamic functional connectivity in Alzheimer’s disease

Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer’s disease(AD)or amnestic mild cognitive impairment(aMCI).However,most studies examined traditional resting state functional connections,ignoring the instantaneous connection mode of the whole brain.In this case-control study,we used a new method called dynamic functional connectivity(DFC)to look for abnormalities in patients with AD and aMCI.We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant,and then used a support vector machine to classify AD patients and normal controls.Finally,we highlighted brain regions and brain networks that made the largest contributions to the classification.We found differences in dynamic function connectivity strength in the left precuneus,default mode network,and dorsal attention network among normal controls,aMCI patients,and AD patients.These abnormalities are potential imaging markers for the early diagnosis of AD.

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases.