Image Feature Extraction Algorithm Research Articles

Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches

This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features into SHSIs, providing insights into the spectrum. This method enables the image analysis to perform assessments from SHSIs for health classification using Normalized Difference Vegetation Index (NDVI) values, which are meticulously compared with accurate hyperspectral data using metrics like mean absolute error (MAE) and root mean squared error (RMSE). This strategy enhances precision farming, environmental monitoring, and remote sensing accuracy. Results show that ResNet50’s architecture offers a robust framework for this study’s spectral and spatial analysis, making it a suitable choice over VGG16 and EfficientNet-B7 for transforming RGB images into SHSI. These simulated hyperspectral images offer a scalable and affordable approach for precise assessment of crop disease severity.

Intelligent mobile robot for contagious disease treatments in hospitals

This paper provides a novel and applicable work that builds a real system for disinfecting the air and surfaces of the environment in a hospital room, with a non-contact measurement system for supporting contagious disease treatments in hospitals. The system is built on an intelligent mobile robot system that operates autonomously in a simulated real treatment room. The research team uses a new positioning algorithm. It is a combination of data from the Lidar sensor, encoder, and Extended Kalman filter. The program that applies segmentation and image feature extraction algorithms is developed to meet requirements of real-time environment mapping in the room. Control algorithms for moving and avoiding obstacles are also proposed. Next, techniques for collecting health data including patient identification, body temperature, and blood oxygen index via wireless sensor network are also mentioned in the article. Analysis and experimental results show qualified outcomes and promise. The main contribution of the paper can be listed as follows.•Design and build a new CEE-IMR, an intelligent mobile robot that can regconize patients, guide and lead them walking in hospitals, especially keep a safe distance avoiding contagious deseases.•A novel framework for controlling the robot is proposed. The robot can move flexible, avoid obstacles, etc. based on advanced control algorithms. A new control mechanism is also proposed.•Methods of collecting data and processing medical data to support either patients or doctors to improve the effecency in hospitals in contagious disease management.

Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis.

An Image Feature Extraction Algorithm Based on Tissue P System

As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence.

Design and development of an effective classifier for medical images based on machine learning and image segmentation

Recently, there has been an increase in the death rate due to encephaloma tumours affecting all age groups. Because of their intricate designs and the interference they cause in diagnostic imaging, these tumours are notoriously difficult to spot. Early and accurate detection of tumours is crucial because it allows for identifying and predicting malignant regions using medical imaging. Using segmentation and relegation techniques, medical scans can aid clinicians in making an early diagnosis and potentially save time. On the other hand, the identification of tumours may be a laborious and extended process for professional doctors owing to the complex nature of tumour formations and the presence of noise in the data produced by Magnetic Resonance Imaging (MRI) since it is pretty imperative to locate and determine the site of the tumour as quickly as feasible. This research proposes a method for detecting brain cancers from MRI scans based on machine learning. It uses the Support Vector Machine, K Nearest Neighbor, and Nave Bayes algorithms for image preprocessing, picture segmentation, feature extraction, and classification. According to the findings, the SVM algorithm accomplished the best level of accuracy, which is 89 %.

Machine Learning Application for Concrete Surface Defects Automatic Damage Classification

Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and complicated, requiring access to many components of a large project design. Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from the digital images. The aim of this research is to increase the efficiency of concrete surface defect analysis in terms of quality, time and cost. 200 images were collected, with 50 images for each concrete defect (crack, corrosion, spalling, and no defect) serving as control data. The Gray Level Co-Occurrence Matrix (GLCM) is used to create an image processing and feature extraction algorithm. This model is trained using 80% of the image data and tested using another 20% of the image data. Thus, the model achieved 95% accuracy on the training data and 70% on the test data when using Quadratic Discriminant Analysis. These findings is very important to help engineers or construction inspectors in inspection activities.

A COMPREHENSIVE REVIEW ON SUITABLE IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUE FOR DISEASE IDENTIFICATION OF TOMATO AND POTATO PLANTS

Agriculture plays a vital role in the Sri Lankan economy. Cultivation of crops like tomatoes and potatoes which is being used as a fruit and vegetable will contribute significantly to farmer’s earnings. However, tomato and potato crop faces numerous challenges, such as disease infection can significantly reduce the yield. Early identification of these diseases is crucial for implementing timely interventions and minimizing the potential damage. The current study aims to analyze existing methodologies and identify the most effective approaches for disease detection in tomato and potato crops. Image processing techniques enable the extraction of relevant features from digital images of infected plants, aiding in the identification of diseases accurately. Additionally, machine learning algorithms have proven to be valuable tools for analyzing complex datasets and distinguishing between healthy and diseased plants. The review explores various image processing techniques, including image segmentation, feature extraction, and classification algorithms (support vector machines, random forests, and convolutional neural networks). Suitability of these techniques assured that the disease identification in tomato plants based on their accuracy, efficiency, and robustness. The findings of this review will serve as a foundation for the development of a software application to identify tomato leaf diseases accurately. By enabling accurate disease identification and management, this study seeks to enhance the resilience and productivity of tomato and potato cultivation in Sri Lanka, contributing to the sustainable growth of the agricultural sector.

Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection

Alongside the development of technology to facilitate multi-family security, security tools are also being developed. Smart home security is one of the very popular security tools in Indonesian home construction. The tool works automatically in real time and has no restrictions on environmental conditions. However, currently available tools still lack consistent accuracy and consistent performance. To solve this problem, the author proposes a smart home security system with an Arduino UNO-connected camera, two relay modules, a magnetic lock, and connecting to a home Internet of Things system. The methods used in the research for this thesis project were: 1. Literature review of ongoing Smart Home Security using facial image feature extraction algorithm research; 2. Deployment of Arduino UNO, 2 Relay Module, and Solenoid Lock; 3. The feature extraction algorithm used is Wavelet. The proposed method is expected to achieve an accuracy of 80% or more. The experimental results showed that the proposed prototype of this experiment achieved the accuracy of 85.7%. In addition to accuracy, there is also precision rate at 87.94%, recall rate at 87.56%, and f1-score rate at 87.28%

Interior Space Design Method Considering Image Feature Extraction Algorithms

Interior Space Design Method Considering Image Feature Extraction Algorithms

Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis

Abstract With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.

Image feature extraction algorithm based on visual information

Abstract Vision is the main sensory organ for human beings to contact and understand the objective world. The results of various statistical data show that more than 60% of all ways for human beings to obtain external information are through the visual system. Vision is of great significance for human beings to obtain all kinds of information needed for survival, which is the most important sense of human beings. The rapid growth of computer technology, image processing, pattern recognition, and other disciplines have been widely applied. Traditional image processing algorithms have some limitations when dealing with complex images. To solve these problems, some scholars have proposed various new methods. Most of these methods are based on statistical models or artificial neural networks. Although they meet the requirements of modern computer vision systems for feature extraction algorithms with high accuracy, high speed, and low complexity, these algorithms still have many shortcomings. For example, many researchers have used different methods for feature extraction and segmentation to get better segmentation results. Scale-invariant feature transform (SIFT) is a description used in the field of image processing. This description has scale invariance and can detect key points in the image. It is a local feature descriptor. A sparse coding algorithm is an unsupervised learning method, which is used to find a set of “super complete” basis vectors to represent sample data more efficiently. Therefore, combining SIFT and sparse coding, this article proposed an image feature extraction algorithm based on visual information to extract image features. The results showed that the feature extraction time of X algorithm for different targets was within 0.5 s when the other conditions were the same. The feature matching time was within 1 s, and the correct matching rate was more than 90%. The feature extraction time of Y algorithm for different targets was within 2 s. The feature matching time was within 3 s, and the correct matching rate was between 80 and 90%, indicating that the recognition effect of X algorithm was better than that of Y algorithm. It indicates the positive relationship between visual information and image feature extraction algorithm.

Dynamic Interactive Design of Graphic Design Elements Based on Image Feature Extraction Algorithm

Abstract This paper first analyzes the use and expression of line and surface, color, and text as graphic design composition elements. Secondly, it analyzes the relationship between interaction design and other disciplines and constructs a dynamic interactive design model for graphic design. Then, the features of color, texture, shape and spatial relationship of the image are extracted by the feature extraction algorithm, and the gesture recognition algorithm is used to determine the operation requested by the user and execute it. Finally, data analysis was conducted on 32 randomly selected students from the design school of University H who applied the dynamic interactive design model. The results show that the dynamic interactive design has the highest rating in terms of smooth operation, with an average score of 6.893, and the lowest rating is in terms of low learning cost, with an average score of 5.087. This shows that the dynamic interactive design model of graphic elements can improve the smoothness of operation, thus improving the dynamic interactive experience of students and providing direction for the development of the field of graphic design.

ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference.

Simultaneous Localization and Mapping (SLAM) technology is crucial for achieving spatial localization and autonomous navigation. Finding image features that are representative presents a key challenge in visual SLAM systems. The widely used ORB (Oriented FAST and Rotating BRIEF) algorithm achieves rapid image feature extraction. However, traditional ORB algorithms face issues such as dense, overlapping feature points, and imbalanced distribution, resulting in mismatches and redundancies. This paper introduces an image feature extraction algorithm called Adaptive Threshold and Local Gray Difference-ORB(ALGD-ORB) to address these limitations. Specifically, an adaptive threshold is employed to enhance feature point detection, and an improved quadtree method is used to homogenize feature point distribution. This method combines feature descriptors generated from both gray size and gray difference to enhance feature descriptor distinctiveness. By fusing these descriptors, their effectiveness is improved. Experimental results demonstrate that the ALGD-ORB algorithm significantly enhances the uniformity of feature point distribution compared to other algorithms, while maintaining accuracy and real-time performance.

Finger Vein Recognition Technology: Principles, Applications, and Future Prospects

Finger vein recognition technology is a biometric identification technique that analyzes the vein patterns in the fingers to authenticate individuals. This paper aims to provide a comprehensive overview of the principles, applications, and future prospects of finger vein recognition technology. Firstly, the unique biological characteristics and advantages of finger vein recognition will be explored. Secondly, the working principle and key techniques of the finger vein recognition system will be introduced, covering image acquisition, feature extraction, and matching algorithms. Subsequently, the extensive applications of finger vein recognition technology in personal identity authentication, financial payments, information security, and other domains will be discussed. Finally, the future development of finger vein recognition technology will be forecasted, addressing potential challenges and solutions, as well as its potential applications in broader domains.

Information Extraction and Passport Recognition

Abstract: The passport recognition and information extraction project aims to develop a software system that can automatically recognize and extract key information from passports. The system will utilize computer vision techniques to process passport images and extract important data such as the passport holder's name, date of birth, passport number, and expiration date. The extracted information will then be used to populate a database or other software systems for further processing. The project will involve the use of deep learning algorithms for image recognition and feature extraction, as well as natural language processing techniques for text extraction and analysis. The system will be designed to operate accurately and efficiently across a range of different passport types and will be suitable for use in a variety of applications, including border control, identity verification, and travel management.

A classification model of breast masses in DCE-MRI using kinetic curves features with quantum-Raina’s polynomial based fusion

Breast cancer is the most common cancer among women worldwide and is currently the second most common cancer-related death in women. However, early detection and diagnosis of breast cancer can lead to a complete remission and can extend survival periods. Dynamic Contrast Enhanced MRI (DCE-MRI) is being increasingly used in early detection and characterization of breast lesions. It has higher accuracy in breast cancer detection than other imaging modalities currently utilized in breast imaging as it offers better insight into tumor morphology and microenvironment. The most important problem is that earlier research did not consider breast cancer characteristics’ features that might be useful for accurately identifying breast malignancy and differentiating it from the more common benign breast pathologies. The use of recently developed image feature extraction algorithms in conjunction with radiological imaging helps in the diagnosis and categorization of breast cancer. With this view, the current study presents a novel fusion model using the kinetic curves features with a proposed quantum-Raina’s polynomial features for the breast cancer classification in MRI. This study uses two approaches for features extractions of DCE-MRI scans of the breast, namely, kinetic and QRP features.The excellent performance of the fusion extracted features improves the classification accuracy of breast into benign and malignant lesions. The maximum achieved accuracy for classifying a dataset comprising 300 DCE-MRI breast scans is 97.4%. The accuracy of the proposed classification model is significant with a low complexity rate.

Image feature extraction algorithm based on parameter adaptive initialisation of CNN and LSTM

Image feature extraction algorithm based on parameter adaptive initialisation of CNN and LSTM

Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia

BackgroundGlaucoma is one of the major causes of blindness; it is estimated that over 110 million people will be affected by glaucoma worldwide by 2040. Research on glaucoma detection using deep learning technology has been increasing, but the diagnosis of glaucoma in a large population with high incidence of myopia remains a challenge. This study aimed to provide a decision support system for the automatic detection of glaucoma using fundus images, which can be applied for general screening, especially in areas of high incidence of myopia.MethodsA total of 1,155 fundus images were acquired from 667 individuals with a mean axial length of 25.60 ± 2.0 mm at the National Taiwan University Hospital, Hsinchu Br. These images were graded based on the findings of complete ophthalmology examinations, visual field test, and optical coherence tomography into three groups: normal (N, n = 596), pre-perimetric glaucoma (PPG, n = 66), and glaucoma (G, n = 493), and divided into a training-validation (N: 476, PPG: 55, G: 373) and test (N: 120, PPG: 11, G: 120) sets. A multimodal model with the Xception model as image feature extraction and machine learning algorithms [random forest (RF), support vector machine (SVM), dense neural network (DNN), and others] was applied.ResultsThe Xception model classified the N, PPG, and G groups with 93.9% of the micro-average area under the receiver operating characteristic curve (AUROC) with tenfold cross-validation. Although normal and glaucoma sensitivity can reach 93.51% and 86.13% respectively, the PPG sensitivity was only 30.27%. The AUROC increased to 96.4% in the N + PPG and G groups. The multimodal model with the N + PPG and G groups showed that the AUROCs of RF, SVM, and DNN were 99.56%, 99.59%, and 99.10%, respectively; The N and PPG + G groups had less than 1% difference. The test set showed an overall 3%–5% less AUROC than the validation results.ConclusionThe multimodal model had good AUROC while detecting glaucoma in a population with high incidence of myopia. The model shows the potential for general automatic screening and telemedicine, especially in Asia.Trial registration: The study was approved by the Institutional Review Board of the National Taiwan University Hospital, Hsinchu Branch (no. NTUHHCB 108-025-E).

LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

Identifying and detecting the loading size of heavy-duty railway freight cars is crucial in modern railway freight transportation. Due to contactless and high-precision characteristics, light detection and ranging-assisted unmanned aerial vehicle stereo vision detection is significant for ensuring out-of-gauge freight transportation security. However, the precision of unmanned aerial vehicle flight altitude control and feature point mismatch significantly impact stereo matching, thus affecting the accuracy of railway freight measurement. In this regard, the altitude holding control strategy equipped with a laser sensor and SURF_rBRIEF image feature extraction and matching algorithm are proposed in this article for railway freight car loading size measurement. Moreover, an image segmentation technique is used to quickly locate and dismantle critical parts of freight cars to achieve a rapid 2-dimension reconstruction of freight car contours and out-of-gauge detection. The robustness of stereo matching has been demonstrated by external field experiment. The precision analysis and fast out-of-gauge judgment confirm the measurement accuracy and applicability.

Application of CAD Technology in Extracting Line Feature of Industrial Part Image

In order to solve the problem that the properties of the acquired images in engineering are often poor and the features of the images are seriously lost, so that the image features cannot be effectively developed, an application method of CAD technology in the extraction of linear features of industrial parts images is proposed. Based on 3D computer-aided design (CAD) data, by solving the parameter transformation relationship between the CAD 3D Cartesian coordinate system and the image coordinate system, the target line feature is projected onto the image, and the initial value range of the image line feature is obtained. The edge point sequence is extracted by multiplication template matching, and the generated point sequence is fitted by the least square method to obtain the line equation of the image line feature. By solving the intersection of the intersecting lines of the image, the straight line segment representing the contour of the industrial part can be obtained with high precision. The experimental results show that the above algorithm is used to extract the line features of real industrial part images, and the median error of the difference between the reconstructed line length and the line length measured by the vernier caliper is 0.232 mm. The practice has proved that CAD technology has a certain auxiliary and guiding role in the research of industrial part image feature extraction algorithms. It can be applied to high-precision image measurement and reverse engineering of regular industrial parts. In engineering technology, it can help improve the automation level of industrial production.