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- 摘要
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- 实验方案
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[Lecture Notes in Electrical Engineering] Recent Trends in Communication, Computing, and Electronics Volume 524 (Select Proceedings of IC3E 2018) || Classification of Normal and Abnormal Retinal Images by Using Feature-Based Machine Learning Approach
摘要： The human eye is one of the most beautiful and important sense organs of human body as it allows visual perception by reacting to light and pressure. Human eyes are capable of differentiating approximately 10 million colors. It contains more than 2 million tissues and cells. Along with these entire specialties, human eyes are the most delicate and sensitive organ. If not taken proper care, it may be infected with various diseases like glaucoma, myopia, hyper-myopia, diabetic retinopathy, age-related macular disease. Therefore, early-stage detection of these diseases could help in curing them completely and prevent from complete blindness. In this paper, we propose an approach to classify the normal (healthy) and abnormal (disease-infected) retinal images by using retinal image feature-based machine learning classification approach. The performance of proposed approach by using SVM classifier is 77.3%, which is found better with respect to the other classifiers like k-NN, linear discriminant, quadratic discriminant and decision tree classifiers.
关键词： Machine learning and classification,Texture features,Retina images
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Feature-Level Loss for Multispectral Pan-Sharpening with Machine Learning
摘要： Multispectral pan-sharpening plays an important role in providing earth observation with both high-spatial and high-spectral resolutions, and recently pan-sharpening with machine learning has been attracting broad interest. However, these algorithms minimizing the pixel-wise mean squared error, generally suffer from over-smoothed results that lack of high-frequency details in both spatial and spectral dimensions. In this paper, we propose to tackle this problem by shifting the learning loss from pixel-wise error to a higher-level feature loss. The new loss function, formulated by spatial structure similarity and spectral angle mapping, pushes the model to generate results that have similar feature representations with ground truth, rather than match with pixel-wise accuracy. Consequently, more realistic fusion results can be produced. Visual and quantitative analysis both demonstrate that our approach achieves better performance in comparison with state-of-the-art algorithms. Furthermore, experiments on high-level remote sensing task further confirm the superiority of the proposed method in real applications.
关键词： Spectral Angle Mapping,Spatial Structure Similarity,Multispectral Pan-sharpening,Feature-Level Loss,Machine Learning
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Weld Classification Using Gray Level Co-Occurrence Matrix and Local Binary Patterns
摘要： This paper presents an algorithm that can classify weld seams from images, exploiting machine learning techniques. Manual visual inspection is the primary way of evaluating weld seams, in cases where the primary goal is to keep inspection costs low. Such, visual inspections entail manual interpretation and evaluation, which are both time consuming and the result often depends on the person assigned to the task. These drawbacks render automatic visual inspection appealing. Thus, this paper seeks to find a possible solution for the visual inspection of welds, where two feature extraction methods are examined and tested in conjunction with two different classifiers. We investigate whether visual inspection based on texture-describing features, processed with a machine learning algorithm, can detect flaws and defects in a weld merely by inspecting the surface of the object, in a way similar to how human eyes detect them and we achieve 96% classification accuracy on a new dataset.
关键词： machine learning,visual inspection,Gray Level Co-Occurrence Matrix,weld classification,Local Binary Patterns
更新于2025-09-09 09:28:46
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Machine Learning Based PML for the FDTD Method
摘要： In this paper, a novel absorbing boundary condition (ABC) computation method for Finite-Difference Time-Domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF based PML model. Compared to the conventional approach, the novel method greatly decreases the size of computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark the performance of the proposed method. The results demonstrate that the newly proposed method could replace conventional PML and could be integrated into FDTD solving process with satisfactory accuracy and compatibility to FDTD. According to our knowledge, this proposed model combined ANN model is an unreported new approach based on machine learning based for FDTD.
关键词： PML,machine learning,FDTD,HTBF neural network
更新于2025-09-09 09:28:46
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Vision-based Bed Detection for Hospital Patient Monitoring System
摘要： In recent years, as a way to prevent patient fall-down, studies have been conducted using patient room cameras to detect the patient behavior of leaving the bed. It is very important to specify the patient bed location in the process of detecting patient behavior using camera images. In this study, we propose a method to specify the patient bed location using a monocular camera. In this proposal, we convert a camera image viewpoint into a bird’s-eye view image as a preprocessing step. By using planer perspective transformation, it is possible to display the bed as a rectangular shape with a fixed ratio, even if the bed location or camera position is changed. Therefore, it is possible to detect the bed location with a high degree of accuracy by means of machine learning. The simulation experiment re-sults confirm that the average error and standard deviation of the bed coordinates are 7.9 and 5.0 pixels, respectively; in the practical scene, we confirm that the average error and standard deviation of the bed coordinates are 12.1 and 8.2 pixels, respec-tively.
关键词： machine learning,bed detection,patient monitoring,monocular camera,planar perspective transformation
更新于2025-09-09 09:28:46
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Efficient Shaped-Beam Reflectarray Design Using Machine Learning Techniques
摘要： This papers introduces the use of machine learning techniques for an ef?cient design of shaped-beam re?ectarrays considerably accelerating the overall process while providing accurate results. The technique is based on the use of Support Vector Machines (SVMs) for the characterization of the re?ection coef?cient matrix, which provides an ef?cient way for deriving the scattering parameters associated with the unit cell dimensions. In this way, the SVMs are used within the design process to obtain a re?ectarray layout instead of a Full-Wave analysis tool based on Local Periodicity (FW-LP). The accuracy of the SVMs is assessed and the in?uence of the discretization of the angle of incidence is studied. Finally, a considerable acceleration is achieved with regard to the FW-LP and other works in the literature employing Arti?cial Neural Networks.
关键词： Machine Learning,Support Vector Machine (SVM),re?ectarray
更新于2025-09-04 15:30:14
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Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers
摘要： The purpose of this article is to highlight best practices for writing and reviewing articles on artificial intelligence for medical image analysis. Artificial intelligence is in the early phases of application to medical imaging, and patient safety demands a commitment to sound methods and avoidance of rhetorical and overly optimistic claims. Adherence to best practices should elevate the quality of articles submitted to and published by clinical journals.
关键词： machine learning,artificial intelligence,deep learning,technology assessment
更新于2025-09-04 15:30:14
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Deep learning—Using machine learning to study biological vision
摘要： Many vision science studies employ machine learning, especially the version called ‘‘deep learning.’’ Neuroscientists use machine learning to decode neural responses. Perception scientists try to understand how living organisms recognize objects. To them, deep neural networks offer benchmark accuracies for recognition of learned stimuli. Originally machine learning was inspired by the brain. Today, machine learning is used as a statistical tool to decode brain activity. Tomorrow, deep neural networks might become our best model of brain function. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions. Here, we hope to help vision scientists assess what role machine learning should play in their research.
关键词： object recognition,neural networks,deep learning,machine learning
更新于2025-09-04 15:30:14
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[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Long-Term and Short-Term Atmospheric Impairments Forecasting for High Throughput Satellite Communication Systems
摘要： In this paper, three different methodologies are employed for the prediction of atmospheric attenuation for the performance evaluation of High Throughput Satellite Communication systems. The first one is based on numerical weather predictions and in particular the ECMWF forecasts that uses high resolution deterministic forecast and the probabilistic forecasts the perturbated products. The second methodology is based machine learning algorithms, which are advanced statistical methods. The two algorithms tested in this study are the random forest and the gradient boosting, both based on regression trees. Finally, the last method that is employed is the recurrent neural networks and in particular the Long Short Term Memory. These neural networks are used for the prediction of time series using memory blocks. All the algorithms are tested using data from the ALPHASAT experiment at Chilbolton and Chilton, UK. The obtained results are very encouraging.
关键词： radiowave propagation,forecast,Ka band and above,Satellite Communications,deep learning,machine learning
更新于2025-09-04 15:30:14
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Deep Learning in Image Cytometry: A Review
摘要： Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data.
关键词： image cytometry,machine learning,biomedical image analysis,convolutional neural networks,deep learning,cell analysis,microscopy
更新于2025-09-04 15:30:14