- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Adaptive SVD Domain-Based White Gaussian Noise Level Estimation in Images
摘要： Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level σ = 15, the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time.
关键词： artificial neural networks,singular value decomposition,image analysis,noise level estimation,Digital images,AWGN,least square methods
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) - Vilnius, Lithuania (2018.11.8-2018.11.10)] 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) - Deep Neural Network-based Feature Descriptor for Retinal Image Registration
摘要： Feature description is an important step in image registration workflow. Discriminative power of feature descriptors affects feature matching performance and overall results of image registration. Deep Neural Network-based (DNN) feature descriptors are emerging trend in image registration tasks, often performing equally or better than hand-crafted ones. However, there are no learned local feature descriptors, specifically trained for human retinal image registration. In this paper we propose DNN-based feature descriptor that was trained on retinal image patches and compare it to well-known hand-crafted feature descriptors. Training dataset of image patches was compiled from nine online datasets of eye fundus images. Learned feature descriptor was compared to other descriptors using Fundus Image Registration dataset (FIRE), measuring amount of correctly matched ground truth points (Rank-1 metric) after feature description. We compare the performance of various feature descriptors applied for retinal image feature matching.
关键词： artificial neural networks,biomedical imaging,machine learning,image registration,retinal images,feature descriptors
更新于2025-09-23 15:22:29
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A review of image-based automatic facial landmark identification techniques
摘要： The accurate identification of landmarks within facial images is an important step in the completion of a number of higher-order computer vision tasks such as facial recognition and facial expression analysis. While being an intuitive and simple task for human vision, it has taken decades of research, an increase in the availability of quality data sets, and a dramatic improvement in computational processing power to achieve near-human accuracy in landmark localisation. The intent of this paper is to provide a review of the current facial landmarking literature, outlining the significant progress that has been made in the field from classical generative methods to more modern techniques such as sophisticated deep neural network architectures. This review considers a generalised facial landmarking problem and provides experimental examples for each stage in the process, reporting repeatable benchmarks across a number of publicly available datasets and linking the results of these examples to the recently reported performance in the literature.
关键词： Vision,Landmarking,Face,Registration,Survey,Image,Review,Artificial neural networks,Deep learning,Machine learning
更新于2025-09-23 15:22:29
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Neural networks for trajectory evaluation in direct laser writing
摘要： Material shrinkage commonly occurs in additive manufacturing and compromises the fabrication quality by causing unwanted distortions or residual stresses in fabricated parts. Even though it is known that the resulting deformations and stresses are highly dependent on the writing trajectory, no effective strategy for choosing suitable trajectories has been reported to date. Here, we present a path to achieve this goal in direct laser writing, an additive manufacturing method based on photopolymerization that commonly suffers from strong shrinkage-induced effects. First, we introduce a method for measuring the shrinkage of distinct direct laser written lines. We then introduce a semi-empirical numerical model to capture the interplay of sequentially polymerized material and the resulting macroscopic effects. Finally, we implement an artificial neural network to evaluate given laser trajectories in terms of the resulting part quality. The presented approach proves feasibility of using artificial neural networks to assess the quality of 3D printing trajectories and thereby demonstrates a potential route for reducing the impact of material shrinkage on 3D printed parts.
关键词： Advanced manufacturing,Residual stresses,Artificial neural networks,Direct laser writing
更新于2025-09-23 15:21:01
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Human sensitivity to perturbations constrained by a model of the natural image manifold
摘要： Humans are remarkably well tuned to the statistical properties of natural images. However, quantitative characterization of processing within the domain of natural images has been difficult because most parametric manipulations of a natural image make that image appear less natural. We used generative adversarial networks (GANs) to constrain parametric manipulations to remain within an approximation of the manifold of natural images. In the first experiment, seven observers decided which one of two synthetic perturbed images matched a synthetic unperturbed comparison image. Observers were significantly more sensitive to perturbations that were constrained to an approximate manifold of natural images than they were to perturbations applied directly in pixel space. Trial-by-trial errors were consistent with the idea that these perturbations disrupt configural aspects of visual structure used in image segmentation. In a second experiment, five observers discriminated paths along the image manifold as recovered by the GAN. Observers were remarkably good at this task, confirming that observers are tuned to fairly detailed properties of an approximate manifold of natural images. We conclude that human tuning to natural images is more general than detecting deviations from natural appearance, and that humans have, to some extent, access to detailed interrelations between natural images.
关键词： natural images,image recognition,noise perturbations,artificial neural networks,generative adversarial nets
更新于2025-09-23 15:21:01
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Robust Method for Diagnosis and Detection of Faults in??Photovoltaic Systems Using Artificial Neural Networks
摘要： During their operation, PV systems can be subject of various faults and anomalies that could lead to a reduction in the effectiveness and the profitability of the PV systems. These faults can crash, cause a fire or stop the whole system. The main objective of this work is to present a sophisticated method based on artificial neural networks ANN for diagnosing; detecting and precisely classifying the fault in the solar panels in order to avoid a fall in the production and performance of the photovoltaic system. The work established in this paper intends in first place to propose a method to detect possible various faults in PV module using the Multilayer Perceptron (MLP) ANN network. The developed artificial neural network requires a large database and periodic training to evaluate the output parameters with good accuracy. To evaluate the accuracy and the performance of the proposed approach, a comparison is carried out with the classic method (the method of thresholding). To test the effectiveness of the proposed approach in detecting and classifying different faults, an extensive simulation is carried out using Matlab SIMULINK.
关键词： diagnosis,artificial neural networks,faults detection,photovoltaic system,method of thresholding
更新于2025-09-23 15:19:57
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Three-dimensional vectorial holography based on machine learning inverse design
摘要： The three-dimensional (3D) vectorial nature of electromagnetic waves of light has not only played a fundamental role in science but also driven disruptive applications in optical display, microscopy, and manipulation. However, conventional optical holography can address only the amplitude and phase information of an optical beam, leaving the 3D vectorial feature of light completely inaccessible. We demonstrate 3D vectorial holography where an arbitrary 3D vectorial field distribution on a wavefront can be precisely reconstructed using the machine learning inverse design based on multilayer perceptron artificial neural networks. This 3D vectorial holography allows the lensless reconstruction of a 3D vectorial holographic image with an ultrawide viewing angle of 94° and a high diffraction efficiency of 78%, necessary for floating displays. The results provide an artificial intelligence–enabled holographic paradigm for harnessing the vectorial nature of light, enabling new machine learning strategies for holographic 3D vectorial fields multiplexing in display and encryption.
关键词： manipulation,microscopy,artificial neural networks,inverse design,optical display,machine learning,3D vectorial holography
更新于2025-09-23 15:19:57
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[IEEE 2018 26th Telecommunications Forum (TELFOR) - Belgrade, Serbia (2018.11.20-2018.11.21)] 2018 26th Telecommunications Forum (TELFOR) - Security Verification of Artificial Neural Networks Used to Error Correction in Quantum Cryptography
摘要： Error correction in quantum cryptography based on artificial neural networks is a new and promising solution. In this paper the security verification of this method is discussed and results of many simulations with different parameters are presented. The test scenarios assumed partially synchronized neural networks, typical for error rates in quantum cryptography. The results were also compared with scenarios based on the neural networks with random chosen weights to show the difficulty of passive attacks.
关键词： artificial neural networks,security,error correction,machine,learning,cryptography,verification,quantum
更新于2025-09-19 17:15:36
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Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on Chip
摘要： We propose a novel photonic accelerator architecture based on a broadcast-and-weight approach for a deep neural network through a photonic integrated cross-connect. The single neuron and the complete neural network operation are numerically simulated. The weight calibration and weighted addition are reproduced and demonstrated to behave as in the experimental measurements. A dynamic range higher than 25 dB is predicted, in line with the measurements. The weighted addition operation is also simulated and analyzed as a function of the optical crosstalk and the number of input colors involved. In particular, while an increase in optical crosstalk negatively influences the simulated error, a greater number of channels results in better performance. The iris flower classification problem is solved by implementing the weight matrix of a trained three-layer deep neural network. The performance of the corresponding photonic implementation is numerically investigated by tuning the optical crosstalk and waveguide loss, in order to anticipate energy consumption per operation. The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy. The ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer for a normalized root mean square error lower than 0.09, suggesting that there is a maximum level of error permitted at the first layer for guaranteeing a final accuracy higher than 89%. However, it is still possible to find good local minima even for an error higher than 0.09, due to the stochastic nature of the network we are analyzing. Lower levels of path losses allow for half the power consumption at the matrix multiplication unit, for the same error level, offering opportunities for further improved performance. The good agreement between the simulations and the experiments offers a solid base for studying the scalability of this kind of network.
关键词： deep neural network,photonic neural network,image classification,semiconductor optical amplifiers,artificial neural networks,photonic integrated circuits
更新于2025-09-19 17:13:59
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A new approach to non-linear multivariate calibration in laser-induced breakdown spectroscopy analysis of silicate rocks
摘要： In this paper a new approach to quantitative Laser-Induced Breakdown Spectroscopy (LIBS) analysis of silicate rocks is presented. The method is adapted from the Franzini and Leoni algorithm, a method widely used in X-Ray Fluorescence analysis for correcting the matrix effects in the determination of the composition of geological materials. To illustrate the features of the new method proposed, nine elements were quantified in 19 geological standards by building linear univariate calibration curves, linear multivariate calibration surfaces (PLS) and using Artificial Neural Networks. The results were then compared with the predictions derived from the application of the algorithm here proposed. It was found that the Franzini and Leoni approach gives results much more precise than linear uni- and multivariate approaches, and comparable with the ones derived from the application of Artificial Neural Networks. A definite advantage of the proposed approach is the possibility of building multivariate non-linear calibration surfaces using linear optimization algorithms, a feature which makes the application of the Franzini and Leoni method in LIBS analysis much simpler (and controllable) with respect to the algorithms based on Artificial Neural Networks.
关键词： LIBS,Artificial Neural Networks,Multivariate Analysis,Geology,PLS
更新于2025-09-19 17:13:59