When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes...
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The symmetric positive definite (SPD) matrices play an important role in diverse domains, including computer vision and signal processing, due to their unique ability to capture the intrinsic structure of nonlinear da...
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ISBN:
(纸本)9789464593617;9798331519773
The symmetric positive definite (SPD) matrices play an important role in diverse domains, including computer vision and signal processing, due to their unique ability to capture the intrinsic structure of nonlinear data using Riemannian geometry. Despite their significance, a notable gap exists in the absence of statistical distributions capable of effectively characterizing the statistical properties within the SPD matrices space. This paper addresses this gap by introducing a novel Riemannian Generalized Gaussian distribution (RGGD). The primary aspect of this work includes presenting the precise expression for the probability density function (PDF) of the RGGD model, along with the parameter estimation method based on the maximum likelihood for this distribution. The second aspect of this work entails harnessing the second-order statistical information captured in the feature maps originating from the initial layers of deep convolutional neural networks (DCNNs) using the RGGD stochastic model within an image classification framework. The third aspect of this work includes also the comparison of the three-parameter RGGD model with its two-parameter predecessors, namely the Riemannian Gaussian distribution (RGD) and the Riemannian Laplacian distribution (RLD). Besides the mathematical foundations, the model's efficiency is validated through experiments conducted on the three well-known datasets, showcasing its effectiveness in capturing the underlying statistics of SPD matrices.
Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vul...
ISBN:
(纸本)9798350374520;9798350374513
Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks;ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.
The machine learning of lattice operators has three possible bottlenecks. From a statistical standpoint, it is necessary to design a constrained class of operators based on prior information with low bias, and low com...
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ISBN:
(纸本)9783031577925;9783031577932
The machine learning of lattice operators has three possible bottlenecks. From a statistical standpoint, it is necessary to design a constrained class of operators based on prior information with low bias, and low complexity relative to the sample size. From a computational perspective, there should be an efficient algorithm to minimize an empirical error over the class. From an understanding point of view, the properties of the learned operator need to be derived, so its behavior can be theoretically understood. The statistical bottleneck can be overcome due to the rich literature about the representation of lattice operators, but there is no general learning algorithm for them. In this paper, we discuss a learning paradigm in which, by overparametrizing a class via elements in a lattice, an algorithm for minimizing functions in a lattice is applied to learn. We present the stochastic lattice descent algorithm as a general algorithm to learn on constrained classes of operators as long as a lattice overparametrization of it is fixed, and we discuss previous works which are proves of concept. Moreover, if there are algorithms to compute the basis of an operator from its overparametrization, then its properties can be deduced and the understanding bottleneck is also overcome. This learning paradigm has three properties that modern methods based on neural networks lack: control, transparency and interpretability. Nowadays, there is an increasing demand for methods with these characteristics, and we believe that mathematical morphology is in a unique position to supply them. The lattice overparametrization paradigm could be a missing piece for it to achieve its full potential within modern machine learning.
When it comes to cancer and its linked disorders, lung cancer is consistently ranked among the top causes of mortality. The primary method for making the diagnosis is to do a scan analysis of the patient's lungs. ...
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image segmentation is one of the key problems in imageprocessing. Among the different models and approaches developed, some of the commonly used statisticalmethods are based on the intensity homogeneity. In this pap...
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The accuracy of electric load forecasting is crucial when working on applications in power grid decision-making and operation. Due to the non-linear and stochastic behaviour of customers, the electric load profile is ...
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ISBN:
(数字)9781665495783
ISBN:
(纸本)9781665495783
The accuracy of electric load forecasting is crucial when working on applications in power grid decision-making and operation. Due to the non-linear and stochastic behaviour of customers, the electric load profile is a complicated signal. In this paper, authors propose machine learning based automated system for electricity load forecasting, taking into consideration various variable factors that have an impact on the future electricity load demand. Three machine learning algorithms are used for evaluation of the proposed framework. The algorithms are evaluated on electricity load data collected from eastern region of Ontario, integrated with the weather and population data of the region. The Light GBM algorithm comparatively performs best with mean absolute error of 0.156. The developed system can be used for more accurate and efficient load forecasting applications.
When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes...
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ISBN:
(数字)9798331528850
ISBN:
(纸本)9798331528867
When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes an image binarization method based on stochastic resonance theory. First, we divide the image into sub-blocks and set a binarization threshold based on the statistical properties of the pixels in each sub-block. Next, the image signal is converted into a one-dimensional time series signal using Hilbert scanning. The processed signals are input into a threshold array system, which amplifies the weak edge information in the input signals through the stochastic resonance phenomenon. Subsequently, we performed modulation and inverse scanning on the output signals of the system to generate the binary image for each sub-block. Finally, all sub-block binary images were combined to complete the binarization of the overall image. Experimental results show that the method proposed in this paper can effectively retain the detailed information of document images and significantly outperforms the traditional binarization method regarding image quality.
Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vul...
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ISBN:
(数字)9798350374513
ISBN:
(纸本)9798350374520
Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks; ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.
Motivated by the recent successes of neural networks that have the ability to fit the data perfectly and generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum ...
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