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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

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A statistical online inference approach in averaged stochastic approximation 22

A statistical online inference approach in averaged stochast...

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Proceedings of the 36th International conference on Neural Information processing Systems

作者： Chuhan Xie Zhihua Zhang School of Mathematical Sciences Peking University Beijing China

ISBN: (纸本)9781713871088

In this paper we propose a general framework to perform statistical online inference in a class of constant step size stochastic approximation (SA) problems, including the well-known stochastic gradient descent (SGD) and Q-learning. Regarding a constant step size SA procedure as a time-homogeneous Markov chain, we establish a functional central limit theorem (FCLT) for it under weaker conditions, and then construct confidence intervals for parameters via random scaling. To leverage the FCLT results in the Markov chain setting, an alternative condition that is more applicable for SA problems is established. We conduct experiments to perform inference with both random scaling and other traditional inference methods, and finds that the former has a more accurate and robust performance.

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2nd International conference on Computer Vision and Data Mining, ICVDM 2021

2nd International Conference on Computer Vision and Data Min...

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2nd International conference on Computer Vision and Data Mining, ICVDM 2021

The proceedings contain 72 papers. The topics discussed include: design of VR application in detection line based on industrial robot for optical routers;multi-lingual speaker recognition based on asymmetric convolution and central loss function;knowledge graph completion based on the joint embedding of entities;curved path planning based on 3D vision water immersion ultrasonic nondestructive testing;research on algorithm of fighter landing gear in bad video image;comparative study of different dimensionality reduction methods in hyperspectral image classification;ballot character recognition based on image processing;shuffling-SinGAN: improvement on generative model from a single image;and multi-objective ant lion optimizer for stochastic robotic disassembly line balancing problem subject to resource constraints.

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Exploiting Local Convergence of Quasi-Newton methods Globally: Adaptive Sample Size Approach 35

Exploiting Local Convergence of Quasi-Newton Methods Globall...

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35th conference on Neural Information processing Systems (NeurIPS)

作者： Jin, Qiujiang Mokhtari, Aryan Univ Texas Austin ECE Dept Austin TX 78712 USA

ISBN: (纸本)9781713845393

In this paper, we study the application of quasi-Newton methods for solving empirical risk minimization (ERM) problems defined over a large dataset. Traditional deterministic and stochastic quasi-Newton methods can be executed to solve such problems;however, it is known that their global convergence rate may not be better than first-order methods, and their local superlinear convergence only appears towards the end of the learning process. In this paper, we use an adaptive sample size scheme that exploits the superlinear convergence of quasi-Newton methods globally and throughout the entire learning process. The main idea of the proposed adaptive sample size algorithms is to start with a small subset of data points and solve their corresponding ERM problem within its statistical accuracy, and then enlarge the sample size geometrically and use the optimal solution of the problem corresponding to the smaller set as an initial point for solving the subsequent ERM problem with more samples. We show that if the initial sample size is sufficiently large and we use quasi-Newton methods to solve each subproblem, the subproblems can be solved superlinearly fast (after at most three iterations), as we guarantee that the iterates always stay within a neighborhood that quasi-Newton methods converge superlinearly. Numerical experiments on various datasets confirm our theoretical results and demonstrate the computational advantages of our method.

关键词： Learning systems

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PyramidTabNet: Transformer-Based Table Recognition in image-Based Documents 17th

PyramidTabNet: Transformer-Based Table Recognition in Image-...

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17th International conference on Document Analysis and Recognition (ICDAR)

作者： Umer, Muhammad Mohsin, Muhammad Ahmed Ul-Hasan, Adnan Shafait, Faisal Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan Natl Ctr Artificial Intelligence NCAI Deep Learning Lab Islamabad Pakistan

ISBN: (纸本)9783031417337;9783031417344

Table detection and structure recognition is an important component of document analysis systems. Deep learning-based transformer models have recently demonstrated significant success in various computer vision and document analysis tasks. In this paper, we introduce PyramidTabNet (PTN), a method that builds upon Convolutionless Pyramid Vision Transformer to detect tables in document images. Furthermore, we present a tabular image generative augmentation technique to effectively train the architecture. The proposed augmentation process consists of three steps, namely, clustering, fusion, and patching, for the generation of new document images containing tables. Our proposed pipeline demonstrates significant performance improvements for table detection on several standard datasets. Additionally, it achieves performance comparable to the state-of-the-art methods for structure recognition tasks.

关键词： deep learning image transformer image processing data augmentation table augmentation table segmentation table detection structure recognition

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Effect of spectral resolution on the segmentation quality of magnetic resonance imaging data 26

Effect of spectral resolution on the segmentation quality of...

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26th IEEE International conference on Intelligent Engineering Systems, INES 2022

作者： Gyorfi, Agnes Csaholczi, Szabolcs Lukats-Pisak, Ioan-Marius Denes-Fazakas, Lehel Koble, Andrea Shvets, Olga Eigner, Gyorgy Kovacs, Levente Szilagyi, Laszlo Physiological Controls Research Center Óbuda University Budapest Hungary Sapientia Univ. Comput. Intell. Research Group Tg. Mureş Romania East-Kazakhstan Technical University Dept. of Information Technology and Intelligent Systems Ust'-Kamenogorsk Kazakhstan

ISBN: (数字)9781665492096

ISBN: (纸本)9781665492096

The majority of the machine learning methods employed for brain tissue or tumor segmentation from multi-spectral MRI data, especially the ensemble learning methods, use hundreds of attributes for the characterization of the pixels, which leads to enormous storage space requirement. Dealing with hundreds of volumetric records under such circumstances also represents a severe computational burden. To facilitate the establishment and deployment of such data processing frameworks, this paper proposes to investigate, which is the best trade-off between segmentation accuracy and necessary storage space, via manipulating with the spectral resolution used for the attributes of the pixels. Three machine learning methods, two multi-spectral brain MRI datasets, and five statistical indicators of segmentation accuracy were involved in the experimental study, which revealed that an 8-bit color depth or spectral resolution of the feature data is sufficient to obtain the finest achievable segmentation accuracy, while allowing for up to 50% reduction of the memory required by the segmentation procedure, compared to the commonly deployed feature encoding techniques. © 2022 IEEE.

关键词： image segmentation

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Reduced KPCA based Ensemble Learning Approach for Fault Diagnosis of Grid-Connected PV Systems 19

Reduced KPCA based Ensemble Learning Approach for Fault Diag...

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19th IEEE International Multi-conference on Systems, Signals and Devices, SSD 2022

作者： Dhibi, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&M University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults. First, an ensemble learning (EL) method that combines several base models is proposed. Next, kernel principal components analysis (KPCA) and reduced KPCA are proposed to extract and select the pertinent characteristics from raw data. Then, the extracted significant characteristics are transmitted to the EL model for classification purposes. The main idea behind these proposals is to provide the best accuracy and also improve the results in terms of computation time. The diagno-sis results demonstrated the efficiency of the proposed frameworks. © 2022 IEEE.

关键词： Multivariant analysis

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M2Mamba: Multi-Scale in Multi-Scale Mamba for Blind Face Restoration

M2Mamba: Multi-Scale in Multi-Scale Mamba for Blind Face Res...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Weidan Yan Wenze Shao Dengyin Zhang School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications Nanjing P. R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the evolution of telemedicine, clean images, especially facial images, are crucial in areas such as symptom evaluation and cosmetic medicine. However, dealing with indiscernible facial images, a condition known as blind degradation, poses a formidable challenge for existing blind face restoration (BFR) techniques due to their inherently ill-posed nature. Thus, a delicate interaction between preserving local details and maintaining global geometries is desired. Despite advances in convolutional neural networks (CNNs), transformers, and denoising diffusion, where no matter serried convolutions, elaborately-designed self-attentions, or stochastic noises, tend to isolate either local or non-local features. To this end, a new candidate model termed Multi-Scale in Multi-Scale Mamba (M2Mamba) is proposed, which builds on a pioneering structured state space model-based network and includes three new components: multi-scale learned fusion module (MSLFM), multi-scale attention fusion module (MSAFM), and multi-scale inspired Mamba (MS-Mamba). Firstly, the MSLFM is adopted by extracting image-level global guidance from inputs of different scales, preserving intuitive perception yet enriching semantic understanding. Secondly, the MSAFM dynamically integrates features from various encoder stages. Thirdly, the MS-Mamba employs separate branches for both small- and large-scale receptive fields, benefiting modeling of long-range dependencies. In the final, M2Mamba is demonstrated on both synthetic and realistic benchmarks, showing comparable or better performance than state-of-the-art methods. The codes are available at: https://***/yanwd628/M2Mamba.

关键词： Hands Biological system modeling Telemedicine Semantics Noise reduction stochastic processes Transformers image restoration Convolutional neural networks Faces

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Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, Real-Time Approach with Angiography Imaging

Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, ...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International conference on

作者： Aruna Kumara B Raveendra Gudodagi Madan H T Kavitha D D Manjunatha H M Dept. of Computer Science & Engg. Proudhadevaraya Instititute of Technology Hosapete India School of Electronics and Communication Engg. REVA University Bengaluru Inida Department of Electronics and Communication (Advanced Communication Technology) NITTE (Deemed to be University) NMAM Institute of Technology Nitte India Dept. of Computer Science & Engg. HKBK College of Engineering Bengaluru India Electrical & Electronics Engg. Bapuji Institute of Engineering and Technology Davanagere Inida

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Brain aneurysm is a swell or balloon in a blood vessel in the brain that can cause severe health problems to the patients. Due to its potentiality, if it ruptures it leads to serious neurological disorder such as: coma, hemorrhagic stroke, and even death. Early identification of brain aneurysm plays a crucial role in treatment. However, the traditional methods such as Computerized Tomographic Angiography (CTA), Magnetic Resonance Imaging (MRI) takes more time and complex procedures. The relentless advancement of medical imaging technologies continues to transform diagnostic paradigms in neurology. In this context, this study integrates the convolutional neural networks (CNNs) into real-time brain aneurysm detection from angiography image. This paper presents a CNN-based model designed to rapidly and accurately identify cerebral aneurysms from angiography images. The proposed method, initially prepares the dataset by collecting diverse angiography images from medical archives. Later, the images undergoes with various preprocessing methods such as: denoising, contrast enhancement, normalization, resizing, and augmentation to enhance the quality of data, and model generalization. Then, the method automatically extracts the blood vessel structure and aneurysm characteristics such as: irregular shapes, unusual bulges, and densities within the vessels as features. Finally, the extracted features passed through the connected layers and the CNN classifies the whether an image contains aneurysm or not. The CNN model is trained with stochastic gradient descent (SGD) algorithm, and the trained model is deployed on a standard Graphics processing Unit (GPU) for real time inference. The results showed that, the performance of the method was superior compared to the traditional methods.

关键词： Angiography Magnetic resonance imaging Graphics processing units Process control Aneurysm Feature extraction Brain modeling Real-time systems Convolutional neural networks Standards

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methods of the theory of random processes in optical spectrometry

Methods of the theory of random processes in optical spectro...

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2021 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2021

作者： Moskaletz, D.O. Moskaletz, O.D. Paraskun, A.S. Saint Petersburg Electrotechnical University 'Leti' Saint Petersburg Russia Saint Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia

The stochastic aspect of spectrometry in the case of the model 'deterministic spectral instrument under stochastic action' based on the general theoretical approach to the problem of statistical measurements is reviewed. The harmonized random process of the analyzed signal is a model. Based on deterministic representations, a stochastic connection is established between the input and output of a spectral device for complex spectra. It is shown that the apparatus complex spectrum exists with a probability of one. The obtained results to the description of the spectral processing of optical radiation by a parallel optical signal spectrum analyzer and a diffraction grating spectral device. The obtained estimates of the energy spectrum of optical radiation are reviewed. The problems of digital processing of spectroscopic information are also reviewed. © 2021 IEEE.

关键词： Spectroscopy

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Visual-based defect detection for product classification: a machine learning approach 47

Visual-based defect detection for product classification: a ...

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conference on Infrared Technology and Applications XLVII

作者： Saragaglia, A. Calafat, N. LYNRED Actipole CS 10021364 Route Valence F-38113 Veurey Voroize France

ISBN: (纸本)9781510643208

Visual inspection is the final stage for quality product testing of infrared detectors. Traditional methods for this type of work commonly rely on human visual inspection, which is the ultimate standard for determining the visual quality of the image. However, using human inspection can be problematic because of the statistical variation between observers. Automated methods based on machine learning techniques, and especially Deep Learning, have emerged in recent decades that are able to closely match the visual perception of the human eye, while providing the added benefits of speed and consistency. In the present paper, we propose a hybrid method to classify images acquired from assembled detectors based on the combination of both infrared-adapted image processing techniques and machine learning approaches. The first classifier extracts several features from the infrared image and uses support vector machines (SVM) for classification. The second, a Convolutional Neural Network (CNN), is dedicated to the detection of specific defects, tough to identify with traditional image processing techniques because of their low contrast and is also dedicated to novelty detection. While CNN achieves precise and robust identification of special defects, the first classifier secures the global detection of new defects even if their shape is not clearly identified. The proposed model has proven to be robust and able to accurately classify detectors images, regarding human visual inspection. The approach was developed with the capability to scale-up and evolve in order to take in consideration new products. We also show that using AI presents the double benefit of improving the quality of the products meanwhile reducing human time effort.

关键词： Deep Learning Visual Inspection image processing CNN Quality

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