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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Development of Realistic Numerical Brain Model Based on MRIs for Microwave Brain Stroke Detection

Development of Realistic Numerical Brain Model Based on MRIs...

引用

International conference on Electronic Measurement and Instruments, ICEMI

作者： Ping Han Ziyu Liu Qinwei Li Ming Yu Rongnan Zhao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China Institute of Medical Support Technology Institute of Systems Engineering Academy of Military Sciences Tianjin China Yingkou Jucheng Development Ltd. of teaching Yingkou China

ISBN: (纸本)9781665444927

In this paper, a two-dimensional truth-oriented brain model is proposed for imaging simulation of wearable stroke detection devices. Stroke detection based on ultra-wideband microwave imaging is a new detection method, which is portable, fast and safe. Wearable detection equipment is an important development direction of ultra-wideband microwave imaging for stroke. By analyzing the electromagnetic characteristics of the brain tissue at different frequencies, the electromagnetic parameters of the brain tissue were determined, and the real human brain MRI images were used for analysis and modeling. Compared to previous simple brain model, this model can more truly reflect the internal structure of human brain, and provide a more accurate experimental platform for various imaging simulation algorithms. Finally, the delay confocal algorithm was used to reconstruct the brain image. In the imaging results, the bleeding spots with a diameter of 6 mm in the brain is clearly identified.

关键词： Microwave integrated circuits Magnetic resonance imaging Imaging Microwave devices Brain modeling Numerical models Microwave FET integrated circuits

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Distributed matrix methods of compression, masking and noise-resistant image encoding in a high-speed network of information exchange, information processing and aggregation

Distributed matrix methods of compression, masking and noise...

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SPIE conference on Future Sensing Technologies

作者： Kapranova, Ekaterina A. Nenashev, Vadim A. Sergeev, Alexander M. Burylev, Dmitry A. Nenashev, Sergey A. St Petersburg State Univ Aerosp Instrumentat 67 Bolshaya Morskaia Str St Petersburg 190000 Russia

ISBN: (数字)9781510631120

ISBN: (纸本)9781510631120

Optical location systems implemented on the basis of high-resolution video cameras are currently used in many areas. These are, for example, medical equipment, traffic control systems, satellite monitoring systems, preventive security systems, object recognition and classification systems, etc. For these systems, the requirements for high-resolution image processing speed of 8K, 16K and more are increasing every year. processing of such information becomes even more difficult when providing a high frequency of reading frames from the matrix of the video camera, especially for systems operating in real time and using high-speed networks of exchange, processing and integration of information. This requires to determine a set of types of information processing procedures: masking, compression, noise-protected coding, etc., for which algorithms should be revised in case of multi-user and multiposition application in distributed information processing and aggregation systems. In this regard, the problems of development and improvement of new ways of representation, compression, storage, masking and error-correcting coding of high-resolution images with a common mathematical basis are relevant. Most information transformation procedures are based on the use of orthogonal bases, in particular orthogonal and quasiorthogonal matrices. The paper presents the results of the search and formation of such bases, the methods of synthesis of quasi-orthogonal matrices for image processing problems that meet the formulated requirements. The methods of guaranteed synthesis of matrices of symmetric, cyclic, block-cyclic and other structures of different orders, assuming economical storage and generation, are proposed. Such matrix bases, which are constantly expanding, provide developers with a wide range of algorithms to choose the most appropriate one from them. The problem of search and study of extreme quasi-orthogonal matrices has great importance for a wider range of information p

关键词： masking images noise-protected coding of high-resolution images compression images information processing and aggregation systems orthogonal bases quasi-orthogonal matrices the problem of search and study of extreme quasi-orthogonal matrices noise-resistant coding of data during their transmission in wireless high-speed networks

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An Effective Nuclear Extraction Mask Method for SVM Classification

An Effective Nuclear Extraction Mask Method for SVM Classifi...

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International conference on Computers, Communications, and systems (ICCCS)

作者： Qinghua Li Hailong Ma Zhao Zhang Chao Feng School of Electronic and Information Engineering Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Electrical Engineering and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

With the development of medical technology, the automatic cell analysis system plays an important role in medical diagnosis and medical image processing. The kernel recognition theory and technology based on support vector machine (SVM) classifier are mainly optimized from the perspective of the kernel segmentation algorithm to improve the recognition accuracy of the SVM classifier. Unfortunately, the nuclear overlap treatment can not accurately separate the nuclear gelling impurities in the dyeing process, resulting in the low classification accuracy of SVM. To solve the above image segmentation problems in the process of nuclear imaging processing, an effective nuclear extraction method based on the mask method for the SVM classifier is proposed. Compared with related work, the proposed method enables one to achieve a higher accuracy of SVM cross-validation.

关键词： image segmentation Support vector machines Classification algorithms Clustering algorithms Training Feature extraction Kernel

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EczemaNet: A Deep CNN-based Eczema Diseases Classification

EczemaNet: A Deep CNN-based Eczema Diseases Classification

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International image processing, Applications and systems conference (IPAS)

作者： Masum Shah Junayed Abu Noman Md Sakib Nipa Anjum Md Baharul Islam Afsana Ahsan Jeny Daffodil International University Dhaka Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh American University of Malta Bormla BML Malta

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. In recent years, several attempts have been taken to automate the detection of skin diseases with much accuracy. Many methods such as image processing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin diseases. It is found that among all those skin disease detection systems, particularly detection work on eczema disease is rare. There is also insufficiency in eczema disease dataset. In this paper, we propose a novel deep CNN-based approach for classifying five different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. Regularization techniques such as batch normalization and dropout helped to reduce overfitting. Our proposed model achieved an accuracy of 96.2%, which exceeded the performance of the state of the arts.

关键词： image segmentation Machine learning algorithms conferences Transforms Medical services Skin Diseases

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Multispectral image processing algorithms for enhanced vision systems in the Arctic 4

Multispectral image processing algorithms for enhanced visio...

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4th International Scientific conference on Arctic: History and Modernity

作者： Kirillov, S.N. Pokrovskij, P.S. Baukov, A.A. Skonnikov, P.N. Ryazan State Radio Engineering University Ryazan Russia

The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images being subject to contrast enhancement parameters are estimated. Based on these characteristics, the authors concluded that image areas requiring the greatest contrast enhancement are the areas with low saturation and magnitude gradients, at certain brightness values. image quality improvement method is proposed. It performs processing only in the areas where it is necessary to enhance the contrast, practically without affecting the most homogeneous or structured image parts. The processed image saturation remains due to the processing of both luminance channel and saturation channel. The algorithm proposed also provides contrast enhancement of shaded image areas. The calculated values of various objective image quality indices indicate that the contrast enhancement algorithm proposed provides better results than known approaches. In addition, different spectral range image fusion algorithm ensuring visibility in the presence of interfering factors is proposed. It differs from known methods by adaptive weight adjustment in different areas of image. The example confirming the effectiveness of the fusion method proposed is shown. For its comparison with known methods, the values of fusion objective quality indices are calculated. The fusion algorithm proposed is shown to surpass known methods by various quality assessments. The conclusion about the expediency of using the algorithms developed in technical vision systems of robotic complexes in the Arctic is made. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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ID-Reveal: Identity-aware DeepFake Video Detection

ID-Reveal: Identity-aware DeepFake Video Detection

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International conference on Computer Vision (ICCV)

作者： Davide Cozzolino Andreas Rössler Justus Thies Matthias Nießner Luisa Verdoliva University Federico II of Naples Technical University of Munich Max Planck Institute for Intelligent Systems Tübingen

ISBN: (纸本)9781665428132

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.

关键词： Training image coding Social networking (online) Training data Robustness Forgery Faces

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Workpiece Detection Based on image processing and Convolutional Neural Network

Workpiece Detection Based on Image Processing and Convolutio...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International conference on

作者： Wocheng Chen Yi Lei Min Liu Junming Zhang xifan Yao School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728192772

ISBN: (纸本)9781728192789

This paper presents a method on workpiece detection based on image processing and convolutional neural network(CNN). Firstly, four extreme points and center point of the workpiece are detected by image processing technologies such as canny edge detection operator, morphological processing and denoising processing. And the predicted boxes fitting the shape of image is generated. Then, according to the coordinates of the extreme points, the image with a single workpiece is cut out, and a novel CNN named workpiece-net(wp-net) is created to classify the object. As a result, the accuracy of image cutting is 0.9986, the average Intersection over Union(IoU) is 0.9235; the parameter size of wp-net is 98.25K and the average precision of classification is 0.9883. In addition, the average recall of classification is 0.9877 and the speed of classification is 0.1243s/fps without Graphics processing Unit(GPU) and multithreading. Compared with the pure deep learning method, this method can detect more accurate coordinates which are consisted of extreme points. At the same time, the number of wp-net parameters and the complexity of the model structure used for classification are so far less than the popular deep neural network for detection that it can be easily deployed in embedded devices with limited storage space and computing power.

关键词： image processing Deep learning Task analysis Classification algorithms Computational modeling Complexity theory Training

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An Algorithm for Multi-Fame image Super-Resolution Under Applicative Noise Based on a Convolutional Neural Network

An Algorithm for Multi-Fame Image Super-Resolution Under App...

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International conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Sergey Savvin Alexander Sirota Faculty of Computer Sciences Voronezh State University Voronezh Russia

ISBN: (数字)9781728188409

ISBN: (纸本)9781728181134

The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations (damaged regions) in initial low resolution images. It analyses the existing solutions to the problem based on using recurrent algorithms of optimal filtering of a sequence of low resolution images. The paper also considers the existing super-resolution algorithms based on convolutional neural networks and deep learning models. A new approach and a corresponding image processing scheme is suggested for multi-frame image super-resolution under applicative noise based on using a convolutional neural network. The paper presents the results of the experiment conducted in order to compare the considered approaches to multi-frame image super-resolution by means of a sequence of low resolution frames under applicative noise.

关键词： Training Energy resolution Neural networks Iterative algorithms Registers Convolutional neural networks Mathematical model

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A Survey: image Segmentation Techniques 4th

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4th International conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Phonsa, Gurbakash Manu, K. Mewar Univ Chittaurgarh 312901 Rajasthan India Lovely Profess Univ Jalandhar 144403 Punjab India

ISBN: (纸本)9789811307614;9789811307607

image segmentation is a core step of image processing. image segmentation is an image processing technique distinct as in which we divide the image into multiple regions in forms of pixel or break down the image into different objects and regions. The main aim of image segmentation is to represent an image on noise-free form. The main use of image segmentation is to detect the objects, relevant data in digital image and their boundaries. These techniques split the image into small regions in order to analyse them and it is also helpful to distinguish the different types of object in single image. Till date, various image segmentation approaches are suggested by researchers with specific aspects or little diversity and commitment for betterment. Researchers work continuously to optimize techniques and enhancing them in order to make image more recognizable, i.e. more smother and noise free. In this paper, as per our study, different researcher works on image processing approaches like thresholding, neural network, edge base segmentation, region base segmentation, etc. are presented.

关键词： Segmentation techniques Edge detection Boundary-based segmentation Region-based segmentation

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Polarization Sensitive Array Beamforming Algorithm for Multipath Mitigation in GNSS

Polarization Sensitive Array Beamforming Algorithm for Multi...

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IEEE International conference on Electronic Information and Communication Technology (ICEICT)

作者： xiaojian Wang Jianxing Li Ming Zhang Wenchao Chen xiaoming Chen School of Information and Communications Engineering Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

In this paper, the polarization sensitive array is proposed to mitigate the multipath reflections in global navigation satellite systems (GNSS). The minimum variance distortionless response (MVDR) and the multiple signal classification (MUSIC) algorithms are used to estimate the directions and polarization of the signal and the multipath reflections. By using the estimated information, the linear constrained minimum variance (LCMV) beamformer maximizes the signal, meanwhile, nulls the multipath reflections, in both spatial and polarization domains.

关键词： Global navigation satellite system Array signal processing Pattern classification Reflection Multiple signal classification Classification algorithms Information and communication technology

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