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IEEE Visual Communications and image processing (VCIP)

作者： Yongli Chang Sumei Li Li Ma Jie Jin School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The design of stereo image quality assessment (SIQA) methods cannot be well based on the biological theory of human vision, so the performance of many SIQA methods cannot achieve good consistency with the subjective perception. The research on the visual system tends to the dorsal and ventral pathways, which ignores the information asymmetry in the early visual pathways. It is worth noting that the ON and OFF receptive fields in retinal ganglion cells (RGCs) respond asymmetrically to the statistical features of images. Inspired by this, we propose a SIQA method based on monocular and binocular visual features, which takes into account the asymmetry of local contrast bright and dark features in early visual pathways. First, this paper extracts the response maps of ON and OFF cell in RGCs to left and right views respectively. And then the different information fusion modes of visual cortex are used to fuse the response maps information of left and right views. Final, monocular and binocular features were extracted and sent to support vector regression (SVR) for quality regression. Experimental results show that the proposed method is superior to several mainstream SIQA metrics on two publicly available databases.

关键词： Visualization Feature extraction Distortion Information filters image quality Retina Brain modeling

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Rail accident analysis using large-scale investigations of train derailments on switches and crossings: Comparing the performances of a novel stochastic mathematical prediction and various assumptions

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ENGINEERING FAILURE ANALYSIS 2019年 103卷 203-216页

作者： Dindar, Serdar Kaewunruen, Sakdirat An, Min Univ Birmingham Sch Engn Dept Civil Engn Birmingham B15 2TT W Midlands England Univ Birmingham Birmingham Ctr Railway Res & Educ Birmingham B15 2TT W Midlands England Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA Univ Salford Sch Built Environm Manchester M5 4WT England

Each day tens of turnout-related derailment occur across the world. Not only is the prediction of them quite complex and difficult, but this also requires a comprehensive range of applications, and managing a well-designed geographic information system. With the advent of Geographic Information Systems (GIS), and computers-aided solutions, the last two decades have witnessed considerable advances in the field of derailment prediction. Mathematical models with many assumptions and simulations based on fixed algorithms were also introduced to estimate derailment rates. While the former requires a costly investment of time and energy to try and find the most fitting mathematical solution, the latter is sometimes a high hurdle for analysists since the availability and accessibility of geospatial data are limited, in general. As train safety and risk analysis rely on accurate assessment of derailment likelihood, a guide for transportation research is needed to show how each technique can approximate the number of observed derailments. In this study, a new stochastic mathematical prediction model has been established on the basis of a hierarchical Bayesian model (HBM), which can better address unique exposure indicators in segmented large-scale regions. Integration of multiple specialized packages, namely, MATLAB for image processing, R for statistical analysis, and ArcGIS for displaying and manipulating geospatial data, are adopted to unleash complex solutions that will practically benefit the rail industry and transportation researchers.

关键词： Derailment Turnout component failures Hierarchical Bayesian analysis Freight transportation Spatial analysis

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Study of problem solution quality for detection of discrete failure parameter by complex adaptive data processing algorithms for mobile ground object navigation systems

Study of problem solution quality for detection of discrete ...

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Mechanical Science and Technology Update (MSTU) conference

作者： Ivanov, A., V Surkov, V. O. Belyaev, M. P. Varepo, L. G. Belyaev, P. S. Tambov State Tech Univ 106 Sovetskaya Str Tambov 392000 Russia MESC AF NE Zhukovsky & YA Gagarin Air Force Acad 54 ASt Bolchevikov Str Voronezh 394064 Russia Omsk State Tech Univ 11 Mira Ave Omsk 644050 Russia

The study of the possibility of detecting radio signals of global satellite navigation systems in the mobile ground object navigation systems, developed on complex adaptive algorithms of data processing is conducted. Quality assessment of the discrete parameter detection characterizing the presence or absence of radio signals is performed by the methods of statistical computer modeling. The probability of the false alarm and probability of a miss dependences on the threshold level, and the total probability error dependence on the signal-noise ratio at the receiving channel input by the maximum-correctness criteria are obtained.

关键词： Identification adaptive estimation discrete-continuous process Markov theory of stochastic processes estimation

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An Improvement of Spatial Fuzzy C-means Clustering Method for Noisy Medical image Analysis 6

An Improvement of Spatial Fuzzy C-means Clustering Method fo...

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6th International conference on image and Signal processing and their Applications, ISPA 2019

作者： Beddad, Boucif Hachemi, Kaddour Postaire, Jack-Gerard Jabloncik, Frantisek Messai, Oussama University of Saida Dr Tahar Moulay LTC Laboratory Saida Algeria University of Lille CRIStAL Laboratory-UMR 9189 Lille France University of Žilina Department of Mechatronics and Electronics Žilina Slovakia Automatic and Robotic Laboratory Electronics Department University of Mentouri Constantine Algeria

ISBN: (纸本)9781728131566

Medical image Segmentation plays a major role in MRI images processing;it's performed before the analysis and decision-making stages in several medical processes. Many investigators have developed several Fuzzy C-means methods. In this work, a reliable automatic segmentation algorithm based on the spatial FCM clustering is developed to minimize the effect of noise and intensity inhomogeneities. This approach combines two properties of the Spatial FCM using neighbor's statistical characteristics and pillar k-means. The proposed system has been implemented with simulink. Experimental results on brain MRI images show the improvement and increase the segmentation *** Current research work is compared with some wellknown existed methods to show the effectiveness that contributes to the development of tools needed in computer aided diagnosis systems aiming to assist specialists in making diagnosis decisions. © 2019 IEEE.

关键词： Simulink

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statistical Analysis and New Modeling of Real Clutter Signal in FM Radio-based Passive Radars 6

Statistical Analysis and New Modeling of Real Clutter Signal...

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6th International conference on image and Signal processing and their Applications, ISPA 2019

作者： Bendjama, Linda Laroussi, Toufik Mezache, Amar Département d'Electronique Université des Frères Mentouri Constantine Algeria Laboratoire Signaux et Systèmes de Communication SISCOM Constantine Algeria Université Mohamed Boudiaf Département d'Electronique M'sila Algeria

ISBN: (纸本)9781728131566

The major problematic in the design of a competitive detector is the construction of a statistical model that fits real data. In this paper, we present a new theoretical model that describes real data from an FM (Frequency Modulation) radio-based passive bistatic radar. In doing this, we aim to estimate the model parameters using a conventional technique and two optimization numerical methods;namely, the trust-region-reflective approach for non-linear minimization subject to bounds and the direct search Method based on the Nelder-Mead (N-M) algorithm. © 2019 IEEE.

关键词： Radar clutter

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AdaGrad Gradient Descent Method for AI image Management

AdaGrad Gradient Descent Method for AI Image Management

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IEEE International conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jen-Kuang Fang Cher-Min Fong Peng Yang Chao-Kai Hung Wen-Long Lu Chien-Wei Chang National Sun Yat-sen University Kaohsiung Taiwan R.O.C Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan R.O.C

ISBN: (数字)9781728173993

ISBN: (纸本)9781728174006

In this study, we used AdaGrad gradient descent method in optimizer for image deep learning, and compare with Adam gradient descent methods. After processing over six thousand huge database of through silicon via images, AdaGrad has shown a fast convergence and less generalization errors than Adam. The results help Artificial Intelligence for making the management of image judgment more accurate and faster.

关键词： Training Through-silicon vias Mathematical model Deep learning Artificial intelligence stochastic processes Real-time systems

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Segmentation Algorithm for the Evolutionary Biological Objects images on a Complex Background 2nd

Segmentation Algorithm for the Evolutionary Biological Objec...

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2nd conference on Computational methods in Systems and Software (CoMeSySo)

作者： Ipatov, Yury Krevetsky, Alexand Andrianov, Yury Sokolov, Boris Volga State Univ Technol Yoshkar Ola Russia Russian Acad Sci SPIIRAS St Petersburg Inst Informat & Automat St Petersburg Russia

ISBN: (纸本)9783030002114;9783030002107

In the paper, characteristic features of color images for evolving biological objects are investigated. To create an effective and fast algorithm, the choice of the color space for subsequent processing is justified. A mathematical model of images for localized objects is proposed. Estimates of the accuracy for image segmentation are received.

关键词： Evolving objects image segmentation Non-uniform background Dynamic scenes Convex hull Color space Precision segmentation Color image Clustering Calorimetry Model approximating an ellipsoid

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Optimal Measurement Budget Allocation For Particle Filtering

Optimal Measurement Budget Allocation For Particle Filtering

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IEEE International conference on image processing

作者： Antoine ASPEEL Amaury GOUVERNEUR Raphaël M. JUNGERS Benoît MACQ ICTEAM Institute UCLouvain Avenue Georges Lemaître 4-6 Louvain-la-Neuve Belgium

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter. Firstly, an example demonstrates that the genetic algorithm outperforms a random trial optimization. Then, the interest of non-regular measurements versus measurements performed at regular time intervals is illustrated and the efficiency of our proposed solution is quantified: better filtering performances are obtained in 87.5% of the cases and on average, the relative improvement is 27.7%.

关键词： Atmospheric measurements Particle measurements Genetic algorithms Monte Carlo methods Mathematical model Cost function

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A Painting Artist Recognition System Based on image processing and Hierarchical SVM 5

A Painting Artist Recognition System Based on Image Processi...

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IEEE 5th conference on Knowledge Based Engineering and Innovation (KBEI)

作者： AlyanNezhadi, Mohammad M. Dabbaghan, Hossein Moghani, Samira Forghani, Majid Shahrood Nonprofit & Nongovt Higher Educ Inst Dept Comp Engn Shahrood Iran Ural Fed Univ Inst Nat Sci & Math Ekaterinburg Russia

ISBN: (纸本)9781728108728

Over the past years, forgery paintings of famous artists have been sold as original. In order to spot the fake painting, the experts make the decision based on personal experience and with the help of examining some characteristics of the painting and painter. Applying the image processing methods to artwork can reduce the need of the expert and provide quick and reliable results to recognize the originality of the artwork. In this paper, the proposed method is able to identify the painter of artwork using image processing and data mining techniques. The method consists of two typical main stages, feature extraction, and classification. In the feature extraction, 11 statistical features are extracted from each image. These features have been selected in such a way that maximize the distinction of painters. In the second step, the painters are identified by hierarchical classification. In order to evaluate the performance of proposed method, it has applied to a collection of 348 paintings from eight Iranian artists. The method has been able to identify the artwork painter with the accuracy about 84.21%.

关键词： Hierarchical Classification Support Vector Machine Iranian Painting Forgery

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Finding Logo and Seal in Historical Document images - An Object Detection Based Approach 5th

Finding Logo and Seal in Historical Document Images - An Obj...

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5th Asian conference on Pattern Recognition, ACPR 2019

作者： Chanda, Sukalpa Prasad, Prashant Kumar Hast, Anders Brun, Anders Martensson, Lasse Pal, Umapada Faculty of Computer Science Østfold University College Halden Norway Centre for Image Analysis Uppsala University Uppsala Sweden Department of Scandinavian Languages Uppsala University Uppsala Sweden RCC Institute of Information Technology Kolkata India CVPR Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783030414030

Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach. © Springer Nature Switzerland AG 2020.

关键词： Object detection

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