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检索条件"任意字段=3rd Iberian Conference on Pattern Recognition and Image Analysis"

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COMPLEXIS 2018 - Proceedings of the 3rd International conference on Complexity, Future Information Systems and Risk

COMPLEXIS 2018 - Proceedings of the 3rd International Confer...

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3rd International conference on Complexity, Future Information Systems and Risk, COMPLEXIS 2018

ISBN: (纸本)9789897582974

The proceedings contain 19 papers. The topics discussed include: communicability in networked systems - implications for stability, spatial efficiency and dynamical process;why so emotional? an analysis of emotional Bot-generated Content on Twitter;PaaS-BDP a multi-cloud architectural pattern for big data processing on a platform-as-a-service model;a new pricing model for freelancing platforms based on financial and social capital;on code-prompting auto-catalytic sets and the origins of coded life;using tag based semantic annotation to empower client and REST service interaction;a novel algorithm for bi-level image coding and lossless compression based on virtual ant colonies;proposing a holistic framework for the assessment and management of manufacturing complexity through data-centric and human-centric approaches;CBR-mining approach to improve learning system engineering in a collaborative e-learning platform;a new pricing model for freelancing platforms based on financial and social capital;on the public perception of police forces in riot events - the role of emotions in three major social networks during the 2017 G20 riots;complexity evaluation with business process modeling and simulation;modeling and implementation of a Ludic application using simple reactive agents - hydrological impact of high Andean ecosystems;bio-backfill: a scheduling policy enhancing the performance of bioinformatics workflows in shared clusters;and the fuzzy mortality model based on quaternion theory.

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A Level set based Method for Coastline Detection of SAR images 3

A Level set based Method for Coastline Detection of SAR Imag...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Modava, Mohammad Akbarizadeh, Gholamreza Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Ahvaz Iran

ISBN: (纸本)9781509064540

Coastline detection is important for surveying and mapping reasons. This paper presents an efficient approach to detect coastlines from synthetic aperture radar (SAR) images. The proposed approach is based on fuzzy c-means (FCM) clustering and level set segmentation. It consists of a sequence of image processing algorithms. First, the FCM clustering is applied to the input SAR image. Second, the level set method has been used the result of FCM clustering as initial contours to extract the coastline. The method proposed in this paper, does not require determining the initial shape for active contour. Also it is robust to speckle noise. Experimental results on high and low resolution SAR images show the good performance of this method for coastline detection.

关键词： Coastline detection Spatial FCM clustering level set method SAR image

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Nonlinear analysis of Video images using Deep Recurrent Auto-Associative Neural Networks for Facial Understanding 3

Nonlinear Analysis of Video Images using Deep Recurrent Auto...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Moghadam, Saeed Montazeri Seyyedsalehi, Seyyed Ali Amirkabir Univ Technol Tehran Polytech Dept Biomed Engn Tehran Iran

ISBN: (纸本)9781509064540

Preliminary experiments on the deep architectures of the Auto-Associative Neural Networks demonstrated that they have a fascinating ability in complex nonlinear feature extraction, manifold formation and dimension reduction. However, they should successfully pass a serious challenge of training. Furthermore, using the valuable information inclined in video sequences is so helpful in manifold formation and recognition tasks. Considering sequential information, the recurrent networks are widely used in dynamical modeling. This paper presents a novel nine-layer deep recurrent auto-associative neural network which is capable of simultaneously extracting three different information (identity, emotion and gender) from videos of the face. The proposed framework is extensively evaluated on extended Cohn-Kanade database in analyzing dynamical facial expression. The experimental results demonstrate that the recognition rates of emotion and gender are 95.35% and 97.42%, respectively which is comparable with other state-of-the-art.

关键词： Nonlinear Video analysis Deep Recurrent Auto-Associative Neural Networks Emotion recognition Gender recognition

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Enhance Support Relation Extraction Accuracy Using Improvement Of Segmentation In RGB-D images 3

Enhance Support Relation Extraction Accuracy Using Improveme...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Ahmadi, Shokouh S. Khotanlou, Hassan Bu Ali Sina Univ RIV Lab Dept Comp Engn Hamadan Iran

ISBN: (纸本)9781509064540

Todays, increasing in machine vision fields and applications make it necessary to have accurate scene understanding and analyzing. Support relation extraction is one of the most important and critical problem in robotic and machine vision task. In this article, we enhance support relation extraction accuracy using improvement of segmentation. Having the depth, moreover the color, in RGB-D images enable us to obtain accurate and precise support relation. In this paper an approach is also presented to redress discontinuities in point cloud occurred while recording. Experimental result shows the accuracy of the extracted support relation will be significantly increase after segmentation improvement.

关键词： Support relation extraction 3D segmentation Scene understanding RGB-D image segmentation

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Coarse-to-Fine Parameter Tuning for Content-Based Object Categorization 3

Coarse-to-Fine Parameter Tuning for Content-Based Object Cat...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Moshkelgosha, Vahid Behzadi-Khormouji, Hamed Yazdian-Dehkordi, Mahdi Yazd Univ Comp Engn Dept Yazd Iran

ISBN: (纸本)9781509064540

Object categorization is an interesting application in computer vision. To develop an efficient system for this purpose, finding an appropriate classifier in conjunction with a suitable feature is essential. Most classifiers and features have one or more parameters to be tuned through cross validation. In this paper, we examined a number of classifiers with several feature descriptors and advise an efficient hybrid feature descriptor for object categorization. Besides, we propose a coarse-to-fine parameter tuning method to avoid exhaustive search within various hyper-parameter of the classifiers. The experimental results provided on a subset of COREL dataset shows the efficiency of the advised hybrid feature and the proposed tuning parameters.

关键词： Corel dataset image classification k-nearest neighbor Support Vector Machine AdaBoostM2 feature descriptor

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A New Pansharpening Method Using Multi Resolution analysis Framework and Deep Neural Networks 3

A New Pansharpening Method Using Multi Resolution Analysis F...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Azarang, Arian Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781509064540

Present work describes a promising method in image fusion remote sensing applications. Due to intrinsic properties of deep neural networks (DNN) in image reconstruction, a novel pansharpening method presents based on multi resolution analysis (MRA) framework. First, a low resolution Panchromatic (LR Pan) image is constructed using its high resolution (HR) version. Then, the relationship between LR/HR Pan images are used to reconstruct the HR Multispectral (MS) image utilizing the LR MS. For our work, two datasets are considered and for each of them, the effect of several parameters such as window size, overlapping percentage and number of training samples on spectral distortion are considered. After training DNN, the LR MS image is given to the trained network as input to obtain MS image with better spatial details and finally the fused image obtains using MRA framework. Comparison with state of art methods, the proposed method has better results from objective and visual perspectives.

关键词： Pansharpening multi resolution analysis deep neural networks

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Shrinkage Estimator based Common Spatial pattern for Multi-Class Motor imagery Classification by Hybrid Classifier 3

Shrinkage Estimator based Common Spatial Pattern for Multi-C...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Sharbaf, Mohammadreza Edalati Fallah, Ali Rashidi, Saeid Amirkabir Univ Technol Fac Biomed Engn Tehran Iran Islamic Azad Univ Sci & Res branch Fac Biomed Engn Tehran Iran

ISBN: (纸本)9781509064540

Motor imagery BCI is a system that is very useful to help people with disabilities who can't move their limbs. These systems use brain activity patterns that are made from motor imagery without actual movement. In this paper, we proposed enhanced One Versus One (OVO) structure to classify EEG-based multi-class motor imagery signals. Also, shrinkage estimator based Common Spatial pattern (CSP) is used to overcome disadvantages of conventional CSP. Shrinkage estimator is a procedure to estimate covariance matrix that regularizes CSP versus overfitting. The results of four-class classification of BCI competition IV dataset 2a, show that the performance is improved to 0.61 kappa score.

关键词： BCI motor imagery OVO shrinkage estimator multi-class

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Content-based image Retrieval with Color and Texture Features in Neutrosophic Domain 3

Content-based Image Retrieval with Color and Texture Feature...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Rashno, Abdolreza Sadri, Saeed Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9781509064540

In this paper, a new content-based image retrieval (CBIR) scheme is proposed in neutrosophic (NS) domain. For this task, RGB images are first transformed to three subsets in NS domain and then segmented. For each segment of an image, color features including dominant color discribtor (DCD), histogram and statistic components are extracted. Wavelet features are also extracted as texture features from the whole image. All extracted features from either segmented image or the whole image are combined to create a feature vector. Feature vectors are presented for ant colony optimization (ACO) feature selection which selects the most relevant features. Selected features are used for final retrieval process. Proposed CBIR scheme is evaluated on Corel image dataset. Experimental results show that the proposed method outperforms our prior method (with the same feature vector and feature selection method) by 2% and 1% with respect to precision and recall, respectively. Also, the proposed method achieves the improvement of 13% and 2% in precision and recall, respectively, in comparison with prior methods.

关键词： Content-based image retrieval neutrosophic domain ant colony optimization color features texture features

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Introducing a New Method Robust Against Crop Attack In Digital image Watermarking Using Two-Step Sudoku 3

Introducing a New Method Robust Against Crop Attack In Digit...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Goli, Mohammad Shahab Naghsh, Alireza Islamic Azad Univ Najafabad Branch Digital Proc & Machine Vis Res Ctr Opt Commun Najafabad Iran Islamic Azad Univ Najafabad Branch Dept Elect Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Digital Proc & Machine Vision Res Ctr Najafabad Iran

ISBN: (纸本)9781509064540

Several methods are exploited to watermark digital images as a safety measure for storing information, but an attacker can destroy the information by cropping a segment of the watermarked image. In recent years, numerous schemes were proposed that reduce the impact of such attacks. A new method has been proposed to confront cropping attack that is carried out using two sudoku tables. In this method, the watermark image is scattered in two sudoku table layouts with different solutions and is watermarked in the host image. Using this method, the watermark image is repeated 81 times in the host image, and to this effect the watermark image can be reconstructed using other segments when cropped by the attacker. Both sudokus used in this paper are in the classic 9x9 form and using this method, resistance to cropping attacks increases up to 98.8%.

关键词： component cropping attack digital image watermarking spatial domain sudoku

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SAR image DENOISING USING HOMOMORPHIC AND SHEARLET TRANSFORMS 3

SAR IMAGE DENOISING USING HOMOMORPHIC AND SHEARLET TRANSFORM...

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3rd International conference on pattern analysis and image analysis (IPRIA)

作者： Rezaei, Hossein Karami, Azam Shahid Bahonar Univ Kerman Fac Phys Kerman Iran Univ Antwerp IMinds Visionlab Antwerp Belgium

ISBN: (纸本)9781509064540

Recently, denoising of Synthetic Aperture Radar (SAR) images has gained particular attention. SAR image is usually affected by speckle noise. In this paper a new method for speckle noise reduction of SAR images using shearlet transform (ST) is introduced. ST could significantly remove the Gaussian noise therefore in the proposed method first, noisy images are converted to a domain which type of noise is Gaussian using homomorphic transform (HT). Second, 2D shearlet is applied to the data. Third, the hard thresholding is used in order to denoise the shearlet coefficients. Finally reconstructed denoised images are obtained by applying the inverse shearlet and homomorphic transforms. The proposed method (ST-HT) is compared with state of art denoising algorithms on SAR images. Obtained results show the superiority of the proposed approach.

关键词： Denoising Synthetic Aperture Radar Shearlet transform

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