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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2015年 9082卷 63-74页

作者： Souza, Roberto Rittner, Letícia Machado, Rubens Lotufo, Roberto University of Campinas Campinas Brazil Center for Information Technology Renato Archer Campinas Brazil

ISBN: (纸本)9783319187198

Attribute filters and extinction filters are connected filters used to simplify greyscale images. the first kind is widely explored in the image processing literature, while the second is not much explored yet. Both kind of filters can be efficiently implemented on the max-tree. In this work, we compare these filters in terms of processing time, simplification of flat zones and reduction of max-tree nodes. We also compare their influence as a pre-processing step before extracting affine regions used in matching and pattern recognition. We perform repeatability tests using extinction filters and attribute filters, set to preserve the same number of extrema, as a pre-processing step before detecting Hessian-Affine and Maximally Stable Extremal Regions (MSER) affine regions. the results indicate that using extinction filters as pre-processing obtain a significantly higher (more than 5% on average) number of correspondences on the repeatability tests than the attribute filters. the results in processing natural images show that preserving 5% of images extrema using extinction filters achieve on average 95% of the number of correspondences compared to applying the affine region detectors directly to the unfiltered images, and the average number of max-tree nodes is reduced by a factor greater than 3. therefore, we can conclude that extinction filters are better than attribute filters with respect to preserving the number of correspondences found by affine detectors, while simplifying the max-tree structure. the use of extinction filters as a pre-processing step is recommended to accelerate image recognition tasks. © Springer international Publishing Switzerland 2015.

关键词： image recognition

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State recognition of Egg Embryo in Vaccines Production Based on SVM

State Recognition of Egg Embryo in Vaccines Production Based...

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2017 5th international Conference on Mechanical, Automotive and Materials Engineering（CMAME 2017）

作者： Yanwei Xu Menghui Chen Tancheng Xie School of Mechatronics Engineering Henan University of Science and Technology

the method for state recognition of egg embryo in biological vaccines production based on computer image processing and support vector machine was ***,the median filtering method was applied to eliminate noises in the images of egg embryo that were classified by the requirements of practical production,then the threshold segmentation method was used to segment the images,then the blood vessels and black blocks in the egg embryo images were selected as major characteristics to *** decision tree classification model with structure of binary tree was built on support vector machine,and the model was trained with the toolkit LIBSVM,the penalty factor C = 2,RBF kernel parameter γ=3.0512 e-05 and the precision of cross validation accuracy is 98.913%.Finally,100 egg embryos were randomly selected as the test samples to do pattern recognition,and compared with manual test *** comparison result showed that the discriminant accuracy of SVM classifier on weak embryo,polluted embryo or dead embryo was 100%,and it of SVM classifier on live embryo was 94.62%.

关键词： egg embryo pattern recognition support vector machine computer image processing

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Autonomous 3D Model Reconstruction and Its Intelligent Applications in Vehicle System Dynamics Part II: Applications

Autonomous 3D Model Reconstruction and Its Intelligent Appli...

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international symposium on Intelligent Systems and Informatics (SISY)

作者： Annamaria R. Varkonyi-Koczy Department of Measurement and Information Systems Integrated Intelligent Systems Japanese-Hungarian Laboratory Budapest University슠of슠Technology슠and슠Economics Budapest Hungary

3D model reconstruction plays a very important role in computer vision as well as in different engineering applications. the determination of the 3D model from multiple images is of key importance. One of the most important difficulties in autonomous 3D reconstruction is the (automatic) selection of the "significant" points which carry information about the shape of the 3D bodies i.e. are characteristic from the model point of view. Another problem to be solved is the point correspondence matching in different images. In this paper a 3D reconstruction technique is introduced, which is capable to determine the 3D model of a scene without any external (human) intervention. the method is based on recent results of image processing, epipolar geometry, and intelligent and soft techniques. Possible applications of the presented algorithm in vehicle system dynamics are also presented. the results can be applied advantageously at other engineering fields, like car-crash analysis, robot guiding, object recognition, supervision of 3D scenes, etc,, as well.

关键词： Intelligent vehicles Remotely operated vehicles Mobile robots Vehicle dynamics image reconstruction Application software Automotive engineering Layout Computer vision Shape

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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

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5th IEEE international Conference on Systems Biology, ISB 2011

作者： Wang, Mingjun Shen, Hong-Bin Akutsu, Tatsuya Song, Jiangning State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC 3800 Australia

ISBN: (纸本)9781457716669

Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). these results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations. © 2011 IEEE.

关键词： Amino acids

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A New Approach to Persian/Arabic Text Steganography

A New Approach to Persian/Arabic Text Steganography

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international Conference on Computer and Information Science (ACIS)

作者： M.H. Shirali-Shahreza M. Shirali-Shahreza Computer Engineering Department Yazd University Yazd Iran Computer Science Department Sharif University of Technology Tehran Iran

Conveying information secretly and establishing hidden relationship has been of interest since long past. Text documents have been widely used since very long time ago. therefore, we have witnessed different method of hiding information in texts (text steganography) since past to the present. In this paper we introduce a new approach for steganography in Persian and Arabic texts. Considering the existence of too many points in Persian and Arabic phrases, in this approach, by vertical displacement of the points, we hide information in the texts. this approach can be categorized under feature coding methods. this method can be used for Persian/Arabic watermarking. Our method has been implemented by Java programming language

关键词： Steganography Information security image coding Watermarking Cryptography Computer science Java Computer languages image processing pattern recognition

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Unconstrained Face Verification Based on Monogenic Binary pattern and Convolutional Neural Network

Unconstrained Face Verification Based on Monogenic Binary Pa...

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international Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Bilel Ameur Mebarka Belahcene Sabeur Masmoudi Ahmed Ben Hamida ATMS Advanced Technologies for Medecine and Signals National Engineering School of Sfax (ENIS) Sfax University Soukra street Km 3.5 B.P. 1173 Sfax Tunisia LI3C Laboratory of Identification Command Control and Communication Faculty of Science and Technology Mohamed Khider University Biskra BP 145 RP Biskra Algeria

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

Unconstrained Face Verification is still an important problem worth researching. the major challenges such as illumination, pose, occlusion and expression can produce more complex variations in both shape and texture of the face. In this paper, we propose a method based on Monogenic Binary pattern and Convolutional Neural Network (MBP-CNN) to improve the performance of face recognition system. For each facial image, the proposed method firstly extracts local features using Monogenic Binary pattern (MBP) which is an excellent and powerful local descriptor compared to the well-recognized Gabor filtering-based LBP models. then, we use Convolutional Neural Networks which is one of the best representative network architectures of deep learning in the literature, in order to extract more deep features. thus, the developed MBP-CNN has robustness to variations of illumination, occlusion, pose, expression, texture and shape by combining Monogenic Binary pattern and convolutional neural network. Moreover, MBP-CNN was more accurately represented by combining global and local information of facial images. Experiments demonstrate that our method provided competitive performance on the LFW database, compared to the others described in the state-of-the-art.

关键词： Face recognition Feature extraction Convolution Convolutional neural networks Lighting Deep learning Databases

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Digital Mammography 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Joan Martí Arnau Oliver Jordi Freixenet Robert Martí

ISBN: (数字)9783642136665

ISBN: (纸本)9783642136658

this volume of Springer’s Lecture Notes in Computer Science series comprises th the scienti?c proceedings of the 10 international Workshop on Digital Mammography (IWDM), which was held June 16–18, 2010 in Girona, Cata- nia. the IWDMmeetingstraditionallybringtogetheradiversesetofresearchers (physicists, mathematicians, computer scientists, engineers), clinicians (radi- ogists, surgeons) and representatives of industry, who are jointly committed to developing technology, not just for its own sake, but to support clinicians in the early detection and subsequent patient management of breast cancer. the IWDM conference series was initiated at a 1993 meeting of the SPIE Medical Imaging symposium in San Jose, CA, with subsequent meetings hosted every two yearsbyresearchersaroundthe world. Former workshopswereheld in York, England (1994), Chicago, IL USA (1996), Nijmegen, the Netherlands (1998), Toronto, Canada (2000), Bremen, Germany (2002), Durham, NC, USA (2004), Manchester, UK (2006) and Tucson, AZ USA (2008). Each of these scienti?c events was combined with very successful and focused industrial and research exhibits, which demonstrated the milestones of digital mammography over the years. A total number of 141 paper submissions from 21 countries were received. Each of these four-page abstract submissions was reviewed in a blind process by at least two members of the Scienti?c Committee, which led to a ?nal selection of 46 oral presentations and 57 posters during the two and one-half days of scienti?c sessions.

关键词： image processing and Computer Vision Health Informatics Imaging / Radiology Information Storage and Retrieval pattern recognition Computational Biology/Bioinformatics

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SECOND-Order Encoder and Restore Detail Decoder Network for image Semantic Segmentation 22

SECOND-Order Encoder and Restore Detail Decoder Network for ...

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Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Nan Dai Zhiqiang Hou Minjie Cheng Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9781450396899

Traditional convolution and pooling operations in the previous semantic segmentation methods will cause the loss of feature information due to limited receptive field size. they are insufficient to support an accurate image prediction result. To solve this problem, Firstly, we design a Second-Order Encoder to enlarge the feature receptive field and capture more semantic context information. Secondly, we design a Restore Detail Decoder to focus on processing the spatial detail information and refining the object edges. the experiments verify the effectiveness of the proposed approach. the results show that our method achieves competitive performance on two datasets, including PASCAL VOC2012 and Cityscapes with the mIoU of 80.13% and 76.31%, respectively.

关键词： Attention mechanism Computer Vision image segmentation Second-Order statistics

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CCD neural network processors for pattern recognition 91

CCD neural network processors for pattern recognition

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Proceedings of the 5th international Conference on Neural Information processing Systems

作者： Alice M. Chiang Michael L. Chuang Jeffrey R. LaFranchise MIT Lincoln Laboratory Lexington MA

ISBN: (纸本)9781558602229

A CCD-based processor that we call the NNC2 is presented. the NNC2 implements a fully connected 192-input, 32-output two-layer network and can be cascaded to form multilayer networks or used in parallel for additional input or output nodes. the device computes 1.92 × 109 connections/ sec when clocked at 10 MHz. Network weights can be specified to six bits of accuracy and are stored on-chip in programmable digital memories. A neural network pattern recognition system using NNC2 and CCD image feature extractor (IFE) devices is described. Additionally, we report a CCD output circuit that exploits inherent nonlinearities in the charge injection process to realize an adjustable-threshold sigmoid in a chip area of 40 × 80 µm2.

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Efficient Statistical Learning Framework with Applications to Human Activity and Facial Expression recognition

Efficient Statistical Learning Framework with Applications t...

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international Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Fatma Najar Sami Bourouis Marwan Alshar’e Roobaea Alroobaea Nizar Bouguila Ali H. Al Badi Ines Channoufi ENIT Laboratoire RISC Robotique Informatique et Systèmes Complexes Université de Tunis El Manar Tunis Tunisie ENIT Laboratoire: LR-SITI Signal Image et Technologies de 1’Information Université de Tunis E1 Manar Tunis Tunisie Taif University Taif Saudi Arabia Faculty of Computing Sciences Gulf College Muscat Oman The Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Centre for Postgraduate Studies and Research Gulf College Muscat Oman

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. the multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model's parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.

关键词： Bayes methods Face recognition Visualization Mixture models Hidden Markov models Feature extraction Vocabulary

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