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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Santosh, K. C. Univ South Dakota Dept Comp Sci 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9789811048593;9789811048586

One of the key difficulties in graphics recognition domain is to work on complex and composite symbol recognition, retrieval and spotting. This paper covers a quick view on complex and composite symbol recognition, which is inspired by real-world industrial problem. Considering it as a pattern recognition problem, three different approaches: statistical, structural and syntactic are taken into account. It includes fundamental concepts or techniques and research standpoints or directions derived by a real-world application.

关键词： Symbol recognition Complex and composite symbols statistical structural and syntactic approaches Document processing

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Progressive Review Towards Deep learning Techniques 1st

Progressive Review Towards Deep Learning Techniques

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1st international conference on Data Engineering and Communication Technology (ICDECT)

作者： Chaudhari, Poonam Agarwal, Himanshu Symbiosis Inst Technol Pune Maharashtra India

ISBN: (纸本)9789811016752;9789811016745

Deep learning is a thing of tomorrow which is causing a complete drift from shallow architecture to deep architecture and an estimate shows that by 2017 about 10 % of computers will be learning rather than processing. Deep learning has fast growing effects in the area of pattern recognition, computer vision, speech recognition, feature extraction, language processing, bioinformatics, and statistical classification. To make a system learn, deep learning makes use of a wide horizon of machine learning algorithms. Gene expression data is uncertain and imprecise. In this paper, we discuss supervised and unsupervised algorithms applied to gene expression dataset. There are intermediate algorithms classified as semi-supervised and self taught which also play an important role to improve the prediction accuracy in diagnosis of cancer. We discuss deep learning algorithms which provide better analysis of hidden patterns in the dataset, thus improving the prediction accuracy.

关键词： Deep learning Supervised and unsupervised algorithm Semi-supervised and self-taught algorithm Gene expression data

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Local Binary pattern and Its Variants for Target recognition in Infrared imagery

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1st international conference on Computer Vision and image processing (CVIP)

作者： Akula, Aparna Ghosh, Ripul Kumar, Satish Sardana, H. K. CSIR CSIO Chandigarh 160030 India Acad Sci & Innovat Res AcSIR Madras Tamil Nadu India

ISBN: (纸本)9789811021046;9789811021039

In this research work, local binary pattern (LBP)-based automatic target recognition system is proposed for classification of various categories of moving civilian targets using their infrared image signatures. Target recognition in infrared images is demanding owing to large variations in target signature and limited target to background contrast. This demands robust features/descriptors which can represent possible variations of the target category with minimal intra class variance. LBP, a simple yet efficient texture operator initially proposed for texture recognition of late is gaining popularity in face and object recognition applications. In this work, the suitability of LBP and two of its variants, local ternary pattern (LTP), complete local binary pattern (CLBP) for the task of recognition in infrared images has been evaluated. The performance of the method is validated with target clips obtained from 'CSIR-CSIO moving object thermal infrared imagery dataset'. The number of classes is four-three different target classes (Ambassador, Auto and Pedestrian) and one class representing the background. Classification accuracies of 89.48 %, 100 % and 100 % were obtained for LBP, LTP and CLBP, respectively. The results indicate the suitability of LBP operator for target recognition in infrared images.

关键词： Local binary pattern Local ternary pattern Complete local binary pattern Infrared recognition Support vector machine

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Performance Analysis of Texture image Retrieval in Curvelet, Contourlet, and Local Ternary pattern Using DNN and ELM Classifiers for MRI Brain Tumor images

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1st international conference on Computer Vision and image processing (CVIP)

作者： Pandian, A. Anbarasa Balasubramanian, R. Manonmaniam Sundaranar Univ Dept Comp Sci & Engn Tirunelveli India

ISBN: (纸本)9789811021046;9789811021039

The problem of searching a digital image in a very huge database is called content-based image retrieval (CBIR). Texture represents spatial or statistical repetition in pixel intensity and orientation. When abnormal cells form within the brain is called brain tumor. In this paper, we have developed a texture feature extraction of MRI brain tumor image retrieval. There are two parts, namely feature extraction process and classification. First, the texture features are extracted using techniques like curvelet transform, contourlet transform, and Local Ternary pattern (LTP). Second, the supervised learning algorithms like Deep Neural Network (DNN) and Extreme learning machine (ELM) are used to classify the brain tumor images. The experiment is performed on a collection of 1000 brain tumor images with different modalities and orientations. Experimental results reveal that contourlet transform technique provides better than curvelet transform and local ternary pattern.

关键词： CBIR Texture Brain tumor DNN and ELM

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DERMOSCOPIC image ANALYSIS USING pattern recognition TECHNIQUES FROM REGION OF INTEREst (ROI) FOR DETECTION OF MELANOMA 16

DERMOSCOPIC IMAGE ANALYSIS USING PATTERN RECOGNITION TECHNIQ...

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international conference on machine learning and Cybernetics (ICMLC)

作者： Sakeena, Muntaha Riasat, Rubata Sadiq, Abdul Hannan Ahmad, Zeeshan Riaz, Farhan Natl Univ Sci & Technol Islamabad 44000 Pakistan Chinese Acad Sci Inst Software Natl Engn Res Ctr Fundamental Software Beijing 100190 Peoples R China COMSATS Inst Informat Technol Islamabad 45550 Pakistan

ISBN: (纸本)9781538604083

Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background. ROI of image is extracted out by using one of the segmentation techniques named as adaptive thresholding with morphological operators. After extracting ROI, we analyzed the classification results by using texture and color features on the extracted region and compared the results. The images were obtained from one of the hospitals of Portugal utilizing dermoscopy as an imaging methodology. Our extracted algorithm shows better performance than conventional thresholding and based on experimental results it can be concluded that the color features outperform the classification results but it gives more accurate results by combining color and texture features extracted from ROI.

关键词： pattern recognition ROI Segmentation SVM Classification Texture

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Research on Support Vector machines Method Modeling for Rice Potassium Nutrition Diagnosis

Research on Support Vector Machines Method Modeling for Rice...

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2018 international conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Qiong Zhou Hongyun Yang Jun Yang Yuting Sun Aizhen Sun Wenji Yang School of Computer and Information Engineering Jiangxi agricultural university Key Laboratory of Agricultural Information Technology of Colleges and Universities in Jiangxi Province College of Software Engineering Jiangxi agricultural university

ISBN: (纸本)9781510863064

A machine learning method was used to establish a diagnostic model of potassium nutrition for rice obtained from image processing techniques. In this study, super hybrid rice "Liangyoupeijiu" was used as experimental object to set up four kinds of rice cultivation experiments at different potassium fertilization levels, the image data of a total of 1920 groups of the 1st leaves and the 2nd leaves, the 3rd leaves, and their corresponding sheaths were obtained by scanning with a scanner. Nineteen rice characteristic indexes were obtained. Support vector machine was used to establish the diagnostic model of potassium nutrition in nineteen rice characteristic indexes, and to diagnose and identify the potassium nutrition in rice. The experimental results show that the identification method based on image processing and SVM is suitable for the diagnosis of potassium nutrition of the 3rd leaves in rice young panicle differentiation stage with an accuracy of 89%, which provides a reliable and universal method for studying the recognition of potassium nutrition in rice and can meet the needs of agronomic research.

关键词： rice potassium nutrition diagnosis image processing support vector machines

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Computational Intelligence and Its Applications 1st ed. 2018

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丛书名： IFIP Advances in Information and Communication Technology

2018年

作者： Abdelmalek Amine Malek Mouhoub Otmane Ait Mohamed Bachir Djebbar

ISBN: (数字)9783319897431

ISBN: (纸本)9783319897424

This book constitutes the refereed proceedings of the 6th IFIP TC 5 international conference on Computational Intelligence and Its Applications, CIIA 2018, held in Oran, Algeria, in May 2018. The 56 full papers presented were carefully reviewed and selected from 202 submissions. They are organized in the following topical sections: data mining and information retrieval; evolutionary computation; machine learning; optimization; planning and scheduling; wireless communication and mobile computing; Internet of Things (IoT) and decision support systems; pattern recognition and image processing; and semantic web services.

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Unlocking Textual Content from Historical Maps - Potentials and Applications, Trends, and Outlooks 1st

Unlocking Textual Content from Historical Maps - Potentials ...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Chiang, Yao-Yi Univ Southern Calif Spatial Sci Inst Los Angeles CA 90089 USA

ISBN: (纸本)9789811048593;9789811048586

Digital map processing has been an interest in the image processing and pattern recognition community since the early 80s. With the exponential growth of available map scans in the archives and on the internet, a variety of disciplines in the natural and social sciences grow interests in using historical maps as a primary source of geographical and political information in their studies. Today, many organizations such as the United states Geological Survey, David Rumsey Map Collection, ***, and National Library of Scotland, store numerous historical maps in either paper or scanned format. Only a small portion of these historical maps is georeferenced, and even fewer of them have machine-readable content or comprehensive metadata. The lack of a searchable textual content including the spatial and temporal information prevents researchers from efficiently finding relevant maps for their research and using the map content in their studies. These challenges present a tremendous collaboration opportunity for the image processing and pattern recognition community to build advance map processing technologies for transforming the natural and social science studies that use historical maps. This paper presents the potentials of using historical maps in scientific research, describes the current trends and challenges in extracting and recognizing text content from historical maps, and discusses the future outlook.

关键词： Digital map processing Text recognition Optical character recognition Historical maps Geographic information system Natural science Social science Biology Spatial humanity

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learning Representations through Ensemble of Fuzzy C-Means for Identification of Retinal Pathologies 17

Learning Representations through Ensemble of Fuzzy C-Means f...

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international conference on Internet of Things and machine learning (IML)

作者： Karri, S. P. K. Chakraborty, Debjani Ray, Ajoy K. Chatterjee, Jyotirmoy IIT Kharagpur Kharagpur W Bengal India IIEST Sibpur Howrah India

ISBN: (纸本)9781450352437

Retinal image analysis is employed to automate screening process through low-level feature extraction and classification. Supervised classification approaches are dependent on kernels or distance metrics to handle complex manifolds as they warp feature space for effective classification with less complex boundaries between classes. Proposed approach identifies control points (Voronoi diagram) by exploring the structures of class specific manifolds which constructs complex boundaries with piecewise linear nature. Such a framework has less number of hyperparameters to tweak resulting easy control and understanding of the system. The learning characteristics of the proposed algorithm has been depicted on toy and optical coherence tomography data set. It has illustrated effective performance in identification of retinal pathologies and compared against off-the-shelf classifiers with various parameters. Proposed algorithm is capable of accommodating unsupervised approaches other than Fuzzy C-Means reflecting its adaptability.

关键词： pattern recognition Fuzzy Logic Fractals Voronoi

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Semi-Automatic Analysis for Unidimensional Immunoblot images to Discriminate Breast Cancer Cases Using Time Series Data Mining

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international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2018年第1期32卷 1860004-1860004页

作者： Sanchez-Silva, Diana M. Acosta-Mesa, Hector G. Romo-Gonzalez, Tania Univ Veracruzana Ctr Invest Inteligencia Artificial Xalapa Veracruz Mexico Univ Veracruzana Inst Invest Biol Area Biol & Salud Integral Xalapa Veracruz Mexico

Breast cancer (BC) is one of the leading causes of death in adult women worldwide and the best way to reduce mortality and improve prognosis is through early diagnosis. Thus, it is necessary to optimize diagnostic methods;one option could be the automatic detection of patterns in 1D-II. In that respect, through recent analysis of unidimensional Immunoblot images (1D-II), it was possible to distinguish between women with and without breast disease using as a discrimination criterion the presence of autoantibodies (bands) in their blood. However, the analysis of 1D-II is a difficult task even for an expert, generating great subjectivity and complexity in the process of interpretation. In the present study, a semi-automatic methodology for the bands' analysis contained in the 1D-II's was implemented and evaluated, the bands were extracted using digital image processing techniques. This was possible through the recognition of banding patterns represented as time series to distinguish between three classes: women with breast cancer (BC), women with benign breast pathology (BBP) and women without breast pathology (H). The classification was performed using the machine learning algorithm k-nearest neighbors (KNN) with different parameters over the time series representation. The semi-automatic method here presented was able to reduce the time, complexity and subjectivity of the image analysis with the performance metrics compared, obtaining similar percentages for both representations. With the traditional analysis, binary representation [Accuracy 72.8%, Precision 73.42% for three classes (BC, BBP and H) and Accuracy 90.91% Accuracy 92.55% Sensitivity 93.57% and Specificity 92.99% for two classes (BC and H)], versus Time series representation [Accuracy 66.4%, Precision 67.07% for three classes (BC, BBP and H) and Accuracy 86.36% Accuracy 87.31% Sensitivity 95.86% and Specificity 85.56% for two classes (BC and H)].

关键词： Breast cancer unidimensional immunoblot protein bands time series data mining semi-automatic method digital image processing

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