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Classification by learning of wavelet and texture features 1st

Classification by learning of wavelet and texture features

引用

1st international conference on Frontiers in Smart Systems Technologies, ICFSst 2019

作者： Bagavathi, C. Saraniya, O. Department of Electronics and Communication Engineering Government College of Technology Coimbatore641013 India

ISBN: (纸本)9789811550287

image classification is one among the significant tasks that can benefit industry through advancement in science and technology. Texture images possess a specific pattern that can be utilized to uniquely identify a texture class. Classification of Texture images can be done through proper selection of parameter that uniquely identifies an image in a group containing images of different classes. machine learning approaches are currently the choice of implementation to provide accuracy and robustness in various fields like automatic image recognition, registration, and analysis. This work addresses machine learning methods to classify learnt texture samples through feature fusion of parameters obtained through wavelet and texture analysis. © Springer Nature Singapore Pte Ltd 2021.

关键词： Support vector machines

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The evaluation of wavelet and data driven feature selection for image understanding

The evaluation of wavelet and data driven feature selection ...

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1st international conference on Biomedical Engineering and Informatics

作者： Liu Jinshuo Zhang Dengyi Liu Siwen Fang Ying Zhang Ming Wuhan Univ Comp Sch Hubei Peoples R China Urban Construct Sch Jilin Dept Mech Engn Jilin Peoples R China Wuhan Huanpi Kaidi Water Serv Co Ltd Wuhan Peoples R China

ISBN: (纸本)9780769531182

image mining with pattern recognition methods has been widely used to understand the image knowledge. The general rule for the number of features is that the number can not be too much to improve the efficiency and speed of mining. This paper presents the wavelet methods for feature compression, and gives the evaluation of the wavelet methods comparing with data driven feature selection using the example of the project of image mining of pathogen yeast Cryptococcus Neoformans. The experiments show that the wavelet methods for feature compression are almost as effective as data driven feature selection to identify variance pathogen condition. The experiments are built on the training images set and evaluated using the new test set images with machine learning tool WEKA.

关键词： wavelet image mining data driven feature compression

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Optical Character recognition of Postmark Date Based on machine Vision

Optical Character Recognition of Postmark Date Based on Mach...

引用

2nd international conference on Engineering Materials, Energy, Management and Control

作者： You Fucheng Liu Yingjie Beijing Inst Graph Commun Informat & Mech Engn Sch Beijing Peoples R China

ISBN: (纸本)9783037853443

For the purpose of information management on postmark according to the date, the paper put forward a method of postmark date recognition based on machine vision, which could meet the demands of personal postmark collectors. On the basis of the relative theories of machine vision, image processing and pattern recognition, the overall process is introduced in the paper from postmark image acquisition to date recognition. Firstly, threshold method is used to generate binary image from smoothed postmark image. So region of date numbers could be extracted from binary image according to different region features. Then regions of date numbers which are connected or broken could be processed through mathematical morphology of binary image. Individual regions of date numbers are obtained for recognition. Finally, classification and pattern recognition based on support vector machine make date numbers classified and date recognition is implemented correctly.

关键词： machine vision postmark date mathematical morphology pattern recognition

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1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

1st FICR International Conference on Rising Threats in Exper...

引用

1st FICR international conference on Rising Threats in Expert Applications and Solutions, FICR-TEAS 2020

ISBN: (纸本)9789811560132

The proceedings contain 95 papers. The special focus in this conference is on Rising Threats in Expert Applications and Solutions. The topics include: A Novel User Authentication Protocol Using Biometric Data for IoT Networks;comparative study of Different machine learning Techniques in the Diagnosis of Dementia;automated Voter System by Using Fingerprint and Offline Data Set with Texture image processing;A Hybrid Cluster and PCA-Based Framework for Heart Disease Prediction Using Logistic Regression;a Deep learning Approach of Collaborative Filtering to Recommender System with Opinion Mining;Analytical Design of the DIS Architecture: The Hybrid Model;a Model for Effective Software Testing in Cloud Environment;heuristic Expert Evaluation of e-learning Application;image Query-Based Tablet Identification System by Examining Various Text recognition Classifiers;assessment of Needless Code in a Program;a Survey of Load Balanced Job Scheduling Schemes in Cloud Computing;analysis of Algorithms K-Means and Apriori for Data Mining;augmenting Cloud Service Discovery Using Ontology;Performance Analysis of AES, Blowfish and Rijndael: Cryptographic Algorithms for Audio;a Secured Cloud-Based Framework for image processing Using Ant Colony Optimization;Chili Plant Leaf Disease Detection Using SVM and KNN Classification;a Review on image Segmentation;ensemble Feature Extraction-Based Detection of Abnormal Mass Present in Medical images Using machine learning;classification of image steganography in Substitution Technique;query Caching Technique Over Cloud-Based MapReduce System: A Survey;challenges and Issues in the Existing Methodology for Dynamic Data Capturing of Ontology;contextual Information Retrieval Search Engine Challenges;a Review on American Sign Language Character recognition.

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Generalized morphological pattern spectrum for classification

Generalized morphological pattern spectrum for classificatio...

引用

The 1994 1st IEEE international conference on image processing

作者： Khorsandi, S. Venetsanopoulos, A.N. Department of Electrical Engineering University of Toronto TorontoONM5S 1A4 Canada

One of the most important problems in computer vision and image processing is signal representation and shape description. This usually involves mapping the signal from an original domain to a set of coefficients describing feature contents of the signal. These methods can be classified in two, global and local, categories. Global methods are sensitive to inaccurate segmentation and masking effects. Local methods, on the other hand, are better in partial pattern recognition but are generally application-dependent. Generalized pattern spectrum (GPS) is a local method and it has proved to be a robust shape descriptor which can also be used in partial pattern recognition. We have investigated the classification properties of this mapping on both noisy and partially missed objects. The results show that it is superior to the pecstrum method in the case of partially missed patterns. © 1994 IEEE.

关键词： Mapping

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machine learning based pattern recognition and Classification Framework Development 17

Machine Learning based Pattern Recognition and Classificatio...

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17th international conference on Control, Automation and Systems (ICCAS)

作者： Omarov, Batyrkhan Cho, Young Im Gachon Univ Dept Comp Engn Seoul 1342 South Korea

ISBN: (纸本)9788993215144

In this paper we describe implementation of several step pattern recognition framework. pattern recognition is the main aspect for different important areas such as video surveillance, biometrics, interactive game applications, human computer interaction and access control systems. These systems require fast real time detection and recognition with high recognition rate. In this paper we propose implementation of the pattern recognition system. In order to increase recognition rate of the system we apply image preprocessing and neural networks.

关键词： Neural networks recognition image preprocessing

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Iris Grid image Classification using Naive Bayes for Human Biometric System 1

Iris Grid Image Classification using Naive Bayes for Human B...

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1st international conference on Information System and Information Technology, ICISIT 2022

作者： Wibawa, Adhi Dharma Pamungkas, Yuri Perdana, Muhammad Ilham Rachmatika, Ratih Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Biometrics is a measurement of a person's physical and behavioral characteristics. Iris image is one of many biometrics data such as fingerprint, voice, face, and gait that can be used as an identifier. Iris is the colored part of the eye that helps the pupil see clearly and regulates light entry. Iris recognition is one of the important topics in biometric systems because of its unique pattern. Several related studies have been carried out to automatically obtain the most efficient method to understand and recognize the iris for human verification. This study proposes an analysis of iris images for biometrics systems with effective image processing techniques for system recognition. CVBL Iris image dataset was used in this study with 4320 iris images. After converting the iris image into a rectangle form, the Grid iris image experiment was implemented to find the highest accuracy. Several iris image grid-size were simulated to find the best accuracy. Multinomial Naive Bayes is used as a classifier. The Naive Bayes method is a machine learning method that uses probability calculations (rules-based). This algorithm uses probability and statistical methods, which predict future probabilities based on the previous data. The study results indicate that the proposed method can recognize the iris by identifying its fibers and encoding the fibers data using a grid image approach, with a classification accuracy of 92.37%, using an iris grid size of 70x50 pixels. This research can be useful for developing human biometric systems based on iris with a simple preprocessing approach. © 2022 IEEE.

关键词： Biometrics

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Analysis of application of machine learning based on bibliometrics in pattern recognition 2

Analysis of application of machine learning based on bibliom...

引用

2020 2nd international conference on Applied machine learning and Data Science, ICAMLDS2020

作者： Huang, Jin Tu, Wenyan Yuan, Qing Huazhong Univ Sci and Technol Lib Wuhan Hubei China

This paper analyzes the application of machine learning in pattern recognition by using bibliometrics methods, keyword cluster analysis, and burst terms detection. According to analysis, the number of published studies on the application of machine learning in pattern recognition has shown a rapid growth trend in recent years. The top five countries with published volumes are the United states, China, the United Kingdom, Germany, and India. According to the results of keyword co-occurrence cluster analysis, it is found that the hot research topics in this field mainly include feature extraction, support vector machines, and neural networks. Research topics include functional magnetic resonance imaging, deep learning and image processing, bioinformatics and fuzzy logic systems, semi-supervised learning and face recognition. According to the analysis of burst terms detection results, it is found that future research frontiers include big data, sensors, classifier integration, entropy, pattern classification, attribute reduction, image analysis, principal component analysis, fuzzy logic, Alzheimer's disease, magnetic resonance imaging, etc. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fuzzy logic

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Connected Ocular Screening System Using Smartphones 1

Connected Ocular Screening System Using Smartphones

引用

1st IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Daud, Marizuana Mat Zaki, Wan Mimi Diyana Wan Hussain, Aini Ahmad, Wan Siti Halimatul Munirah Wan Mutalib, Haliza Abdul Univ Kebangsaan Malaysia Fac Engn & Built Environm Ctr Integrated Syst Engn & Adv Technol INTEGRA Bangi Malaysia Univ Kebangsaan Malaysia Fac Hlth Sci Sch Healthcare Sci Optometry & Vis Sci Programme Bangi Malaysia

ISBN: (纸本)9781538652671

This article presents an extensivc survey of recent studies on mobile applications for ocular diseases detection. Survey results show that smartphone applications are the current niche in healthcare, particularly in the diagnosis of ocular diseases. This environment has led to the development of the proposed framework of a corneal disease screening system using mobile phones to overcome the aforementioned limitations. Typically, such systems are integrated with the Internet of Things and image processing with machine learning techniques to process the anterior segment of photographed images. Thus, they are able to serve as automatic systems to assist optometrists in clinical diagnosis.

关键词： Smartphone Ocular diseases image processing Internet of Things Screening system

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Recent Developments in Electronics and Communication Systems - Proceedings of the 1st international conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

Recent Developments in Electronics and Communication Systems...

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1st international conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

ISBN: (纸本)9781643683607

The proceedings contain 108 papers. The topics discussed include: query recommendation for search engine results using machine learning;web defenselessness recognition against case of cross site demand fake;modelling and controlling a buck converter using shunt active power filter;quantum computing in image processing;research into wind speeds and energy generation in India: an overview;comparative analysis of approximate integer and floating-point multiplier;health information sharing in cloud environment using modular encryption standard;stochastic computing solutions challenges and application;a new model that figures out sub-emotionality and the changes in it over time;interference-normalized least mean square algorithm: a comparative study;image classification of land use land cover of Bengaluru city using convolutional neural network;the journey of logarithm multiplier: approach, development and future scope;and bibliometric analysis on identifying plant, crop diseases using machine learning and deep learning.

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