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10th Biennial International Conference on pattern Recognition and Machine Intelligence (PReMI)

作者： Nath, Pranay Mondal, Surajit Ghosh, Lidia Maulana Abul Kalam Azad Univ Technol Kolkata West Bengal India Univ Engn & Management Inst Engn & Management Kolkata West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Advancements in medical science have led to new approaches for preventing, diagnosing, and treating brain tumors, studied by researchers across different fields. The accurate identification of tumors in MRI scans can assist in disease identification, treatment evaluation, and radiation-based therapies. Currently, humans manually perform this task, but research has explored the integration of computer processing in MRI analysis. While MRIs provide a comprehensive view of the brain to identify tumors, they lack accuracy in pinpointing their location and size. To address this, an improved version of local binary pattern (LBP) encoded optimized Convolutional neural network is proposed to improve diagnostic accuracy. LBP captures texture information in a local neighborhood of each pixel, providing additional features to learn from and enhancing the accuracy of texture-based image classification. The model is evaluated on the Figshare dataset through multiple experiments.

关键词： Optimized Convolutional Neural Network local binary pattern Brain tumor Figshare dataset MRI analysis

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local binary pattern-based features for text identification of web images

Local binary pattern-based features for text identification ...

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International Conference on pattern Recognition

作者： Jung, Insook Oh, Il-Seok Department of Computer Science and Engineering Chonbuk National University Jeonju Korea Republic of

ISBN: (纸本)9780769541099

We present a method of robustly identifying a text block in complex web images. The method is a MLP (Multilayer perceptron) classifier trained on LBP (local binary patterns), wavelet and shape feature spaces. Especially, we propose adaptive masks of LBP which responses flexibly to various character sizes. Most of previous works use fixed mask size or multi level scales by pyramid schemes, which may have weakness in dealing with diverse size of text. Experiments carried out on 100 web images show promising results. © 2010 IEEE.

关键词： local binary pattern

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local binary pattern matching for fast retina map relocalization using the slit-lamp 19

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19th Iberoamerican Congress on pattern Recognition, CIARP 2014

作者： Linhares, Rodrigo T.F. Richa, Rogério Moraes, Ricardo Comunello, Eros von Wangenheim, Aldo Brazilian National Institute for Digital Convergence - INCoD Brazil

ISBN: (纸本)9783319125671

Computer assistance has the potential for increasing safety and accuracy during retinal laser treatment using the slit-lamp. In this context, intra-operative retinal mapping is a fundamental requirement to overlay relevant pre-operative information for surgeons. Retinal mapping using the slit-lamp is a challenging task, due to disturbances such as lens distortions, occlusions and glare. Such disturbances have a negative impact on the duration of the mapping procedure, consequently affecting its acceptance in clinical practice. To cope with these visual tracking interruptions, we propose a fast retina map relocalization strategy based on template-matching, using local binary patterns, which are suitable for the retina’s texture. We perform extensive experiments to show the superior accuracy and computational efficiency of the proposed approach in comparison with feature-based methods. © Springer International Publishing Switzerland 2014.

关键词： local binary pattern

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local binary pattern orientation based face recognition 40

Local binary pattern orientation based face recognition

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Shen, Yi-Kang Chiu, Ching-Te Department of Computer Science National Tsing Hua University Taiwan

ISBN: (纸本)9781467369978

Scale-invariant feature transform (SIFT) is a feature point based method using the orientation descriptor for pattern recognition. It is robust under the variation of scale and rotation changes, but the computation cost increases with its feature points. local binary pattern (LBP) is a pixel based texture extraction method that achieves high face recognition rate with low computation time. We propose a new descriptor that combines the LBP texture and SIFT orientation information to improve the recognition rate using limited number of interest points. By adding the LBP texture information, we could reduce the SIFT orientation number in the descriptor by half. Therefore, we could reduce the computation time while keeping the recognition rate. In addition, we propose a matching method to reserve the effective matching pairs and calculate the similarity between two images. By combining these two methods, we can extract different face details effectively and further reduce computational cost. We also propose an approach using the region of interest (ROI) to remove the useless interest points for saving our computation time and maintaining the recognition rate. Experimental results demonstrate that our proposed LBP orientation descriptor can reduce around 30% computation time compared with the original SIFT descriptor while maintaining the recognition rate in FERET database. Adding the ROI at our proposed LBP orientation descriptor can reduce around 58% computation time compared with the original SIFT descriptor in FERET database. For extended YaleB database, our method has 1.2% higher recognition rate than original SIFT method and reduces 28.6% computational time. The experimental results with adding ROI reduces 61.9% computation time for YaleB database. © 2015 IEEE.

关键词： face recognition local binary pattern scale-invariant feature transform

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local binary pattern based face recognition with automatically detected fiducial points

Local binary pattern based face recognition with automatical...

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作者： Lenc, Ladislav Král, Pavel Department of Computer Science and Engineering University of West Bohemia Plzeň Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Plzeň Czech Republic

This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (TK). We focus on popular local binary patterns (LPBs) that are frequently used in this field with high recognition accuracy. One drawback of current LBP based methods is that the positions and number of the fiducial points are fixed. These points thus do not reflect the properties of a particular image whereas we believe it is beneficial to identify the most representative ones. The main contribution consists in proposing and comparing several LBP-based approaches that detect such points fully automatically. We use a set of Gabor filters for this task. local extrema in the filter responses are detected and then used as the feature points. The number of points is further reduced by a clustering algorithm. Our approaches also differ from the other ones in the matching procedure. The proposed methods are evaluated on three standard corpora: ORL, FERET, AR face database and our TK dataset containing uncontrolled face images. Recognition results clearly show high quality of the proposed approaches that outperform significantly the baseline LBP approach on all corpora. The benefits of our methods are particularly evident in the case of real non-controlled images (TK corpus) where the accuracy is increased by more than 20% in absolute value. © 2016 - IOS Press and the author(s). All rights reserved.

关键词： local binary pattern

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local binary pattern (LBP) Extraction for Coffee Classification Using Modular Neural Networks (MNN)

Local Binary Pattern (LBP) Extraction for Coffee Classificat...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Siregar, Martua Andri Rahmad Syah, B.Y. Muliono, Rizki Muhathir Khairina, Nurul Lubis, Andre Hasudungan Universitas Medan Area Medan Faculty of Engineering Indonesia

ISBN: (纸本)9798350376210

This study aims to improve the accuracy of coffee bean classification by utilizing local binary pattern (LBP) extraction with Modular Neural Network (MNN). Coffee, one of Indonesia's leading commodities, plays a vital role in the country's economy. Coffee production, especially arabica and robusta types, experienced fluctuations from 2018 to 2020, motivating this research to explore the latest technology for improving the accuracy of coffee type classification. This research focuses on the three main coffee types in Indonesia, namely arabica, robusta, and liberica. Although arabica and robusta are more common, liberica coffee is also gaining attention in the Indonesian coffee industry. This study illustrates the steps of LBP extraction, an important feature technique in image processing and pattern recognition, using a flowchart to facilitate the process. We tested the effectiveness of LBP extraction on MNN to enhance the quality of classification. It was able to correctly classify 2,000 images of coffee beans 87.25% of the time. Thus, this research makes an important contribution to the development of technology in the context of coffee plantations in Indonesia. LBP in MNN not only improves coffee bean classification accuracy but also opens up further development opportunities in the application of technology in plantations and agro-processing sectors. © 2024 IEEE.

关键词： local binary pattern

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local binary pattern and Census, Which One is Better in Stereo Matching 7

Local Binary Pattern and Census, Which One is Better in Ster...

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7th NAFOSTED Conference on Information and Computer Science, NICS 2020

作者： Nguyen, Vinh Dinh Nguyen, Phuc Hong Debnath, Narayan C. Eastern International University Department of Software Engineering Binh Duong Viet Nam

ISBN: (纸本)9780738105536

local binary patterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have been successfully applied in various applications, such as texture classification, face recognition, object detection, and segmentation, while Census has only been used to investigate stereo correspondence problem. Therefore, this paper investigates the LBP and Census using a non-local-based stereo matching method in order to analyze and discuss the main differences between LBP and Census. Moreover, as many as one hundred variants of LBP have been published to solve various problems, while only a few modifications of the Census exist for stereo matching. Comprehensive experiments with the indoor, Middlebury dataset stated that some novel LBPs that perform well in texture classification and face recognition also work well in a stereo matching application. In most cases, LBP and its variants compare favorably to Census in terms of the accuracy of the stereo method. These results proved that LBP and its variants are suitable for using in solving the stereo correspondence problem or improving the performance of existing stereo methods. © 2020 IEEE.

关键词： local binary pattern

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local binary pattern based Technique for Content Based Image Copy Detection

Local Binary Pattern based Technique for Content Based Image...

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2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2020

作者： Srivastava, Mayank Siddiqui, Jamshed Ali, Mohammad Athar GLA University Department of CEA Mathura UP India Aligarh Muslim University Dept. of Computer Science Aligarh UP India University of Buckingham Dept. of Applied Computing Buckingham United Kingdom

ISBN: (数字)9781728165752

ISBN: (纸本)9781728165752

Now a day's there is a significant increase in the in the duplicate copies of large original images. One of the main reason for such duplication is due to the availability of large number of image editing software. In this paper, we have proposed a significant image hashing technique based on local binary pattern (LBP) for image identification. Here, the input image is initially pre-processed to remove any kind of minor effects. local binary pattern (LBP) is then applied to the processed image to produce features which is used for identification. The calculated experimental values show that proposed hashing is giving extraordinary performance against 'Histogram equalization' attack. Receiver operating characteristics (ROC) curve shows that proposed hashing gives better results as compared to other referenced techniques. Keeping in view the results obtained for the proposed technique, one can say that we can use such a copy detector for online detection of image copies. © 2020 IEEE.

关键词： local binary pattern

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A new local pooling approach for convolutional neural network: local binary pattern

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第12期83卷 34137-34151页

作者： Ozdemir, Cuneyt Dogan, Yahya Kaya, Yilmaz Siirt Univ Comp Engn Dept TR-56100 Siirt Turkiye Batman Univ Comp Engn Dept TR-72000 Batman Turkiye

The pooling layer used in CNN models aims to reduce the resolution of image/feature maps while retaining their distinctive information, reducing computation time and enabling deeper models. Max and average pooling methods are frequently used in CNN models due to their computational efficiency;however, these methods discard the position information of the pixels. In this study, we proposed an LBP-based pooling method that generates a neighborhood-based output for any pixel, reflecting the correlation between pixels in the local area. Our proposed method reduces information loss since it considers the neighborhood and size of the pixels in the pooling region. Experimental studies were performed on four public datasets to assess the effectiveness of the LBP pooling method. In experimental studies, a toy CNN model and various transfer learning models were utilized in conducting test operations. The proposed method provided improvements of 1.56% for Fashion MNIST, 0.22% for MNIST, 3.95% for CIFAR10, and 5% for CIFAR100 dataset using the toy model. In the experimental studies conducted using the transfer learning model, performance improvements of 6.99(-/+)(0.74) and 8.3(-/+)(0.1) were achieved for CIFAR10 and CIFAR100, respectively. We observed that the proposed method outperforms the commonly used pooling layers in CNN models. Code for this paper can be publicly accessed at: https://***/cuneytozdemir/lbppooling

关键词： Pooling methods Convolutional neural network local binary pattern

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Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

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APPLIED SOFT COMPUTING 2015年 32卷 101-112页

作者： Yadav, Arvind R. Anand, R. S. Dewal, M. L. Gupta, Sangeeta Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India Forest Res Inst Dehradun Bot Div Dehra Dun Uttarakhand India

In this paper, multiresolution local binary pattern (MRLBP) variants based texture feature extraction techniques have been proposed to categorize hardwood species into its various classes. Initially, discrete wavelet transform (DWT) has been used to decompose each image up to 7 levels using Daubechies wavelet (db2) as decomposition filter. Subsequently, six texture feature extraction techniques (local binary pattern and its variants) are employed to obtain substantial features of these images at different levels. Three classifiers, namely, linear discriminant analysis (LDA), linear and radial basis function (RBF) kernel support vector machine (SVM), have been used to classify the images of hardwood species. Thereafter, classification results obtained from conventional and MRLBP variants based texture feature extraction techniques with different classifiers have been compared. For 10-fold cross validation approach, texture features acquired using discrete wavelet transform based uniform completed local binary pattern( DWTCLBPu2) feature extraction technique has produced best classification accuracy of 97.40 +/- 1.06% with linear SVM classifier. This classification accuracy has been achieved at the 3rd level of image decomposition using full feature (1416) dataset. Further, reduction in dimension of texture features (325 features) by principal component analysis (PCA) has been done and the best classification accuracy of 97.87 +/- 0.82% for DWTCLBPu2 at the 3rd level of image decomposition has been obtained using LDA classifier. The DWTCLBPu2 texture features have also established superiority among the MRLBP techniques with reduced dimension features for randomly divided database into fix training and testing ratios. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Discrete wavelet transform local binary pattern Completed local binary pattern Linear SVM Linear discriminant analysis RBF kernel SVM

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