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A new robust approach for altered handwritten text detection

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期82卷 20925-20949页

作者： Patil, Gayatri Shivakumara, Palaiahnakote Gornale, Shivanand S. Pal, Umapada Blumenstein, Michael Rani Channamma Univ Dept Comp Sci Belagavi India Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata India Univ Technol Sydney Fac Engn & IT Sydney NSW Australia

Altering handwritten documents is receiving special attention from researchers because it is useful in several sensitive crime applications such as the identification of suicide notes, fraudulent certificates, fake answer scripts, bank and property documents etc. This paper aims at developing a robust method for detecting altered text in handwritten document images in noisy and blurry environments. This study considers ten classes of handwritten text affected by multiple forgery operations with noise and blur as follows: (i) Normal-original text, (ii) Copy-paste forgery, (iii) Insertion forgery, (iv) Copy-paste and insertion forgery, (v) Noisy text, (vi) Blurred text, (vii) Copy-paste forgery with noise, (viii) Copy-paste forgery with blur, (ix) Insertion forgery with noise and (x) Insertion with blur. For ten-class classification, the proposed work explores the combination of statistical, gradient and texture features with a Bayesian classifier. The proposed approach works based on the premise that altered content in noisy and blurry handwritten documents exhibits inconsistent patterns of pixel arrangements while the original text exhibits a regular pattern of pixel arrangements. Comprehensive experiments on our dataset of 10-class and three standard datasets, namely, a dataset of forged handwritten text, a dataset of altered receipt images, and a dataset of forged IMEI number images are conducted to show effectiveness and robustness of the proposed approach compared to the state-of-the-art methods.

关键词： Document forgery Altered text detection local binary patterns Histogram of oriented gradients Statistical features Naive Bayesian classifier

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Facial expression recognition based on multi-channel fusion and lightweight neural network

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SOFT COMPUTING 2023年第24期27卷 18549-18563页

作者： Yu, Yali Huo, Hua Liu, Junqiang Henan Univ Sci & Technol Engn Technol Res Ctr Big Data & Computat Intellig Kaiyuan Ave Luoyang 471003 Henan Peoples R China Guangdong Univ Sci & Technol Sch Comp Dongyuan Ave Dongguan 523083 Guangdong Peoples R China

In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of facial expression classifications. On the other hand, traditional facial expression recognition methods are not only complicated in the feature extraction process, but also unable to obtain more in-depth high-semantic features and deep features from the original image. To solve the above problems, this paper proposes a facial expression recognition method based on multi-channel fusion and lightweight neural network. First, a cascade classifier based on Haar features is used to detect the face region of the facial expression image. Second, the local binary pattern (LBP) is used to extract the local texture features from the face region. Third, face edge features are simultaneously obtained by performing edge detection in the face region based on the Canny edge detection algorithm. Fourth, the obtained face image, LBP texture feature image, and edge detection Canny image are fused, and the fused image is input into the constructed lightweight neural network for training and recognition. Experiments are carried out on the public image databases Facial Expression Recognition 2013 (Fer2013) and extended Cohn-Kanade (CK +) using the hold-out cross-validation method. The experimental results show that the proposed method effectively extracts more complete image features by combining traditional feature extraction algorithms with deep learning feature extraction algorithms, improves the accuracy and robustness of facial expression recognition, and has better recognition rate and generalization ability compared to other mainstream methods.

关键词： Face detection local binary patterns Canny edge detector Facial expression recognition Lightweight neural network

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CN-LBP: COMPLEX NETWORKS-BASED local binary PAT TERNS FOR TEXTURE CLASSIFICATION 18

CN-LBP: COMPLEX NETWORKS-BASED LOCAL BINARY PAT TERNS FOR TE...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Huang, Zheng-Rui Fuzhou Univ Acad Digital China Fujian Fuzhou 350108 Peoples R China Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Peoples R China

ISBN: (纸本)9781665466110

To overcome the limitations of original local binary patterns (LBPs), this article proposes a new texture descriptor aided by complex networks (CNs) and LBPs, named CN-LBP. Specifically, we first abstract a texture image (TI) as directed graphs over different bands with the help of pixel distance, intensity, and gradient (magnitude and angle). Second, several CN feature measurements, including clustering coefficient, in-degree centrality, out-degree centrality, and eigenvector centrality), are selected to decipher the texture features, which generates four feature images retaining the image information as much as possible. Third, given the original TIs, gradient images (GIs), and generated feature images, we can obtain the discriminative representation of TIs based on a uniform LBP (ULBP). Finally, the feature vector is obtained by jointly calculating and concatenating spatial histograms. In contrast to the original LBP, the proposed texture descriptor contains more detailed image information and shows resistance to imaging and noise. Experiment results on four datasets show that the proposed texture descriptor can significantly improve the classification accuracies compared to the state-of-the-art LBP-based variants and deep learning-based methods.

关键词： local binary patterns Complex networks Feature measurement Texture classification

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Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 122卷 103842-103842页

作者： Rampun, Andrik Morrow, Philip J. Scotney, Bryan W. Wang, Hui Univ Sheffield Acad Unit Radiol Dept Infect Immun & Cardiovasc Dis Sheffield S10 2RX S Yorkshire England Ulster Univ Sch Comp Jordanstown BT37 0QB North Ireland

We investigate various channel encoding techniques applied to breast density classification in mammograms;specifically, local binary, ternary, and quinary encoding approaches are considered. Subsequently, we propose a new encoding approach based on a seven-encoding technique, yielding a new local pattern operator called a local septenary pattern operator. Experimental results suggest that the proposed local pattern operator is robust and outperforms the other encoding techniques when evaluated on the Mammographic Image Analysis Society (MIAS) and InBreast datasets. The local septenary pattern operator achieved a maximum classification accuracy of 83.3% and 80.5% on the MIAS and InBreast datasets, respectively. The closest comparison achieved by the other local pattern operators is the local quinary operator, with maximum accuracies of 82.1% (MIAS) and 80.1% (InBreast), respectively.

关键词： Breast mammography Breast density local binary patterns local ternary patterns local quinary patterns local septenary patterns

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A hybrid approach for automatic segmentation and classification to detect tuberculosis

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DIGITAL HEALTH 2024年 10卷

作者： Khan, Muzammil Zaman, Abnash Khan, Sarwar Shah Arshad, Muhammad Univ Swat Dept Comp & Software Technol Khyber Pakhtunkhwa 32600 Pakistan City Univ Sci & IT Dept Comp Sci Khyber Pakhtunkhwa Pakistan

Objective Tuberculosis (TB) remains a significant global infectious disease, posing a considerable health threat, particularly in resource-constrained regions. Due to diverse datasets, radiologists face challenges in accurately diagnosing TB using X-ray images. This study aims to propose an innovative approach leveraging image processing techniques to enhance TB diagnostic accuracy within the automatic segmentation and classification (AuSC) framework for *** The AuSC of detection of TB (AuSC-DTB) framework comprises several steps: image preprocessing involving resizing and median filtering, segmentation using the random walker algorithm, and feature extraction utilizing local binary pattern and histogram of gradient descriptors. The extracted features are then classified using the support vector machine classifier to distinguish between healthy and infected chest X-ray images. The effectiveness of the proposed technique was evaluated using four distinct datasets, such as Japanese Society of Radiological Technology (JSRT), Montgomery, National Library of Medicine (NLM), and *** Experimental results demonstrate promising outcomes, with accuracy rates of 94%, 95%, 95%, and 93% achieved for JSRT, Montgomery, NLM, and Shenzhen datasets, respectively. Comparative analysis against recent studies indicates superior performance of the proposed hybrid *** The presented hybrid approach within the AuSC framework showcases improved diagnostic accuracy for TB detection from diverse X-ray image datasets. Furthermore, this methodology holds promise for generalizing other diseases diagnosed through X-ray imaging. It can be adapted with computed tomography scans and magnetic resonance imaging images, extending its applicability in healthcare diagnostics.

关键词： AuSC framework tuberculosis detection segmentation classification support vector machine local binary patterns histogram of orientedgradients

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Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor

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JOURNAL OF SUPERCOMPUTING 2023年第8期79卷 9288-9319页

作者： Reddy, K. Rasool Dhuli, Ravindra VIT AP Univ Sch Elect Engn SENSE Amaravati 522237 Andhra Pradesh India

Efficient detection and classification of brain tumors using magnetic resonance images provide significant support to the neurologists. However, many approaches developed for this purpose exhibit limited accuracy due to irregular boundary pixels and intensity non-uniformity in MR images. Therefore, to minimize these issues and attain better performance, a new methodology is proposed based on the fuzzy thresholding and local texture feature descriptor. The proposed model includes four fundamental steps: noise reduction, tumor extraction, feature extraction, and classification. Anisotropic diffusion filtering is implemented to reduce the noise without losing information that is essential in the interpretation of the brain tumor images. Then, spatial fuzzy C-means thresholding and morphological operations-based image segmentation are applied to extract the tumor area of the brain. In the very next step, obtains texture features using a complete local binary pattern - based feature descriptor. These features capture inherent information from brain MR images. In the later stage, these features are concatenated using a serial-based fusion approach before classification using supervised learning approaches (decision tree, naive bayes, random forest, and LogitBoost). The above investigations are evaluated with simulations on harvard medical school and Kaggle repository data sets. The experimental outcomes support the significance of the proposed methodology which exhibited better performance compared to the state-of-the-art methods.

关键词： Anisotropic diffusion filtering Completed local binary patterns local binary patterns Magnetic resonance imaging Morphological operations Spatial fuzzy c-means threshold

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Dual neighborhood thresholding patterns based on directional sampling Effective face representation on mixed face recognition dataset

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KNOWLEDGE AND INFORMATION SYSTEMS 2023年第1期65卷 435-462页

作者： Kas, M. El-merabet, Y. Ruichek, Y. Messoussi, R. Univ Ibn Tofail Fac Sci Dept Phys Lab LASTID BP 133 Kenitra Morocco Univ Bourgogne Franche Comte CIAD UMR 7533 UTBM F-90010 Belfort France

The evaluation of face recognition algorithms relies on the diversity of the challenges simulated on the adopted benchmarks. The main face recognition challenges cover the illumination changes, inhomogeneous background, different facial expressions, pose variations, occlusion, aging and resolution. Many 2D databases that include one challenge or more have been proposed in the state of the art. These databases represent different amount of individuals and samples, and generally the number of persons does not exceed 100 classes. The reason behind this limitation relies on the resources and materials required to construct a database composed of thousands of images and hundreds of persons along with the mentioned face recognition basic challenges. As a solution, researchers proposed to build benchmarks based on collecting the web images of celebrities from search engines such as Google Images and Flicker. The well-known database of this kind is Labeled Faces in the Wild (LFW) as a public benchmark for face verification. This solution managed to constitute a dependent way to construct benchmarks, but it could not be applicable for face recognition since the collected images have a low resolution and the majority of the persons are represented over few samples (one or two in most cases), which made these databases extremely hard for handcrafted-based face recognition systems. In this paper, we propose to construct a challenging database referred to as mixed face recognition database (MFRD) based on gathering the images of eight well-known benchmarks of the literature (FERET, Extended Yale B, ORL, AR, FEI, KDEF, IIVIM and JAFFE). The constructed database is expected to be more cornplex in terms of the amount of classes/images and the diversity of challenges. We expect then that the recognition performance on this database will drop compared to the one recorded on each considered benchmark individually. This paper presents also a new LBP variant, namely dual neighborhood th

关键词： Face recognition challenges Feature extraction Face descriptor local binary patterns Nearest neighbor classification

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Robust watermarking algorithm for screen-shooting images based on pattern complexity JND model

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2025年第2期18卷 139-158页

作者： Peng, Jia Qin, Jiaohua Xiang, Xuyu Tan, Yun Qing, Dashan Cent South Univ Forestry & Technol Coll Comp Sci & Informat Technol Changsha 410004 Peoples R China

The popularity of smart devices has made it more convenient for users to take screen shots, but it has also made it easier to take clandestine shots, resulting in compromised and untraceable information. Therefore, this paper introduces a screen-shooting robust watermarking algorithm based on the pattern complexity just noticeable difference (PC-JND) model. This approach involves the utilisation of local binary patterns (LBP) for block filtering based on texture complexity in the original image. Stable feature blocks are selected and processed using the speeded-up robust features (SURF) algorithm to extract key feature points, defining them as the watermark embedding regions. Finally, the watermark is embedded in the integer wavelet domain's HH sub-band, guided by the PC-JND model. Experimental results demonstrate that this algorithm not only significantly improves the visual quality of images in a shorter embedding time but also exhibits enhanced robustness against screen captures from various angles.

关键词： screen-shooting robust watermarking LBP local binary patterns SURF speeded-up robust features JND just noticeable difference

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Research on Face Recognition Algorithms in Social Software Based on Deep Learning 7

Research on Face Recognition Algorithms in Social Software B...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Heng, Gu Soochow Univ Appl Technol Coll Suzhou 215000 Jiangsu Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the rapid development of mobile internet technology and the widespread adoption of mobile smart devices, mobile social software has garnered significant attention. Traditional face recognition technologies struggle to achieve satisfactory recognition results under large data volumes and unrestricted conditions. The integration of deep learning technology has improved face recognition performance in such scenarios. This paper addresses the challenges posed by factors such as lighting, angles, and expressions on face recognition applications by proposing an improved Deep Belief Network (DBN) face recognition algorithm. The local binary patterns (LBP) features of images are used as input for training the neural network, enabling the DBN model to exhibit robustness against variations in lighting, angles, and grayscale, while retaining the characteristic of bottom-up self-learning of data features inherent in DBN. Additionally, to tackle the issue of overfitting caused by insufficient datasets during the DBN training process, this paper introduces a Dropout technique to enhance the DBN model, resulting in the DDBN (Dropout-DBN) algorithm. This effectively prevents overfitting, improves the generalization ability of the DBN model, and experimental results from the ORL and Yale databases demonstrate that the DDBN face recognition algorithm achieves high recognition rates under unrestricted conditions, with lower computational demands, making it easier to obtain optimal solutions. Furthermore, even with insufficient data, it maintains good recognition performance.

关键词： Deep Belief Network local binary patterns Dropout Face Recognition Deep Learning

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Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

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SN Computer Science 2024年第7期5卷 950页

作者： Prakash, Ankur Singh, Vibhav Prakash Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Prayagraj India

The growing volume of medical imaging data presents significant challenges in terms of storage, management, and utilization. Content-based medical image retrieval (CBMIR) systems serve as a supportive tool for radiologists and doctors, enabling healthcare professionals to efficiently compare current cases with previous ones, thereby facilitating accurate and timely diagnoses and treatments. This paper explores various clinically correlated features using the k-nearest neighbors algorithm to retrieve Computed Tomography images of emphysema and with no emphysema based on query content. We examine and compare several combinations of feature extraction techniques, including local binary patterns (LBP), local ternary patterns (LTP), and Zernike moments (ZMs), to evaluate retrieval performance. Our results demonstrate that the integration of LBP, LTP, and ZMs features performs well. However, the retrieval performance of ZMs alone is also quite encouraging compared to the other feature sets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CBMIR Emphysema local binary patterns local ternary patterns Zernike moments

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