检索结果-内蒙古大学图书馆

Blind Medical Image Watermarking Based on LBP-DWT for Telemedicine Applications

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年 1-26页

作者： Hebbache, Khaled Aiadi, Oussama Khaldi, Belal Benziane, Ali Kasdi Merbah Univ Comp Sci Dept Ghardaia RdBP511 Ouargla 30000 Algeria Lab Intelligence Artificielle & Technol Informat L Ouargla 30000 Algeria Univ Ziane Achour Fac Sci & Technol Djelfa Algeria

With the increasing use of telemedicine in healthcare, the security and integrity of medical images during transmission have become critical. This paper presents a novel blind watermarking scheme local binary Pattern-Discrete Wavelet Transform (LBP-DWT) for medical images based on local binary patterns and the *** Transform in frequency domain. We take advantage of the LBP, which is computationally fast, to improve the watermark's resistance to the different kinds of attacks, and maintain the overall visual quality of the watermarked images. In addition, the DWT could offer a high trade-off between robustness and imperceptibility due to the multi-resolution analysis it provides. During embedding, the LL band (approximation coefficients) of the DWT is selected and divided into 3 x 3 blocks. The resulting LBP codes are then XORed with the embedding bits and hidden in the corresponding blocks using the Least Significant Bit technique. Note that the Arnold transform is used during the embedding step to scramble the watermark, which is then vectorized based on the ZigZag fashion to improve the security of the proposed scheme. To evaluate the performance of the proposed method, extensive experiments are conducted on a dataset of medical images. The watermarked images are tested against various attacks, including compression, noise addition, and cropping. The obtained results demonstrate the effectiveness of the proposed techniques.

关键词： Watermarking Blind Telemedicine Discrete wavelet transform local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Classification of wool and cashmere fiber based on LBP and DWT features: performance comparison of random forest, AdaBoost, and KNN classifiers

引用

JOURNAL OF THE TEXTILE INSTITUTE 2025年

作者： Fang, Xinyue Xin, Binjie Zhan, Zhu Newton, Md All Amin Yan, Feng Jin, Zhenhui Shanghai Univ Engn Sci Sch Text & Fash Art Bldg333 Longteng Rd Shanghai 201620 Peoples R China

The automatic identification of cashmere and wool fibers presents a significant challenge due to their substantial structural and textural similarities. An image-based method is proposed in this article, which combines local binary pattern (LBP), discrete wavelet transform (DWT), and several classification algorithms - including random forest (RF), adaptive boosting (AdaBoost), and K-nearest neighbors (KNN) - to achieve rapid and accurate differentiation between wool and cashmere fibers. Primarily, 300 images each of cashmere and wool fibers were captured using optical microscopy, and the original images were pre-processed to prepare input data for feature extraction. Texture features were then extracted using LBP, and energy entropy was extracted using DWT. Fiber diameters were measured through geometric morphological analysis, using the maximum inscribed circle method to determine fiber diameter from segmented images. In conclusion, these features were input into RF, AdaBoost, and KNN classifiers for model training and classification. Experimental results indicate that the RF classifier achieved superior performance, with a classification accuracy of 94.88%, outperforming both AdaBoost and KNN, and demonstrating high recall, and F1 scores. Overall, the proposed method exhibits exceptional accuracy, robustness, and generalization capability in fiber classification tasks, with significant potential for application to the classification of other natural fibers.

关键词： Fiber identification local binary patterns discrete wavelet transform random forests

来源：评论

学校读者我要写书评

暂无评论

Fractional-order gradient based local binary pattern for texture classification

引用

COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Alpaslan, Nuh Hanbay, Kazim Bingol Univ Sch Engn & Architecture Dept Comp Sci Bingol Turkiye Inonu Univ Sch Engn Dept Software Engn Malatya Turkiye

The local binary patterns method plays an efficient role in texture classification and feature extraction. These approaches extract textural features by using the neighboring pixel values. The single or joint histogram of the texture image is constructed from the LBP features obtained from local relationships. In this study, a method of utilizing fractional derivative information effectively has been proposed for classifying color texture images. The magnitude of the fractional horizontal and vertical derivatives obtained with Gaussian derivative filters are integrated into the ACS-LBP method. The magnitude information of the fractional derivatives of local texture patterns has been modeled according to the relationship between neighboring pixels. The computed derivative information has been incorporated into the ACS-LBP model to effectively encode the local pixel relationship. In order to maintain, these fractional-order edge and texture transition detection operators provide both high robustness and continue to detect small textural details. To accomplish these capabilities, the fractional-order parameter is tuned to target particular pixel transition frequencies. This gives the proposed LBP method greater latitude in selecting the fractional-order mask. An additional degree of freedom in designing various masks is provided by the fractional-order parameter. The developed model has been evaluated on widely used texture databases. It also has been compared with existing LBP and deep learning models in terms of different performance metrics. The proposed method has shown significant advantages over up to date methods in both classification accuracy and execution time.

关键词： local binary patterns Fractional derivative Texture classification

来源：评论

学校读者我要写书评

暂无评论

SISIM: statistical information similarity-based point cloud quality assessment

引用

VISUAL COMPUTER 2025年第1期41卷 625-638页

作者： Xiao, Shuyu Wang, Yongfang Wang, Yihan Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Commun & Informat Engn Shanghai 200444 Peoples R China

Due to the widespread use of point cloud, the demand for compression and transmission is more and more prominent. However, this cause various losses to the point cloud. It is necessary for application to evaluate the quality of point cloud. Therefore, we propose a new point cloud quality assessment (PCQA) metric named statistical information similarity (SISIM). First, we preprocess point cloud (PC) by scaling based on density and then project PC into texture maps and geometry maps. In addition, the SISIM based on Natural Scene Statistics (NSS) is proposed as texture features under the premise of proving that the texture maps meet NSS. Furthermore, we propose to extract geometry features based on local binary patterns (LBP) on account of the phenomenon that LBP maps of geometry images vary with different distortions. Finally, we predict the quality of PCs by fusing texture features with geometry features. Experiments show that our proposed method outperforms the state-of-the-art PCQA metrics on three publicly available datasets.

关键词： local binary patterns Point cloud quality assessment Statistical similarity Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Tea leaf disease recognition using attention convolutional neural network and handcrafted features

引用

CROP PROTECTION 2025年 190卷

作者： Wu, Peng Liu, Jinlan Jiang, Mingfu Zhang, Li Ding, Shining Zhang, Kewang Xinyang Agr & Forestry Univ Sch Informat Engn Xinyang 464000 Peoples R China Macao Polytech Univ Fac Appl Sci Macau 999078 Peoples R China

The diseases of tea leaves have a significant impact on their quality and yield, making the rapid identification of leaf diseases in tea crucial for prevention and control. We propose an LBPAttNet model, incorporating a lightweight coordinate attention mechanism into ResNet18 to enhance disease localization and reduce background interference. Furthermore, we employ the local binary patterns (LBP) algorithm to further extract local structural and textural features of tea leaf diseases, and integrate deep features to obtain a more comprehensive feature representation. Additionally, we utilize the focal loss function to alleviate the issues of class imbalance and varying difficulty levels in tea leaf disease, thereby further enhancing the accuracy of tea disease recognition. Our model achieves an accuracy of 92.78% and 98.13% on two publicly available tea disease datasets, surpassing ResNet18 by 3.84% and 2.59% respectively. Compared to traditional algorithms such as AlexNet, GoogleNet, MobileNet, VGG16, and other tea disease recognition algorithms, our model also shows significant improvements. These results highlight the superior performance and robustness of our model.

关键词： Tea leaf disease Deep learning Attention local binary patterns Handcrafted features

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Human Detection Using local Shape and Non-Redundant binary patterns

Human Detection Using Local Shape and Non-Redundant Binary P...

引用

International Conference on Control, Automation, Robotics and Vision

作者： Duc Thanh Nguyen Wanqing Li Philip Ogunbona Advanced Multimedia Research Lab ICT Research Institute School of Computer Science and Software Engineering University of Wollongong Australia

ISBN: (纸本)9781424478149

Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance features are extracted. We introduce a novel local binary pattern (LBP) descriptor, called Non-Redundant LBP (NRLBP), to encode local appearance of human. The proposed method was evaluated and compared with other state-of-the-art human detection methods on two commonly used datasets: MIT and INRIA pedestrian test sets. We also performed extensive experiments on selecting appropriate parameters as well as verifying the improvement of the proposed method through all stages of the framework.

关键词： Human detection local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Semi-local Structure patterns for Robust Face Detection

引用

IEEE SIGNAL PROCESSING LETTERS 2015年第9期22卷 1400-1403页

作者： Jeong, Kyungjoong Choi, Jaesik Jang, Gil-Jin Ulsan Natl Inst Sci & Technol Sch Elect & Comp Engn Ulsan 689798 South Korea Kyungpook Natl Univ Sch Elect Engn Daegu 702701 South Korea

In many image processing and computer vision problems, including face detection, local structure patterns such as local binary patterns (LBP) and modified census transform (MCT) have been adopted in widespread applications due to their robustness against illumination changes. However, being reliant on the local differences between neighboring pixels, they are inevitably sensitive to noise. To overcome the problem of noise-vulnerability of the conventional local structure patterns, we propose semi-local structure patterns (SLSP), a novel feature extraction method based on local region-based differences. The SLSP is robust to illumination variations, distortion, and sparse noise because it encodes the relative sizes of the central region with locally neighboring regions into a binary code. The principle of SLSP leads noise-robust expansions of LBP and MCT feature extraction frameworks. In a statistical analysis, we find that the proposed methods transform a substantial amount of random noise patterns in face images into more meaningful uniform patterns. The empirical results on the MIT + CMU dataset and FDDB (face detection dataset and benchmark) show that the proposed semi-local patterns applied to LBP and MCT feature extraction frameworks outperform the conventional LBP and MCT features in AdaBoost-based face detectors, with much higher detection rates.

关键词： AdaBoost distortion face detection local binary patterns semi-local structure patterns

来源：评论

学校读者我要写书评

暂无评论

Robust local oriented patterns for ear recognition

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第41-42期79卷 31183-31204页

作者： Hassaballah, M. Alshazly, H. A. Ali, Abdelmgeid A. South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt South Valley Univ Fac Sci Dept Math Qena 83523 Egypt Minia Univ Fac Sci Dept Comp Sci Al Minya 61519 Egypt

Extraction and description of image features is an active research topic and important for several applications of computer vision field. This paper presents a new noise-tolerant and rotation-invariant local feature descriptor called robust local oriented patterns (RLOP). The proposed descriptor extracts local image structures utilizing edge directional information to provide rotation-invariant patterns and to be effective in noise and changing illumination situations. This is achieved by a non-linear amalgamation of two specific strategies;binarizing neighborhood pixels of a patch and assigning binomial weights in the same formula. In the encoding methodology, the neighboring pixels is binarized with respect to the mean value of pixels in an image patch of size 3 x 3 instead of the center pixel. Thus, the obtained codes are rotation-invariant and more robust against noise and other non-monotonic grayscale variations. Ear recognition is considered as the representative problem, where the ear involves localized patterns and textures. The proposed descriptor encodes all images' pixels and the resulting RLOP-encoded image is divided into several regions. Histograms of the regions are constructed to estimate the distribution of features. Then, all histograms are concatenated together to form the final descriptor. The robustness and effectiveness of the proposed descriptor are evaluated through conducting several identification and verification experiments on four different ear databases: IIT Delhi-I, IIT Delhi-II, AMI, and AWE. It is observed that the proposed descriptor outperforms the state-of-the-art texture based approaches achieving a recognition rate of 98% on the average providing the best performance among the tested descriptors.

关键词： Biometrics Ear recognition Features extraction local image descriptors local binary patterns

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：