Face Recognition Using Completed Local Ternary Pattern (CLTP) Texture Descriptor

Nowadays, face recognition becomes one of the important topics in the computer vision and image processing area. This is due to its importance where can be used in many applications. The main key in the face recognition is how to extract distinguishable features from the image to perform high recognition accuracy.  Local binary pattern (LBP) and many of its variants used as texture features in many of face recognition systems. Although LBP performed well in many fields, it is sensitive to noise, and different patterns of LBP may classify into the same class that reduces its discriminating property. Completed Local Ternary Pattern (CLTP) is one of the new proposed texture features to overcome the drawbacks of the LBP. The CLTP outperformed LBP and some of its variants in many fields such as texture, scene, and event image classification.  In this study, we study and investigate the performance of CLTP operator for face recognition task. The Japanese Female Facial Expression (JAFFE), and FEI face databases are used in the experiments. In the experimental results, CLTP outperformed some previous texture descriptors and achieves higher classification rate for face recognition task which has reached up 99.38% and 85.22% in JAFFE and FEI, respectively

Multi-Scale Colour Completed Local Binary Patterns for Scene and Event Sport Image Categorisation

The Local Binary Pattern (LBP) texture descriptor and some of its variant descriptors have been successfully used for texture classification and for a few other tasks such as face recognition, facial expression, and texture segmentation. However, these descriptors have been barely used for image categorisation because their calculations are based on the gray image and they are only invariant to monotonic light variations on the gray level. These descriptors ignore colour information despite their key role in distinguishing the objects and the natural scenes. In this paper, we enhance the Completed Local Binary Pattern (CLBP), an LBP variant with an impressive performance on texture classification. We propose five multiscale colour CLBP (CCLBP) descriptors by incorporating five different colour information into the original CLBP. By using the Oliva and Torralba (OT8) and Event sport datasets, our results attest to the superiority of the proposed CCLBP descriptors over the original CLBP in terms of image categorisation

Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Security analyses of false positive problem for the SVD-based hybrid digital image watermarking techniques in the wavelet transform domain

Singular Value Decomposition (SVD) comprises many important mathematical properties that are useful in numerous applications. Newly developed SVD-based watermarking schemes can effectively maintain minor changes despite the large altered singular values S caused by the attacks. Due to the stability and the properties of S, most of the researchers prefer to embed into S. However, despite satisfying the stability and robustness criteria, SVD-based image watermarking can still encounter false positive problems (FPP). Avoiding FPPs is one of the popular research topics in the field of SVD-based image watermarking. Satisfying robustness and imperceptibility requirements, as well as preventing FPPs, in SVD-based image watermarking is crucial in applications such as copyright protection and authentication. In this paper, false positive problem is studied, analysed and presented in detail. Different schemes are studied and classified based on the probability of exposure to false positive problem. All types of SVD-based embedding algorithms that leads to false positive problem and the related potential attacks has been evaluated using the reliability test as well as all solutions to false positive problem are reviewed. To understand how the attacks can threaten the rightful ownership and how to avoid these attacks, the three potential attacks of false positive problem has been demonstrated using recent proposed watermarking schemes. The main perspective of this paper is to gather all the issues belong to the false positive problem with SVD-based schemes

Performance Evaluation of SVD-based Digital Image Watermarking Scheme on Print-Scan and Print-Cam (PSPC) Applications

With easy and quick data distribution over the Internet, copyright protection and authentication become important applications of digital watermarking. The image watermarking schemes that are useful to serve these applications should perform well in some of challenging applications such as print-scan and print-cam (PSPC) applications. This challenge provides an impetus for research in the digital watermarking field. In this paper, the performance and the efficiency of several proposed hybrid SVD-based digital image watermarking schemes are evaluated and studied for PSPC as well as for copyright protection and authentication

Alzheimer's diseases detection by using deep learning algorithms: a mini-review

The accurate diagnosis of Alzheimer’s disease (AD) plays an important role in patient treatment, especially at the disease’s early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages

Performance Evaluation Of RDWT-SVD and DWT-SVD Watermarking Schemes

Digital image watermarking protects content by embedding a signal (i.e., owner information) into the host image without noticeable degradation in visual quality. To develop any image watermarking scheme, there some important requirements should be achieved such as imperceptibly, robustness, capacity, security, and, etc. Generally, the watermarking scheme based on wavelet transform domain shows an advantage in human perception and good imperceptibility and robustness. Due to this fact, this paper presents two blind image watermarking schemes based on DWT-SVD and RDWT-SVD. To evaluate their performance, these schemes are exposed to different geometric and non-geometric attacks. Although, DWT-SVD and RDWT-SVD showed robust against all attacks, RDWT-SVD is better than DWT-SVD, especially for geometrical attacks

Block-Based Discrete Wavelet Transform-Singular Value Decomposition Image Watermarking Scheme Using Human Visual System Characteristics

Digital watermarking has been suggested as a way to achieve digital protection. The aim of digital watermarking is to insert the secret data into the image without significantly affecting the visual quality. This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and human visual system in the discrete wavelet transform (DWT) domain. The proposed method is considered to be a block-based scheme that utilises the entropy and edge entropy as HVS characteristics for the selection of significant blocks to embed the watermark, which is a binary watermark logo. The blocks of the lowest entropy values and edge entropy values are selected as the best regions to insert the watermark. After the first level of DWT decomposition, the SVD is performed on the low-low sub-band to modify several elements in its U matrix according to predefined conditions. The experimental results of the proposed scheme showed high imperceptibility and high robustness against all image processing attacks and several geometrical attacks using examples of standard and real images. Furthermore, the proposed scheme outperformed several previous schemes in terms of imperceptibility and robustness. The security issue is improved by encrypting a portion of the important information using Advanced Standard Encryption a key size of 192-bits (AES-192)

Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)

In this paper, a new texture descriptor inspired from completed local binary pattern (CLBP) is proposed and investigated for texture image classification task. A waveletCLBP (WCLBP) is proposed by integrating the CLBP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLBP are extracted from the LL sub-bands coefficients of the image. The RDWT is selected due to its advantages. Unlike the other wavelet transform, the RDWT decompose the images into the same size sub-bands. So, the important textures in the image will be at the same spatial location in each sub-band. As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLBP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and OuTex texture databases show that the proposed WCLBP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy