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Reversible data hiding (RDH) technology is an effective means to ensure the secrecy of information transmission. In particular, the interpolation-based RDH algorithm is widely used due to its advantages of large embedding capacity, good visualization, and low computational cost. However, existing interpolation-based RDH algorithms have a common problem that the algorithm performance is greatly affected by the interpolation accuracy and image texture. This paper proposed a novel difference division interpolation (DDI) method that exploits the difference between the original pixels and predicted pixels to construct the cover image (CI) so that the pixel error is within a fixed range and independent of the image texture. Meanwhile, to avoid information aggregation, an adaptive chunking strategy (ACS) is designed to replace the traditional fixed chunking mode. The chunking mode and the number of embeddable bits for every non-reference pixel can be adaptively selected according to the embedding capacity, which enhances the visual quality of images. In addition, an out-of-bounds adjustment (OA) method is introduced to minimize the distortion of the steganographic image (SI) and solve the pixel underflow and overflow. Compared with existing algorithms, experimental results show the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have performed well in image denoising. Although some CNNs enlarge convolutional kernels and increase stacked convolutional layers to overcome the locality defect of convolutional operations, they may increase computational costs. In this paper, we propose a parallel and serial denoising network (PSDNet) for image denoising to preserve image texture. Specifically, the proposed PSDNet contains a parallel block (PB), a serial block (SB), and a reconstruction block (RB). A PB uses two heterogeneous sub-networks with a deformable convolution in a parallel way to extract comparative information for better-recovering image texture. A SB utilizes an enhanced residual dense architecture via combinations of a batch normalization, ReLU, and convolutional layer in a serial way to refine obtained features for obtaining more accurate noise information. A RB is responsible for reconstructing images. Experimental results reveal that our PSDNet is very effective in image denoising, according to quantitative analysis and visual analysis. Codes can be obtained at https://github.com/hellloxiaotian/PSDNet. • Heterogeneous architecture with deformable convolution can better filter noise. • An enhanced residual architecture is used to remove redundant features. • Combining a parallel and serial way can improve effects of images denoising. • Proposed network is effective for synthesized and real noisy image denoising. [ABSTRACT FROM AUTHOR]

To ensure the timely detection of safety hazards in transmission lines and to enhance accurately the detection of insulator defects in complicated environments, this study proposes a 3D attention-focused pure convolutional target detection algorithm (CMYOLOv7) based on you only look once v7 (YOLOv7) for defect detection on insulators in complicated environments. Firstly, to address the incapacity of focusing on the target in the backbone feature extraction networks, this study proposes a focused pure convolutional feature extraction module (ConvSimCB) to enhance the extraction and focus capability on insulator defect features. Secondly, to solve the problem of loss of key detail feature information caused by maximum pooling in spatial pyramid pooling module (SPPCSPC), this study proposes a mixed spatial pyramid pooling module (MIXPCSPC) to retain abundant image texture detail information and increase accuracy in detection of tiny insulator defects. Finally, a lightweight generic upsampling operator (CARAFE) is introduced to enhance the feature map resolution to address image distortion caused by the Nearest Neighbor Method of upsampling. This study proposed CMYOLOv7 achieves 98.37% precision, 90.59% recall, 95.68% mean average precision (MAP), and 94% F1 score, higher than YOLOv7 by 0.61%, 8.64%, 5.41%, and 5%. [ABSTRACT FROM AUTHOR]

Highlights • A comparative study of ear recognition using local binary patterns variants is done. • A new texture operator is proposed and used as an ear feature descriptor. • Detailed analysis on Identification and verification is conducted separately. • An approximated recognition rate of 99% is achieved by some texture descriptors. • The study has significant insights and can benefit researchers in future works. Abstract Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions; namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%; while, their performance faces difficulties when the level of distortions increases. Moreover, it is noted that the tested LBP variants achieve almost close performance on ear recognition. Thus, further studies on other applications are needed to verify this close performance. We believe that the presented study has significant insights and can benefit researchers in choosing between LBP variants as well as acting as a connection between previous studies and future work in utilizing LBP-like features in ear recognition systems. [ABSTRACT FROM AUTHOR]

• Embedding multiple watermarks method based on inter-block correlation is proposed. • The texture complexity method is designed to adaptively select adjacent blocks. • Multiple watermarks are embedding in the same region of the image in parallel. • Two methods of adjacent block selection are proposed for image adaptive selection. • The watermark is embedded alternately in the middle frequency and low frequency. People pay more and more attention to copyright protection and digital watermarking technology is a reliable method for copyright identification. In this paper, we propose the parallel multiple watermarking method using adaptive inter-block correlation. Considering the image texture characteristics, the texture complexity method is designed, based on which, the Circular-shaped Adjacent Block Selection and the Arch-shaped Adjacent Block Selection will be adaptively selected for adjacent block selection. The multiple watermarks are alternately embedded into the low and middle frequency band in DCT domain of the image blocks to good imperceptibility and high robustness. By adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector, the multiple watermarks can be embedded, so the embedding capacity increases. In addition, we improve the multiple watermarks embedding based on orthogonal spreading vectors so that it can be adapted to embedding multiple watermarks in parallel based on inter-block correlation, saving embedding and extraction time. We theoretically analyze the rationality of parallel orthogonal embedding of multiple watermarks and experimentally verify the high efficiency of parallelism. Cover images of different texture complexity are included in the test dataset, and various attacks have been simulated in the experiments. Experimental results on imperceptibility, robustness, time cost and watermark capacity show the satisfied performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

A World Health Organization (WHO) report estimates that in 2015, at least 561 thousand women will die of breast cancer. Although breast cancer is considered a disease of the developed world, nearly 50% of the cases and 58% of the deaths occur in the less developed countries. A mammogram is a way of discovering not just the palpable tumors that cause cancer but also other lesions that are not perceived during the physical examination performed by the expert physician or during self-exams; however, it is known that this exam is targeted for women after the age of 40 because age is one of the factors that can cause great variations in sensitivity during the exam. Besides the patient’s age, the expert’s experience and the quality of the images obtained during the exam are decisive factors in the detection of breast cancer. This work presents two novelties. The first is the use of Local Binary Patterns (LBPs) to generate a representation of a Region of Interest (ROI) image. Over this representation, we generate other representations using techniques such as image histograms, gray-level co-occurrence matrices (GLCMs) and gray-level run-length matrices (GLRLMs). These representations allow texture analysis through several perspectives. The second novelty uses these representations as input to the application of indexes adapted from ecology (Shannon, McIntosh, Simpson, Gleason and Menhinick) as texture descriptors. Based on this strategy, we analyze mammographic image textures to classify regions of these images as benign or malignant using a Support Vector Machine (SVM). The best result achieved was of 88.31% accuracy, 85% sensitivity, 91.89% specificity, a positive probability ratio of 10.48, a negative probability ratio of 0.16, and an area under the Receiver Operating Characteristic (ROC) curve of 0.88, obtained through the Shannon index. We believe that the proposed method, with some adaptations, may also be used for image texture analysis of several different lesions such as lung nodules, glaucoma and prostates. This belief is based on the achieved results and the method’s simplicity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive and behavioral impairment that significantly interferes with social and occupational functioning. Mild cognitive impairment (MCI) is a relatively broad clinical condition involving a slight memory deficit, which in many cases represents a transitional state between a cognitively normal (CN) condition and AD. Structural magnetic resonance (sMR) imaging has been widely used in studies related to AD because it provides images with excellent anatomical details and information about structural and contrast changes induced by the disease in the brain. Many published studies restrict their analysis to a few particular regions of the brain and search for structural changes caused by the disease. Recent studies start looking for new AD biomarkers using multiple brain regions and focusing on subtle texture changes in the image. Therefore, this study proposes a new technique for MR image classification in AD diagnosis using graph kernels constructed from texture features extracted from sMR images. In our method, we first segment the MR brain images into multiple regions with the FreeSurfer. Then, we extract 22 texture features using three methods and define the graph-node attributes as the probability distributions of the extracted features. Next, for each texture feature, we build a graph and define its edge weights as the distances between pairs of node attributes using three distance metrics. After that, we use a threshold-based approach for graph edges removal and create the graph-kernels matrices. Finally, we perform image classification using Support Vector Machines (SVMs) with two graph-kernels. Results of our method have shown better performances for the CN × AD (AUC = 0.92) and CN × MCI (AUC = 0.81) classifications, and worse for the MCI × AD case (AUC = 0.78). This trend is consistent with other published results and makes sense if we consider the concept of Alzheimer's disease continuum from pathophysiological, biomarker and clinical perspectives. Besides allowing the use of different texture attributes for the diagnosis of Alzheimer's, our method uses the graph-kernel approach to represent texture features from different regions of the brain image, which considerably facilitates the image classification task via SVMs. Our results were promising when compared to the state-of-the-art in graph-based AD classification. • Alzheimer's disease classification based on graph kernel Support Vector Machines. • Evaluation of image texture patterns of multiple brain regions in Alzheimer. • Assessment of three distance metrics to measure node attributes differences. • Threshold based method for graph edge removal to obtain discriminative graphs. • Classification results comparable to other proposed graph-based models. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects that different preprocessing techniques have on the performance of features extracted from Haralick's co-occurrence matrix, one of the best known methods for analyzing image texture. In addition, we compare and combine different strategies for extracting descriptors from the co-occurrence matrix. We propose an ensemble of different preprocessing methods, where, for each descriptor, a given Support Vector Machine (SVM) classifier is trained. The set of classifiers is then combined by weighted sum rule. The best result is obtained by combining the extracted descriptors using the following preprocessing methods: wavelet decomposition, local phase quantization, orientation, and the Weber law descriptor. Texture descriptors are extracted from the entire co-occurrence matrix, as well as from sub-windows, and evaluated at multiple scales. We validate our approach on eleven image datasets representing different image classification problems using the Wilcoxon signed rank test. Results show that our approach improves the performance of standard methods. All source code for the approaches tested in this paper will be available at: https://www.dei.unipd.it/node/2357 [ABSTRACT FROM AUTHOR]

Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90. [Copyright &y& Elsevier]