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The extraction of texture features from images faces two new challenges: large-scale databases with diversified textures, and varying imaging conditions. We propose a novel method termed multiscale blob features (MBF) to overcome these two difficulties. MBF analyzes textures in both resolution scale and gray level. Proposed statistical descriptors effectively extract structural information from the decomposed binary images. Experimental results show that MBF outperforms other methods on combined large-scale databases (VisTex+Brodatz+CUReT+OuTex). Moreover, experimental results on the University of Illinois at Urbana-Champaign database and the entire Brodatzs atlas show that MBF is invariant to gray-level scaling and image rotation, and is robust across a substantial range of spatial scaling. [ABSTRACT FROM AUTHOR]

To balance noise reduction and texture details of low-dose computed tomography (LDCT) images during deep learning training, we propose a network combining edge features and a pyramid structure for LDCT denoising (PLEDNet). PLEDNet contains three states: superficial feature acquisition, deep feature acquisition, and feature confluence. First, the superficial feature acquisition stage uses a simple convolutional block to perform feature extraction on the input LDCT image. Second, in the deep feature extraction phase, one branch uses pyramid pooling to extract multiscale features, and the other uses the trainable Laplacian of the Gaussian module to extract edge detail features. Finally, the feature fusion phase uses a fusion attention module to adaptively fuse multiscale and edge features. The results of the experiments demonstrate that the method not only enhances various relative objective indicators but also effectively represses the noise in LDCT images while fully conserving the image texture details, which has great advantages in terms of visual effects. [ABSTRACT FROM AUTHOR]

Texture classification has been one of the problem-solving strategies. The feature selection technique is critical for increasing the efficiency of machine learning because it significantly improves the performance of texture classification by removing irrelevant features from the original collection. The majority of methods for selecting features are statistical in nature. Accuracy in database classification is primarily achieved by selecting attributes and simultaneously increasing the classification rate. The task's primary objective is to select the most significant features from the feature database for execution. We use the cluster-based feature selection method to derive features for statistical and structural approaches. The proposed strategy should be implemented in three stages. To begin, the function can be extracted using the local binary pattern (LBP), the gray level co-occurrence matrix, or the Gabor filter. Following that, a modified K-means clustering method is used to select features using four standard distance measurements: Euclidean, Minkowski, Chebychev, and City Block. Finally, benchmark classifiers such as naive bayes (NB), support vector machine (SVM), and K-nearest neighbor (KNN) were used to effectively compute the classification accuracy of the research. The proposed approach achieves accuracy of 99.4328 for KNN, 98.6654 for NB, and 99.6712 for SVM with the highest classification rate. [ABSTRACT FROM AUTHOR]

The primary objective of precipitation forecasting is to accurately predict short-term precipitation at a high resolution within a specific area, which is a significant and intricate challenge. Traditional models often struggle to capture the multidimensional characteristics of precipitation clouds in both time and space, leading to imprecise predictions due to their expansion, dissipation, and deformation. Recognizing this limitation, we introduce ConvLSTM-UNet, which leverages spatiotemporal feature extraction from meteorological images. ConvLSTM-UNet is an efficient convolutional neural network (CNN) based on the classical UNet architecture, equipped with ConvLSTM and improved deep separable convolutions. We evaluate our approach on the generic time series dataset Moving MNIST and the regional precipitation dataset of the Netherlands. The experimental results show that the proposed method has better spatiotemporal prediction skills than other tested models, and the mean squared error is reduced by more than 7.2%. In addition, the visualization results of the precipitation forecast show that the approach has a better ability to capture heavy precipitation, and the texture details of the precipitation forecast are closer to the ground truth. [ABSTRACT FROM AUTHOR]

Existing style transfer methods need more texture structure of style images with aesthetic guidance, resulting in the loss of a large amount of texture details, which affects the visual effect. We propose a style transfer model based on multi-adaptive generative adversarial networks (MA-GANs) to address this. Specifically, the aesthetic ability learned by the discriminator is used for feature extraction, and the obtained more generalized features are passed into the multi-attention aesthetic module, which includes a collaborative self-attention (CSA) module and a self-attention normalization (SAN) module. The CSA calculates the correlation between the entangled style features and aesthetic features, capturing texture details and style geometric structures. The SAN balances the content semantic structure with the style geometric structure, harmoniously integrating the style pattern into the content image, thus achieving a more practical style transfer. Extensive qualitative and quantitative experimental studies demonstrate the superiority of MA-GAN in visual quality, enabling the synthesis of art images with smooth brushstrokes and rich colors. [ABSTRACT FROM AUTHOR]

Defining fiber orientation at each pixel within a medical image has traditionally been computationally intensive and prone to systematic errors. A weighted orientation vector summation algorithm capable of detecting fiber orientation simultaneously at each pixel within an image is presented. As a result, pixel-specific fiber orientation information with 2 deg to 3 deg accuracy can be determined within seconds, enabling the practical use of pixel-wise orientation data for characterizing structural anisotropy. This analysis technique has applicability and potential diagnostic utility for a variety of modalities, including second harmonic generation, scanning electron microscopy and immunohistochemical imaging is demonstrated. [ABSTRACT FROM AUTHOR]

When conducting image classification, traditional and deep-learning based methods require extracting target features in different ways and then classifying targets based on the features. Therefore, extracting more informative and effective features is very important, and this problem also exists in plant leaf classification. To address the issue for traditional leaf classification, this article carefully designs two shape features [area ratio-based shape descriptor (ARSD) and corner-based shape descriptor (CSD)] and one leaf vein feature (LVF) extraction method based on statistical characteristics. Then, according to the extraction of ARSD, CSD, and LVF, a general feature extraction framework is formed, which first normalizes direction, segments region of interest, then divides it into subregions, counts the number of pixels in each sub region, and finally forms feature vectors. Finally, to visually verify the effectiveness of these features, they were applied to the classic leaf classification framework, which is adjusted to form a two-stage classification method from coarse classification of shapes and fine classification of species. In the first stage (coarse classification), leaves in three universal datasets [Flavia dataset, Swedish dataset and Intelligent Computing Laboratory (ICL) dataset] and a self-built LZU dataset are respectively divided into several shape categories only by the ARSD. Experiment results show that the ARSD has obvious advantages in shape-based rough classification. Then in the second stage (fine classification), shape, texture, vein, and color features, including ARSD, CSD, local binary patterns, gray-level co-occurrence matrix, the developed LVF, and color moments, are combined to subdivide finer species in each shape category. Extensive experiment results show that the classification accuracy on the Flavia, Swedish, ICL, and LZU datasets have reached 99.3%, 98.9%, 91.5%, and 95.3%, respectively, comparable to deep learning-based methods. [ABSTRACT FROM AUTHOR]

Performance of modern automated pattern recognition (PR) systems is heavily influenced by accuracy of their feature extraction algorithm. Many papers have demonstrated uses of deep learning techniques in PR, but there is little evidence on using them as feature extractors. Our goal is to contribute to this field and perform a comparative study between classical used methods in feature extraction and deep learning techniques. For that, a biometric recognition system, which is a PR application, is developed and evaluated using a proposed evaluation metric called expected risk probability. In our study, two deeply learned features, based on PCANet and DCTNet deep learning techniques, are used with two biometric modalities that are palmprint and palm-vein. Subsequently, the efficiency of these techniques is compared with various classical feature extraction methods. From the obtained results, we drew our conclusions on a very positive impact of deep learning techniques on overall recognition rate, and thus these techniques significantly outperform the classical techniques. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033008] [ABSTRACT FROM AUTHOR]

Latent representation features in deep learning (DL) exhibit excellent potential for visual data applications. For example, in traffic monitoring and video surveillance, the features simultaneously perform image analysis for machine vision and image reconstruction for human viewing. However, the existing deep features that appeal to machine and human receivers are always combinations of separated pieces and specific features. Due to these features being extracted from different branches in collaboration frameworks, the inherent relations between machine and human vision are insufficiently explored. Therefore, to obtain one set of representative and generic features, we propose a dynamic groupwise splitting network based on image content to explore and extract generic features for the two different receivers. First, we analyze the characteristics of the latent features and adopt intermediate features as the base features. Then, a feature classification and transformation mechanism based on image content is proposed to enhance the base features for further image reconstruction and analysis. Consequently, an end-to-end model with multimodel cascading and multistage training realizes both machine and human vision tasks. Extensive experiments show that our human–machine vision collaboration framework has high practical value and performance. [ABSTRACT FROM AUTHOR]

Image oversegmentation creates small, compact, and irregularly shaped regions subject to further clustering. Consideration of texture characteristics can improve the resulting quality of the clustering process. Existing methods based on an orthogonal transform into frequency domain can extract texture features of arbitrarily shaped regions only from inscribed rectangles. We propose a method for extracting texture features of entire arbitrarily shaped image regions using orthogonal transforms. Furthermore, we introduce a mathematically correct method for unifying spectral dimensions that is necessary for accurate comparison and classification of spectra with different dimensions. The proposed method is particularly suitable for classifying areas with periodic and quasiperiodic textures. Our approach exploits the texture periodification property of certain orthogonal transforms that is based on insertion of zeros into the spectrum. We identified some of those orthogonal transforms which possess this important property and also provide mathematical proofs of our claims. Last, we show that inclusion of luminance and chrominance components into the feature vector increases the precision of the proposed method which then becomes suitable for natural scene images as well. [ABSTRACT FROM AUTHOR]

The purpose of behavior recognition is to recognize the actions of the human body in action. It plays a great role in surveillance, video recommendation, and human-computer interaction with video. With the rise of neural networks, behavior recognition has also continued to develop and progress and has reached a relatively advanced level. However, behavior recognition is still insufficient in recognizing complex human movements and recognizing videos in different bands. To solve this problem, this paper establishes a convolutional neural network (CNN) cross-spectral human behavior recognition algorithm based on global time domain representation. It adopts the method of time-domain feature extraction, construction of optimized convolutional neural network layers, and global time-domain cross-spectrum construction. It also uses videos from the unified compliance framework (UCF)-sports and UCF-11 datasets for experiments. Experiments show that the algorithm achieves an average accuracy of 90% in the behavior recognition of UCF-sports. It still maintains an average accuracy rate of >90 % in the more complex behavior recognition of UCF-11, and the highest accuracy rate is 93%. [ABSTRACT FROM AUTHOR]

The capability of field emission scanning electron microscopy and energy dispersive x-ray spectroscopy (FE-SEM/EDX) to scan material structures at the microlevel and characterize the material with its elemental properties has inspired this research, which has developed an FE-SEM/EDX-based cervical cancer screening system. The developed computer-aided screening system consisted of two parts, which were the automatic features of extraction and classification. For the automatic features extraction algorithm, the image and spectra of cervical cells features extraction algorithm for extracting the discriminant features of FE-SEM/EDX data was introduced. The system automatically extracted two types of features based on FE-SEM/EDX images and FESEM/ EDX spectra. Textural features were extracted from the FE-SEM/EDX image using a gray level co-occurrence matrix technique, while the FE-SEM/EDX spectra features were calculated based on peak heights and corrected area under the peaks using an algorithm. A discriminant analysis technique was employed to predict the cervical precancerous stage into three classes: normal, low-grade intraepithelial squamous lesion (LSIL), and high-grade intraepithelial squamous lesion (HSIL). The capability of the developed screening system was tested using 700 FE-SEM/EDX spectra (300 normal, 200 LSIL, and 200 HSIL cases). The accuracy, sensitivity, and specificity performances were 98.2%, 99.0%, and 98.0%, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of electronic computers and information technology, face recognition is widely used in fields such as enterprises, entertainment, information security, and daily life. However, the current face recognition technology is still relatively poor in distinguishing facial features, resulting in a low accuracy of face recognition, which cannot meet increasing application requirements. For this reason, it is necessary to develop more accurate face recognition technology. Residual neural network (ResNet) neural network is a deep residual learning convolutional neural network, which can be used to solve the degeneration problem (i.e., after adding more layers to the neural network, the performance drops rapidly) of deep-learning neural networks. We aim to study the specific principles, calculation methods, and characteristics of the ResNet neural network, analyze the complexity of the classroom environment, propose the use of ResNet network for face feature extraction, and use additive angular margin loss for deep face recognition (ArcFace) as the loss function of the ResNet network to improve the ResNet network. Because the ArcFace function has the advantages of high performance, easy programming, low complexity and high training efficiency. We use this scheme to conduct field tests of face recognition on many students in the classroom. The test results show that this scheme enhances the discrimination of facial features and improves the accuracy of face recognition. In the case of different numbers of people, facial defects, and strong light exposure, the system can still detect and recognize faces stably. In the case of facial defects, the recognition accuracy rate is still 63.3%, and the recognition accuracy rate is still more than 60% under the illumination of strong light, and the recognition accuracy rate under correct conditions is more than 70%. [ABSTRACT FROM AUTHOR]

The demand to design lightweight semantic segmentation models on mobile devices is growing. Current U-shape structures can improve the segmentation accuracy. However, they can hardly achieve lightweight requirements due to their inefficient encoders. Besides, partial details and edges are damaged during the process of repeated downsampling. To this end, we propose an efficient and lightweight U-shape network (ELUNet) for real-time semantic segmentation. In the encoder, a light split-shuffle convolution block is designed as the key component of feature extraction to achieve high-precision segmentation in the resource-limited scene. Furthermore, we propose a bridge channel attention module in the skip connection to selectively emphasize the valuable features. In the decoder, we propose an upsample feature fusion module to capture global contextual information, significantly improving the ability of the network to extract spatial information. Moreover, we design an edge refinement module to refine the segmentation predictions further. Extensive experiments prove the effectiveness of the ELUNet on Cityscapes and Camvid benchmarks. Specifically, the ELUNet contains only 2.0M parameters and achieves 73.3% mIoU on Cityscapes validation set with the speed of 52.6 FPS for a 512 × 1024 input image on a single 1080Ti GPU. [ABSTRACT FROM AUTHOR]

Currently, the reported image hashing schemes cannot perform satisfactorily in some aspects, such as rotation robustness. To this end, a perceptual color image hashing scheme is proposed. First, the original image is normalized and smoothed by Gaussian low-pass filter. The obtained secondary image is divided into a series of ring-ribbons with different radii and the same number of pixels. Then, textural and color features are extracted in local and global manners. Specifically, local textural features are extracted on luminance values of the ring-ribbons using quadtree decomposition, and global textural features are extracted by gray level co-occurrence matrix. Local color features of significant corner points are extracted on outer boundaries of ring-ribbons through color vector angles, and global color features are extracted by low-order moments. The extracted features are concatenated after quantization and permutation to generate the final hash. Receiver operating characteristic curves verified the effectiveness of our scheme, including robustness, discrimination, and security, which can be effectively applied in content authentication and tampering detection. [ABSTRACT FROM AUTHOR]

The stationarity of a texture can be considered a fundamental property of images, although the property of stationarity is difficult to define precisely. We propose a stationarity test based on multiscale, locally stationary, 2D wavelets. Three separate experiments were performed to evaluate the capabilities and the limitations of this test. The experiments comprised a chessboard stationarity analysis, two classification tasks, and a psychophysical experiment. The classification tasks were performed on 110 texture images from a texture database. In one subtask, five texture feature vectors were extracted from each image and the classification accuracy of two classical methods compared, whereas in the second subtask, the classification accuracy of several methods was compared to the descriptors defined for each image within the database. In the psychophysical experiment, the correlation between the classification results and observer judgements of texture similarity were determined. It was found that a combination of wavelet shrinkage and rotation-invariant local binary pattern best predicted the observer response. The results show that the proposed stationarity test is able to provide relevant information for texture analysis. [ABSTRACT FROM AUTHOR]

We address kinship verification, which is a challenging problem in computer vision and pattern discovery. It has several applications, such as organizing photoalbums, recognizing resemblances among humans, and finding missing children. We present a system for facial kinship verification based on several kinds of texture descriptors (local binary patterns, local ternary patterns, local directional patterns, local phase quantization, and binarized statistical image features) with pyramid multilevel (PML) face representation for feature extraction along with our proposed paired feature representation and our proposed robust feature selection to reduce the number of features. The proposed approach consists of the following three main stages: (1) face preprocessing, (2) feature extraction and selection, and (3) kinship verification. Extensive experiments are conducted on five publicly available databases (Cornell, UB KinFace, Family 101, KinFace W-I, and KinFace W-II). Additionally, we provided a wide experiment for each stage to find the best and most suitable settings. We present many comparisons with state-of-the-art methods and through these comparisons, it appears that our experiments show stable and good results. [ABSTRACT FROM AUTHOR]

With the increasing needs in security systems, iris recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for iris recognition by analyzing iris patterns. To improve the rate of recognition, we divide the normalized iris image into several regions to keep the iris image away from several noise factors, such as eyelids, eyelashes, and motion blur. For feature extraction, the local edge pattern (LEP) operator is designed to capture local characteristics of the iris image to produce discriminating texture features in every region. A resulting 2D feature vector is mapped into a low-dimensional subspace using two dimension linear discriminant analysis (2DLDA), and then the minimum distance classifier (MDC) is adopted for recognition. Experiments on the public and freely available iris images taken from the CASIA (Institute of Automation, Chinese Academy of Sciences) and UBIRIS databases confirm the advantage of the proposed approach in terms of speed and accuracy. [ABSTRACT FROM AUTHOR]