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A novel three‐dimensional (3D) surface texture measure (3DSTM) is presented based on the micro‐geometry of paper surfaces to classify different paper substrates. This is useful to automatically determine whether a document is printed on the correct paper substrate to help identify fraud. We use a 4‐light source photometric stereo (PS) method to recover the dense 3D geometry of paper surfaces captured using a high‐resolution sensing device. We derive a unique 3DSTM for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed 3DSTM can robustly and accurately classify paper substrates with different physical properties and different surface textures. The accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non‐printed paper types and a measure of success over 92% is achieved. [ABSTRACT FROM AUTHOR]

To effectively mine the contents embedded in web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis. The authors herein propose a hierarchical algorithm for efficiently classifying web images into four classes. Their algorithm consists of two stages: the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first stage classifier are processed by the second stage, which further extracts local texture features represented in the bag-of-words framework and uses another SVM classifier for final classification. In addition, they design two fusion strategies to train the second-stage classifier and generate the final prediction depending on the usage of local features in the second stage. To validate the effectiveness of proposed method, they built a database containing more than 55,000 images from various sources. On their test image set, they obtained an overall classification accuracy of 98.4% and the processing speed is over 27 fps on an Intel(R) Xeon(R) central processing unit (2.90 GHz).

This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself. [ABSTRACT FROM AUTHOR]

Face recognition systems are becoming ubiquitous, but they are vulnerable to spoofing attacks. The recently available light field cameras can be used for spoofing attack detection. In this study, the IST Lenslet Light Field Face Spoofing Database (IST LLFFSD) is proposed, consisting of 100 genuine images, from 50 subjects, captured with a Lytro ILLUM lenslet light field camera, and a set of 600 face spoofing attack images, captured using the same camera. The IST LLFFSD simulates six different types of presentation attacks, including printed paper, wrapped printed paper, laptop, tablet and two different mobile phones. This study also proposes a novel spoofing attack detection solution, based on a compact, yet effective, descriptor exploiting the colour and texture variations associated with the different directions of light captured in light field images. Extensive experiments show very effective results, with the proposed solution performing better than state‐of‐the‐art alternatives for the face spoofing attack types considered. [ABSTRACT FROM AUTHOR]

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, we first introduce the truncated Huber penalty function which shows strong flexibility under different parameter settings. A generalized framework is then proposed with the introduced truncated Huber penalty function. When combined with its strong flexibility, our framework is able to achieve diverse smoothing natures where contradictive smoothing behaviors can even be achieved. It can also yield the smoothing behavior that can seldom be achieved by previous methods, and superior performance is thus achieved in challenging cases. These together enable our framework capable of a range of applications and able to outperform the state-of-the-art approaches in several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, image texture removal, etc. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. A simple yet effective approach is further proposed to reduce the computational cost of our method while maintaining its performance. The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications. Our code is available at https://github.com/wliusjtu/Generalized-Smoothing-Framework . [ABSTRACT FROM AUTHOR]

This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images. [ABSTRACT FROM AUTHOR]

An in-depth determination of date fruit properties belonging to a given variety can have an impact on their consumption, processing, and storage. The objective of this study was to characterize date fruits of the 'Mejhoul' variety using (i) objective and non-destructive image-analysis features and (ii) measurements of physicochemical parameters. Based on images acquired using a digital camera, more than 1600 texture parameters from the individual color channels L, a, b, R, G, B, X, Y, and Z, and 40 geometric characteristics (including linear dimensions and shape factors for each fruit), were determined. Additionally, pomological features, water content, water activity, color parameters (L*, a*, b*), total soluble solids (TSS), reducing sugars, and total sugars were measured. As a main result, the application of machine vision allowed for the correct detection of 'Mejhoul' dates and the determination of the image features. The differences in the values of the histogram's mean (HMean texture) for individual color channels were determined. The 'Mejhoul' date fruit images in color channel a (aHMean equal to 145.88) and color channel b (bHMean: 145.49) were the brightest, and in channel Z they were the darkest (ZHMean: 4.23). Due to the determination of the elliptic shape factor (W1) of 1.000 and the circular shape factor (W2) of 0.110, the elliptical shape of the fruit was confirmed. On the other hand, 'Mejhoul' dates were characterized by a length of 47.3 mm, a diameter of 26.4 mm, flesh thickness of 6.25 mm, total soluble solids of 62.1%, water content of 28.0%, water activity of 0.652, hardness of 694 g, reducing sugars of 13.8%, and total sugars of 58.8%. Due to the determination of many image features and other parameters, this paper presents the first comprehensive characterization of 'Mejhoul' date fruits using a non-destructive imaging technique linked to some physicochemical quality attributes. [ABSTRACT FROM AUTHOR]

Texture-based informal settlement (IS) mapping has gained attention in urban remote sensing (RS) research. Numerous studies conducted on the use of texture analysis for IS mapping have investigated wide-ranging sensors, algorithms, scale, classifiers, feature selection methods, and other factors of interest. However, no study has systematically investigated key factors affecting texture-based classification processes. This paper presents a detailed synthesis of scientific progress in texture based IS mapping. Results revealed that grey level co-occurrence matrix was the most popularly used algorithm.Quickbird was the widely used sensor. The use of machine-learning classifiers, particularly, support vector machine and random forest yielded, comparatively, high accuracies (>80%). Interestingly, deep learning showed potential to advance IS identification. Multi-city comparison studies demonstrated need for texture features to be locally specific in order to allow transferability. Thus, integration of RS data and field survey statistics could be crucial in enhancing understanding of morphological variations for improved IS mapping. [ABSTRACT FROM AUTHOR]

This paper introduces a new HEp‐2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co‐occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP‐GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures. [ABSTRACT FROM AUTHOR]

Road monitoring plays a vital role in emergency disposal, traffic accident liability determination, traffic trend analyzes, and other fields. This paper designs a real-time virtual-real fusion framework for multiview surveillance of large-scale scene, in which 2D panorama, satellite texture, and 3D model are merged. The system is implemented with the following contributions. First, a novel automatic mosaic algorithm for multiview overlapped images is proposed. Feature points between adjacent cameras are detected via comotion statistics map. In addition, transformation model is obtained using random sample consensus algorithm. Second, multiview images are morphed to a same overhead viewpoint. Finally, 2D panoramic road image and 3D scene model are combined based on high-precision ground control points. The proposed framework has been successfully applied to a large road intersection. With the virtual-real fusion approach, observers can freely monitor and ramble in the large-scale 3-D road scene. [ABSTRACT FROM PUBLISHER]

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k‐means (KM) and fuzzy c‐means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k‐means (IKM) and improved fuzzy c‐means (IFCM) clustering algorithms segment the INSAT‐3D satellite's thermal infrared image into low‐level, middle‐level, high‐level clouds and non‐cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non‐cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the Fourier power spectrum of the quasi-flat-zone-based scale space of their input. The descriptors are evaluated with the UC Merced Land Use-Land Cover data set, which has been only recently made public. The proposed approach is shown to outperform the best known retrieval scores, despite its shorter feature vector length, thus asserting the practical interest of global content descriptors as well as of mathematical morphology in this context. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess the accuracy of C-band synthetic aperture radar (SAR) datasets in mapping peatland types over a region of Canada's subarctic boreal zone. This paper assessed contributions of quad-polarization linear backscatter intensities (σ°HH, σ°HV, σ°VV), image textures, and two polarimetric scattering decompositions: 1) Cloude–Pottier, and 2) Freeman–Durden. Four quad-polarimetric RADADSAT-2 images were studied at incidence angles of 19.4°, 23.1°, 45.8°, and 48.1°. The influence of combining dual-angular information acquired within a short temporal span was also assessed. These C-band SAR data were used to classify peatlands according to isolated flat bogs (bogs), channel fens (fens), raised peat plateaus (plateaus), and forested uplands (uplands) using a supervised support vector machine (SVM) classifier. Numerous classifications were examined to compare the unique contributions of these variables to classification accuracy. Results suggest linear backscatter variables in isolation produce comparable classification results with those of the Freeman–Durden and Cloude–Pottier decompositions. Combining polarimetric decomposition and texture data into classifications with linear backscatter data resulted in only minor (∼1–3%) improvement. Combining classifications from small and large incidence angles (dual-angular) significantly improved classification results over those of a single image. Classification accuracy was the highest for isolated bogs and open water surfaces, whereas fens, uplands, and plateaus had lower accuracies. The highest accuracy classification (84% and kappa coefficient of 0.80) used a dual-angular approach, with additional decomposition and texture information. However, it is noted that texture information rarely improved classification results across all tests. This approach identified isolated flat bogs, channel fens, and raised peat plateaus with >76% producer's accuracies. [ABSTRACT FROM PUBLISHER]

Fingerprint-based recognition systems have been increasingly deployed in various applications nowadays. However, the recognition systems can be spoofed by using an accurate imitation of a live fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection method which achieves good detection accuracy. We regard the fingerprint liveness detection as a two-class classification problem and construct co-occurrence array from image gradients to extract features. In doing so, the quantization operation is firstly conducted on the images. Then, the horizontal and vertical gradients at each pixel are calculated, and the gradients of large absolute values are truncated into a reduced range. Finally, the second-order and the third-order co-occurrence arrays are constructed from the truncated gradients, and the elements of the co-occurrence arrays are directly used as features. The second-order and the third-order co-occurrence array features are separately utilized to train support vector machine classifiers on two publicly available databases used in Fingerprint Liveness Detection Competition 2009 and 2011. The experimental results have demonstrated that the features extracted with the third-order co-occurrence array achieve better detection accuracy than that with the second-order co-occurrence array and outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The thermal noise-induced distortions in a Sentinel-1 terrain observation with progressive scans synthetic aperture radar image cannot be corrected by the noise equivalent sigma nought (NESZ) subtraction only. Since the thermal noise is scaled during synthetic aperture radar processing, it resides not only as an additive noise in each pixel but also as a multiplicative noise in the interpixel contrast. In this paper, we investigate the noise characteristics and propose an efficient method for the multiplicative textural noise correction. The core ideas are to find the optimal coefficient of the noise-induced standard deviation (SD) and model the noise contribution to the local SD as a function of the NESZ and the signal-to-noise ratio. Denoising is accomplished by a subwindow-wise adaptive rescaling of the pixel values. The improvements in the first- and second-order statistical textural features demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of reduced-resolution transcoding of compressed video bitstreams. An analysis of drift errors is provided to identify the sources of quality degradation when transcoding to a lower spatial resolution. Two types of drift error are considered: a reference picture error, which has been identified in previous works, and error due to the noncommutative property of motion compensation and down-sampling, which is unique to this work. To overcome these sources of error, four novel architectures are presented. One architecture attempts to compensate for the reference picture error in the reduced resolution, while another architecture attempts to do the same in the original resolution. We present a third architecture that attempts to eliminate the second type of drift error and a final architecture that relies on an intrablock refresh method to compensate for all types of errors. In all of these architectures, a variety of macroblock level conversions are required, such as motion vector mapping and texture down-sampling. These conversions are discussed in detail. Another important issue for the transcoder is rate control. This is especially important for the intra-refresh architecture since it must find a balance between number of intrablocks used to compensate for errors and the associated rate-distortion characteristics of the low-resolution signal. The complexity and quality of the architectures are compared. Based on the results, we find that the intra-refresh architecture offers the best tradeoff between quality and complexity and is also the most flexible. [ABSTRACT FROM PUBLISHER]

Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods. [ABSTRACT FROM AUTHOR]

The paper presents the development of a method of computer-aided diagnosis of sarcoidosis based on chest X-ray images, for particular stages of the disease. For this purpose, the research material, which consisted of 98 chest X-rays, was analyzed. The datasets included images for healthy cases and for first, second and third degree sarcoidosis. The research material was pre-processed, after which, on the basis of framing, the regions of interest (ROIs) were extracted from the images for individual cases. Next, the analysis of the selected ROIs was carried out, resulting in discriminatory characteristics describing the properties of the images. For the obtained sets, due to their multidimensionality, extraction and selection of features were carried out. Based on the analysis of the obtained results, a selection of features was selected to reduce the data dimension. Three methods were used to carry it out. In the case of heuristic identification of variables, datasets counting respectively for set X-ray2: 34, X-ray3: 47 textural features were obtained. On the basis of the obtained sets, classifiers were built using the supervised learning method. As a result, one model was obtained, based on a single classifier, for the X-ray2 dataset, with a classification error equal to zero. For the X-ray3 dataset, one model was obtained, which was based on an aggregated classifier consisting of two component classifiers and for which the classification error was also equal to zero. The resulting models were proposed as a final solution. The resulting feature vectors and models obtained during the research can be used to build a computer system that will carry out the diagnostic process automatically. The developed solution allows us to classify images for X-ray imaging, depending on the degree of sarcoidosis, into two categories: healthy or sick. This makes it possible to build a system that improves the work of the diagnostician in the process of diagnosing the disease, by reducing the time and cost of performing image analysis, as well as for the patient's condition, thanks to faster referral to advanced clinical trials. [ABSTRACT FROM AUTHOR]

Eddy current pulsed thermography (ECPT) is implemented for detection and separation of impact damage and resulting damages in carbon fiber reinforced plastic (CFRP) samples. Complexity and nonhomogeneity of fiber texture as well as multiple defects limit detection identification and characterization from transient images of the ECPT. In this paper, an integration of principal component analysis (PCA) and independent component analysis (ICA) on transient thermal videos has been proposed. This method enables spatial and temporal patterns to be extracted according to the transient response behavior without any training knowledge. In the first step, using the PCA, the data is transformed to orthogonal principal component subspace and the dimension is reduced. Multichannel morphological component analysis, as an ICA method, is then implemented to deal with the sparse and independence property for detecting and separating the influences of different layers, defects, and their combination information in the CFRP. Because different transient behaviors exist, multiple types of defects can be identified and separated by calculating the cross-correlation of the estimated mixing vectors between impact the ECPT sequences and nondefect ECPT sequences. [ABSTRACT FROM PUBLISHER]

Infrared images typically contain obvious strip noise. It is a challenging task to eliminate such noise without blurring fine image details in low-textured infrared images. In this paper, we introduce an effective single-image-based algorithm to accurately remove strip-type noise present in infrared images without causing blurring effects. First, a 1-D row guided filter is applied to perform edge-preserving image smoothing in the horizontal direction. The extracted high-frequency image part contains both strip noise and a significant amount of image details. Through a thermal calibration experiment, we discover that a local linear relationship exists between infrared data and strip noise of pixels within a column. Based on the derived strip noise behavioral model, strip noise components are accurately decomposed from the extracted high-frequency signals by applying a 1-D column guided filter. Finally, the estimated noise terms are subtracted from the raw infrared images to remove strips without blurring image details. The performance of the proposed technique is thoroughly investigated and is compared with the state-of-the-art 1-D and 2-D denoising algorithms using captured infrared images. [ABSTRACT FROM AUTHOR]

Image compression, as one of the key-enabling technologies in multimedia communications, has been paid much attention in the past decades, where the two key techniques discrete wavelet transform (DWT) and set-partitioning in hierarchical trees (SPIHT) have great influence on its final performance. Due to the proprieties of fast computation, low memory requirement, DWT has been adopted as a new technical standard for still image compression. But it did not make much use of the region information. Although several improved methods have been proposed that adopt direction-adaptive wavelet for using the geometric and spatial information, they still did not consider the texture information. Furthermore, the traditional SPIHT algorithm has the drawbacks of long bits output and time consuming. In this paper, we first propose a new technique named interpolation-based direction-adaptive lifting DWT. It can adaptively choose the best lifting direction and use the Lagrange interpolation technique to make prediction according to its local characteristics. This method makes good use of the image texture features. Then a modified SPIHT coding algorithm is presented. It improves the scanning process and can effectively reduce the coding bits length and running time. Experimental results demonstrate that our method can yield better results than traditional techniques. [ABSTRACT FROM PUBLISHER]

Degraded image semantic segmentation is of great importance in autonomous driving, highway navigation systems, and many other safety-related applications and it was not systematically studied before. In general, image degradations increase the difficulty of semantic segmentation, usually leading to decreased semantic segmentation accuracy. Therefore, performance on the underlying clean images can be treated as an upper bound of degraded image semantic segmentation. While the use of supervised deep learning has substantially improved the state of the art of semantic image segmentation, the gap between the feature distribution learned using the clean images and the feature distribution learned using the degraded images poses a major obstacle in improving the degraded image semantic segmentation performance. The conventional strategies for reducing the gap include: 1) Adding image-restoration based pre-processing modules; 2) Using both clean and the degraded images for training; 3) Fine-tuning the network pre-trained on the clean image. In this paper, we propose a novel Dense-Gram Network to more effectively reduce the gap than the conventional strategies and segment degraded images. Extensive experiments demonstrate that the proposed Dense-Gram Network yields state-of-the-art semantic segmentation performance on degraded images synthesized using PASCAL VOC 2012, SUNRGBD, CamVid, and CityScapes datasets. [ABSTRACT FROM AUTHOR]

Several human being traits can be used as a robust and distinctive identifier for a given person. The palm region of the hand is one of these features that researchers in biometric fields have given a huge consideration in recent years. Many works have been proposed in the literature to design palmprint (an image acquired of the palm region) recognition framework. Extraction of prominent image local features is a critical module in most of these approaches. Local Binary Patterns (LBP) like methods, have emerged as one of the most effective feature extraction techniques. Despite a period of remarkable evolution, neither extensive and comprehensive evaluation nor comparison has been performed to date on a large number of LBP variants and non‐LBP texture methods in palmprint recognition problem. Motivated by this, this paper aims to fill that gap and provide a comprehensive comparative study of the performance of a large number of recent texture descriptors in palmprint recognition. Extensive experimental results on the well‐known constrained and unconstrained challenging palmprint databases, indicate that a number of tested local texture descriptors, which are evaluated for the first time on palmprint recognition, achieve promising results. Classification results are statistically compared through Wilcoxon signed rank test. [ABSTRACT FROM AUTHOR]

During the process of watching 3D synthesised video (3D‐SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D‐SV with binocular asymmetric distortions is a new and challenging problem. Aiming at this problem, a blind quality assessment method for 3D‐SV with binocular asymmetric distortions is proposed. Firstly, the local edge deformations of synthesised videos at different scales are measured by calculating their standard deviations. Secondly, the global naturalness of synthesised videos is computed by analysing their natural statistical characteristics. Thirdly, a strategy for fusing left and right quality scores is proposed, which considers their texture information in different directions. Finally, the random forest is used to obtain an objective quality score. The experimental results show the superiority of the proposed method on asymmetry 3D‐SV database. [ABSTRACT FROM AUTHOR]

This study is concerned with analysing face–ocular multimodal biometric systems for a person gender prediction. Particularly, this is the first study considering fusion of face and ocular biometrics to predict gender of a person via a hybrid multimodal scheme. The authors aim to investigate the effect of multimodal biometric systems at score and feature‐level fusion on gender classification. The implementation of uniform local binary pattern (ULBP) feature extractor is taken into account to extract the face and ocular texture information. This paper proposes to select the efficient feature sets of both modalities using a novel evolutionary algorithm called backtracking search algorithm (BSA). On the other hand, support vector machine (SVM) is applied for classification purpose using the fused face and ocular features and scores. The proposed scheme is validated using CASIA‐Iris‐Distance and MBGC multimodal biometric databases with consideration of a subject‐disjoint training and testing evaluation. The achieved gender recognition demonstrates the superiority of the hybrid multimodal face–ocular scheme over unimodal face and ocular schemes implemented in this study for a subject gender prediction. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of writer identification from handwriting samples in Arabic. The proposed technique relies on extracting small fragments of writing which are characterised using two textural descriptors, Histogram of Oriented Gradients (HOG) and Gray Level Run Length (GLRL) Matrices. Similarity scores realised using HOG and GLRL features are combined using a number of fusion rules. The system is evaluated on three well‐known Arabic handwriting databases, the IFN/ENIT database with 411 writers, the KHATT database with 1000 writers, and QUWI database with 1,017 writers. Fusion using the 'sum' rule reports the highest identification rates reading 96.86, 85.40, and 76.27% on IFN/ENIT, KHATT, and QUWI databases, respectively. The results realised on the KHATT database are comparable to the state of the art while those reported on the IFN/ENIT and QUWI databases are the highest to the best of authors' knowledge. [ABSTRACT FROM AUTHOR]

Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter. Subsequently, a back propagation network (BPN) scheme using significant image texture features as the input is established to estimate the GPU-based bilateral filter parameters and its denoising process. The k-fold cross validation method is exploited to evaluate the performance of the proposed automatic restoration framework. A wide variety of T1-weighted brain MR images were employed to train and evaluate this parameter-free decision system with GPU-based bilateral filtering, which resulted in a speed-up factor of 208 comparing to the CPU-based computation. The proposed filter parameter prediction system achieved a mean absolute percentage error (MAPE) of 6% and was classified as "high accuracy". Our automatic denoising framework dramatically removed noise in numerous brain MR images and outperformed several state-of-the-art methods based on the peak signal-to-noise ratio (PSNR). The usage of image texture features associated with the BPN to estimate the GPU-based bilateral filter parameters and to automate the denoising process is feasible and validated. It is suggested that this automatic restoration system is advantageous to various brain MR image-processing applications. [ABSTRACT FROM AUTHOR]

This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models—(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score—are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions. [ABSTRACT FROM AUTHOR]

Dynamic texture (DT) classification has attracted extensive attention in the field of image sequence analysis. The probability distribution model, which has been used to analysis DT, can describe well the distribution property of signals. Here, the authors introduce the finite mixtures of Gumbel distributions (MoGD) and the corresponding parameter estimation method based on expectation–maximisation algorithm. Then, the authors propose the DT features based on MoGD model for DT classification. Specifically, after decomposing DTs with the dual‐tree complex wavelet transform (DT‐CWT), the median values of complex wavelet coefficient magnitudes of non‐overlapping blocks in detail subbands are modelled with MoGDs. The model parameters are accumulated into a feature vector to describe DT. During the classification, a variational approximation version of the Kullback–Leibler divergence is used to measure the similarity between different DTs. The experimental evaluations on two popular benchmark DT data sets (UCLA and DynTex++) demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The current JPEG steganalysis systems have attained outstanding achievements. A considerable variety of strategies for feature extraction are developed. However, a common shortcoming in the traditional image steganalysis techniques is that they are conducted on the entire image and do not take advantage of the content diversity. In this paper, a new steganalysis algorithm based on image segmentation is proposed to enable us to utilize the content characteristics of JPEG images. The images are segmented into several sub-images according to the texture complexity. The steganalysis features of each type of sub-images with the same or close texture complexity are extracted separately to build a classifier. The steganalysis results of the entire image are determined through a weighted fusing process. Experimental results demonstrate that the proposed method exhibits excellent performance and significantly improves the detection accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a new spectral-spatial classification method for hyperspectral images via morphological component analysis-based image separation rationale in sparse representation. The method consists of three main steps. First, the high-dimensional spectral domain of hyperspectral images is reduced into a low-dimensional feature domain by using minimum noise fraction (MNF). Second, the proposed separation method is acted on each features to generate the morphological components (MCs), i.e., the content and texture components. To this end, the dictionaries for these two components are built by using local curvelet and Gabor wavelet transforms within the randomly chosen image partitions. Then, sparse coding of one of the MCs and update of the associated dictionary are sequentially performed with the other one fixed. To better direct the separation process, an undecimated Haar wavelet with soft threshold is performed for the content component to make it smooth. This process is repeated until some stopping criterion is met. Finally, a support vector machine is adopted to obtain the classification maps based on the MCs. The experimental results with hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed scheme provides better performance when compared with other widely used methods. [ABSTRACT FROM PUBLISHER]

Image mosaic technology is an important technology in the field of image processing. Based on the general adaptability and clustering of genetic algorithm, we improve it, and apply it to the mosaic algorithm in image processing. In this paper, we test the validity and reliability of the designed algorithm in the process of image mosaic algorithm. Based on the image illumination mosaic and painting texture mosaic image we achieve certain artistic effect. From the convergence results of general algorithm, the results of numerical show substantially concussion, oscillation amplitude reaches a maximum of 105 . The calculation results of the genetic algorithm still have certain degree of concussion, oscillation amplitude reaches a maximum of 106, convergence results are slightly better than the general algorithm. The improved genetic algorithm results have no concussion, the stability is very good, and the results have better astringency. [ABSTRACT FROM AUTHOR]

Wavelet‐based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images. [ABSTRACT FROM AUTHOR]

Facial recognition systems are widely used in various daily applications due to their high accuracy rate and ease of use. Unfortunately, their performance drops drastically when encountering spoofing attacks. In this paper, a novel pipeline for face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces is proposed. To detect print and replay spoofing attacks, the HASC descriptor encodes linear and non-linear dependencies between low-level dense features of the color face image using covariance and information-theoretic measures. The combination of covariance and information-theoretic measures makes HASC an efficient descriptor to explore how the features are interrelated in real and fake faces. The HASC texture descriptions are extracted separately from each color channel of the HSV and YCbCr color spaces, and the resulting feature vectors are concatenated into an extended feature vector to better discriminate between real and fake faces. Extensive experiments are conducted on publicly available databases namely, Replay-Attack, CASIA FASD, and MSU MFSD. The obtained results demonstrate that while being of low dimension, the HASC descriptor when fed into a binary Support Vector Machine (SVM) classifier, achieves competitive results compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

In this paper, we propose a micro-macro feature combination approach for texture classification. The two disparate yet complementary categories of features are combined. By this way, Local Binary Pattern (LBP) plays the role of micro-structure feature extractor while the scattering transform captures macro-structure information. In fact, for extracting the macro-type features, coefficients are aggregated from three different layers of the scattering network. It is a handcrafted convolution network which is implemented by computing consecutively wavelet transforms and modulus non-linear operators. By contrast, in order to extract micro-structure features which are rotation-invariant, relatively robust to noise and illumination change, the completed LBP is utilized alongside the biologically-inspired filtering (BF) preprocessing technique. Overall, since the proposed framework can exploit the advantages of both feature types, its texture representation is not only invariant to rotation, scaling, illumination change but also highly discriminative. Intensive experiments conducted on many texture benchmarks such as CUReT, UIUC, KTH-TIPS-2b, and OUTEX show that our framework has a competitive classification accuracy. [ABSTRACT FROM AUTHOR]

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods. [ABSTRACT FROM PUBLISHER]

Helium ion microscopes (HIM) are capable of acquiring images with better than 1 nm resolution, and HIM images are particularly rich in morphological surface details. However, such images are generally quite noisy. A major challenge is to denoise these images while preserving delicate surface information. This paper presents a powerful slow motion denoising technique, based on solving linear fractional diffusion equations forward in time. The method is easily implemented computationally, using fast Fourier transform (FFT) algorithms. When applied to actual HIM images, the method is found to reproduce the essential surface morphology of the sample with high fidelity. In contrast, such highly sophisticated methodologies as Curvelet Transform denoising, and Total Variation denoising using split Bregman iterations, are found to eliminate vital fine scale information, along with the noise. Image Lipschitz exponents are a useful image metrology tool for quantifying the fine structure content in an image. In this paper, this tool is applied to rank order the above three distinct denoising approaches, in terms of their texture preserving properties. In several denoising experiments on actual HIM images, it was found that fractional diffusion smoothing performed noticeably better than split Bregman TV, which in turn, performed slightly better than Curvelet denoising. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a method to detect co-saliency from an image pair that may have some objects in common. The co-saliency is modeled as a linear combination of the single-image saliency map (SISM) and the multi-image saliency map (MISM). The first term is designed to describe the local attention, which is computed by using three saliency detection techniques available in literature. To compute the MISM, a co-multilayer graph is constructed by dividing the image pair into a spatial pyramid representation. Each node in the graph is described by two types of visual descriptors, which are extracted from a representation of some aspects of local appearance, e.g., color and texture properties. In order to evaluate the similarity between two nodes, we employ a normalized single-pair SimRank algorithm to compute the similarity score. Experimental evaluation on a number of image pairs demonstrates the good performance of the proposed method on the co-saliency detection task. [ABSTRACT FROM AUTHOR]

In this paper a new method is presented for 3-D terrain visualization via reversible JPEG2000-based blind data hiding with special focus on data synchronization and scalability. Online real-time 3-D terrain visualization involves considerable amount of data. The process is essentially the mapping of the aerial photograph, called texture, onto its corresponding digital elevation model (DEM) implying at least two distinct data inputs. The presence of large disparate data necessitates a compression strategy on one hand and the integration of the DEM and texture into one unit on the other. Whilst the compression must accommodate the scalability requirement originated by the diversity of clients, the unification of data ought to be synchronous. For scalability this paper relies on the multiresolution nature of the DWT-based JPEG2000 standard whereas the synchronized unification of DEM with the texture is realized by the application of a perceptually transparent data hiding strategy in the DWT domain. The proposed method is blind in the sense that only a secret key, if any, and the size of the original DEM are needed to extract the data from the texture image. We believe that this is one of the pioneering methods to propose scalable embedding of DEM in the texture image. The method is cost effective, in terms of memory and bandwidths, which is an advantage, especially, in real-time environments when quicker transfer of data is required. The results of a 3-D visualization simulation effected with our method were encouraging and gave a useful insight to the effectiveness of our method in various bandwidth scenarios. [ABSTRACT FROM PUBLISHER]

This paper examines the task of displaying more information about the environment using a conventional ranging sonar than is available in standard time-of-flight (TOF) maps. A conventional ranging sonar forms an environmental image that displays the information in the echo envelope, similar to a medical ultrasound image. The sonar performs rotational sector scans of simple objects and two complex environments containing various reflecting structures. In acquiring sonar data, we repeatedly reset the conventional sonar to generate a point process whose density relates to the echo amplitude. This point process is displayed as a grayscale image, called a brightness scan (B-scan), analogous to B-scans in medical ultrasound. We compare the information content of sonar B-scans to TOF maps for object classification and show B-scans to be richer. B-scan textures produced by rough surfaces and volumes containing random scatterers exhibit statistical invariance, similar to some organs within the body, suggesting the feasibility of automated classification. Image artifacts and means for their identification are discussed. The qualitative information present in sonar B-scans should lead to improved quantitative techniques for classifying objects. [ABSTRACT FROM PUBLISHER]

A video bit allocation technique adopting a visual distortion sensitivity model for better rate-visual distortion coding control is proposed in this paper. Instead of applying complicated semantic understanding, the proposed automatic distortion sensitivity analysis process analyzes both the motion and the texture structures in the video sequences in order to achieve better bit allocation for rate-constrained video coding. The proposed technique evaluates the perceptual distortion sensitivity on a macroblock basis, and allocates fewer bits to regions permitting large perceptual distortions for rate reduction. The proposed algorithm can be incorporated into existing video coding rate control schemes to achieve same visual quality at reduced bitrate. Experiments based on H.264 JM7.6 show that this technique achieves bit-rate saving of up to 40.61%. However, the conducted subjective viewing experiments show that there is no perceptual quality degradation. EDICS-1-CPRS, 3-QUAL. [ABSTRACT FROM PUBLISHER]

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, luminance offset and contrast scaling parameter are typically used as the fractal indices. However, luminance offset and contrast scaling parameter are strongly correlated. In this paper, we prove that range block mean and contrast scaling parameters are independent. Based on this independence, we propose four statistical indices for efficient image retrieval. In addition, we propose an efficient hierarchical indexing strategy based on the de and ac component analysis. Experimental results on a database of 416 texture images, created by decomposing 26 images, indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods. [ABSTRACT FROM PUBLISHER]

Many factors, such as the number of vertices and the resolution of texture, can affect the display quality of three-dimensional (3-D) objects. When the resources of a graphics system are not sufficient to render the ideal image, degradation is inevitable. It is, therefore, important to study how individual factors will affect the overall quality, and how the degradation can be controlled given limited resources. In this paper, the essential factors determining the display quality are reviewed. We then integrate two important ones, resolution of texture and resolution of wireframe, and use them in our model as a perceptual metric. We assess this metric using statistical data collected from a 3-D quality evaluation experiment. The statistical model and the methodology to assess the display quality metric are discussed. A preliminary study of the reliability of the estimates is also described. The contribution of this paper lies in: 1) determining the relative importance of wireframe versus texture resolution in perceptual quality evaluation and 2) proposing an experimental strategy for verifying and fitting a quantitative model that estimates 3-D perceptual quality. The proposed quantitative method is found to fit closely to subjective ratings by human observers based on preliminary experimental results. [ABSTRACT FROM PUBLISHER]