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This paper presents a simple, efficient, yet robust approach, named joint orthogonal combination of local ternary pattern, for automatic forward-looking infrared target recognition. It gives more advantages to describe the macroscopic textures and microscopic textures by fusing variety of scales than the traditional LBP-based methods. In addition, it can effectively reduce the feature dimensionality. Further, the rotation invariant and uniform scheme, the robust LTP, and soft concave-convex partition are introduced to enhance its discriminative power. Experimental results demonstrate that the proposed method can achieve competitive results compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

For faded relics, such as Terracotta Army, the 2D-3D registration between an optical camera and point cloud model is an important part for color texture reconstruction and further applications. This paper proposes a nonuniform multiview color texture mapping for the image sequence and the three-dimensional (3D) model of point cloud collected by Handyscan3D. We first introduce nonuniform multiview calibration, including the explanation of its algorithm principle and the analysis of its advantages. We then establish transformation equations based on sift feature points for the multiview image sequence. At the same time, the selection of nonuniform multiview sift feature points is introduced in detail. Finally, the solving process of the collinear equations based on multiview perspective projection is given with three steps and the flowchart. In the experiment, this method is applied to the color reconstruction of the kneeling figurine, Tangsancai lady, and general figurine. These results demonstrate that the proposed method provides an effective support for the color reconstruction of the faded cultural relics and be able to improve the accuracy of 2D-3D registration between the image sequence and the point cloud model. [ABSTRACT FROM AUTHOR]

This paper deals with multi-color space texture classification. Two approaches are proposed and compared: a multi-color space histogram selection (MCSHS) and a multi-color space bin selection. These approaches select local binary pattern (LBP) histograms or LBP bins that have been processed from images coded in multiple color spaces. On the one hand, the proposed LBP-based feature selection scheme overcomes the difficulty of choosing a relevant color space, and on the other hand, it takes advantage of the specific properties of several color spaces by combining them. Experiments show that the MCSHS approach is relevant for color texture classification issues that require good performances whether in accuracy or classification computation time. [ABSTRACT FROM AUTHOR]

Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by nonreal faces such as photographs or videos of valid users. The antispoof problem must be well resolved before widely applying face recognition in our daily life. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to propose a feature descriptor and an efficient framework that can be used to effectively deal with the face liveness detection problem. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and a softmax classifier are concatenated to detect face liveness. We evaluated this approach using the CASIA Face antispoofing database and replay-attack database. The experimental results show that our approach performs better than the state-of-the-art techniques following the provided protocols of these databases, and it is possible to significantly enhance the security of the face recognition biometric system. In addition, the experimental results also demonstrate that this framework can be easily extended to classify different spoofing attacks. ©2016 SPIE and IS&T[DO!: 10.1117/1.JEI.25.4.043014] [ABSTRACT FROM AUTHOR]

Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into ~4 × 4 mm² subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance (p-value < 0.05) between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of f x = 1.37 mm-1, optical wavelength of λ = 490 nm) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign-malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view (cm²) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone. [ABSTRACT FROM AUTHOR]

This paper presents a color-texture descriptor based on the local mapped pattern approach for color-texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color-texture information from the images. These features are combined with texture information from the luminance image in a multiresolution fashion to get the ECLMP feature vector. The robustness of the proposed method is evaluated using the RawFooT, KTH-TIPS-2b, and USPtex databases. The experimental results show that the proposed descriptor is more robust to changes in the illumination condition than 22 alternative commonly used descriptors. [ABSTRACT FROM AUTHOR]

This paper presents a comparison of color spaces for material classification. The study includes three device-independent (CIELAB, CIELUV, and CIE XYZ) and seven device-dependent spaces (RGB, HSV, YIQ, YUV, YCbCr, Ohta's I1 l2I3, and RG-YeB-WhBI). The pros and cons of the different spaces and the procedures for converting color data among them are discussed in detail. An experiment based on 12 different image data sets was carried out to comparatively evaluate the performance of each space for material classification purposes. The results showed that CIELAB markedly outperformed the other spaces followed by HSV and CIELUV. Conversely, CIE XYZ came out as the worst performing space. Interestingly, no significant difference emerged among the performance of the other device-dependent spaces. [ABSTRACT FROM AUTHOR]

An endoscopic image processing technique for enhancing the appearance of microstructures on translucent mucosae is described. This technique employs two pairs of co- and cross-polarization images under two different linearly polarized lights, from which the averaged subtracted polarization image (AVSPI) is calculated. Experiments were then conducted using an acrylic phantom and excised porcine stomach tissue using a manual experimental setup with ring-type lighting, two rotating polarizers, and a color camera; better results were achieved with the proposed method than with conventional color intensity image processing. An objective evaluation method that uses texture analysis was developed and used to evaluate the enhanced microstructure images. This paper introduces two types of online, rigid-type, polarimetric endoscopic implementations using a polarized ring-shaped LED and a polarimetric camera. The first type uses a beam-splitter-type color polarimetric camera, and the second uses a single-chip monochrome polarimetric camera. Microstructures on the mucosa surface were enhanced robustly with these online endoscopes regardless of the difference in the extinction ratio of each device. These results show that polarimetric endoscopy using AVSPI is both effective and practical for hardware implementation. [ABSTRACT FROM AUTHOR]

Considering that the rectangular windows that are used by many super-resolution (SR) methods are not suitable for the arc regions and the running time is long, we propose an image and video SR reconstruction scheme, by which rich texture details can be better maintained and the efficiency is higher as compared with those of some state-of-the-art SR methods. In our approach, we do not use the conventional rectangle windows and interpolation technique in the Cartesian coordinates, instead, we adopt the nonlinear interpolation function with an interpolation window in the polar coordinates to perform the image and video SR reconstruction. The basic idea is first to interpolate every pixel's intensity by the Thiele-Newton's rational function and the Newton-Thiele's rational function both in the polar coordinates, respectively, to get two magnified images, second to set different balance factors for different image patches according to whether they are texture patches or flat patches, and then to add these balanced patches to the two magnified image patches to get final result. We demonstrate the performance of the proposed algorithm in producing high-quality resolution as compared with the state-of-the-art methods, and its application to video sequences without algorithmic modification. Experimental results show that the proposed method achieves much better results than other methods in terms of visual effect, running time, and peak signal-to-noise ratio. ©2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.4 .043035] [ABSTRACT FROM AUTHOR]

Information visualization has appeared as an important research field for multidimensional data and correlation analysis in recent years. Parallel coordinates (PCs) are one of the popular techniques to visual high-dimensional data. A problem with the PCs technique is that it suffers from crowding, a clutter which hides important data and obfuscates the information. Earlier research has been conducted to reduce clutter without loss in data content. We introduce the use of image processing techniques as an approach for assessing the performance of clutter reduction techniques in PC. We use histogram analysis as our first measure, where the mean feature of the color histograms of the possible alternative orderings of coordinates for the PC images is calculated and compared. The second measure is the extracted contrast feature from the texture of PC images based on gray-level co-occurrence matrices. The results show that the best PC image is the one that has the minimal mean value of the color histogram feature and the maximal contrast value of the texture feature. In addition to its simplicity, the proposed assessment method has the advantage of objectively assessing alternative ordering of PC visualization. [ABSTRACT FROM AUTHOR]

We present a framework for dynamic textures (DTs) recognition and localization by using a model developed in the text analysis literature: probabilistic latent semantic analysis (pLSA). The novelty is revealed in three aspects. First, chaotic feature vector is introduced and characterizes each pixel intensity series. Next, the pLSA model is employed to discover the topics by using the bag of words representation. Finally, the spatial layout of DTs can be found. Experimental results are conducted on the well-known DTs datasets. The results show that the proposed method can successfully build DTs models and achieve higher accuracies in DTs recognition and effectively localize DTs. [ABSTRACT FROM AUTHOR]

Automated personal identification using the shape of the human ear is emerging as an appealing modality in biometric and forensic domains. This is mainly due to the fact that the ear pattern can provide rich and stable information to differentiate and recognize people. In the literature, there are many approaches and descriptors that achieve relatively good results in constrained environments. The recognition performance tends, however, to significantly decrease under illumination variation, pose variation, and partial occlusion. In this work, we investigate the use of local texture descriptors, namely local binary patterns, local phase quantization, and binarized statistical image features for robust human identification from two-dimensional ear imaging. In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proven to be more effective in real-world conditions. Our extensive experimental results on the benchmarks IIT Delhi-1, IIT Delhi-2, and USTB ear databases show that local texture features in general and BSIF in particular provide a significant performance improvement compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Image inpainting, an art of modifying or recovering an image in an undetectable way by ordinary observers, has been drawing considerable attention in recent years. In this paper, we propose a novel algorithm to address this problem based on the variation of variances and the linear weighted filling-in under the local patch consistency constraints on pixel values and the first-order gradient. First, a variation of variances of neighboring source patches approach is applied to assign priority to target patches on the image structures (e.g., edges or corners). Second, a linear weighted filling-in scheme, which uses the combinational information of source patches rather than a single source patch, is applied to reconstruct the estimated patches among which the best matching patch is selected. Moreover, the technique of rotating the on-edge patches is introduced to extend the sample space. Experiments on natural images and comparisons with representative existing algorithms show that our proposed method can more robustly discriminate structures and textures, estimate the best matching patch with more known information, and improve the visual quality. [ABSTRACT FROM AUTHOR]

Total variation minimizing methods have known great success in image denoizing. Although edge sharpness is well preserved by these methods, important information like textures or small details is often removed in the process of denoizing. We propose a novel denoizing model which better preserves fine scale features. In our model, a set of local constraints are imposed to keep local variance of the noise at certain scale within its proper bounds. Each constraint corresponds to a certain scale of dyadic region of image and therefore the Lagrangemultiplier in the model is space and scale adaptive, which can control the extent of denoizing over dyadic image regions. We characterize the local constraints by wavelet coefficients in order to transform the model into wavelet domain, and then give the solution and algorithm of the proposed model. Finally, we display some numerical experiments to demonstrate that the proposed model can preserve fine scale features effectively in the process of denoizing and the algorithm is quite practical. [ABSTRACT FROM AUTHOR]

Dynamic texture (DT) and scene descriptions are challenging problems in video understanding that play crucial role in different important applications of computer vision. An efficient framework, called completed and statistical adaptive patterns on three orthogonal planes (CSAP-TOP), for representation of dynamic textures and scenes is addressed in this work. It utilizes adaptive thresholding applied in a completed schema on highorder moment images of three orthogonal planes. Some beneficial properties from its complementary features are inherited: taking into account information from local variations of magnitudes, robustness against noise and near-uniform regions, exploiting more useful and stable textural information at local and regional scales. In addition, we also consider the impact of high-order filtered images and adaptive thresholding in volume statistical adaptive patterns (VSAP) to investigate their influence and efficiency on describing DT and scene sequences. Evaluations of recognition on different datasets of dynamic textures and scenes (UCLA, DynTex, YUPENN, YUP ++) show that our proposal, without utilizing sophisticated learning techniques, significantly outperforms recent results in the state-of-the-art.© 2018 SPIE AND IS&T [DOI: 10.1117/1.JEI.27.5.053044] [ABSTRACT FROM AUTHOR]

We present texture-based active contours method for two-phase image segmentation in a statistical framework. The proposed method first combines color, texture, and saliency weight to form an augmented image and introduces the joint distribution of these features into the image likelihood term in the energy function. Second, we use the local probability distribution to obtain a smooth label that can reduce the fragmentation in the initialization and evolution of segmentation contours. Finally, we propose a simple and efficient geometric prior based directly on the level sets and introduce the related spatial constraints into the Bayes inference to estimate the smooth probabilistic label. Therefore, the image is represented by high-dimensional features but segmented in low-dimensional space. Furthermore, evolving of the level-set function and updating of the smooth probabilistic label are run alternately in a fast manner. We experimentally compare our texture-based method with others on complicated natural images and demonstrate its good performance in practice. © 2018 SPIE and IS&T[DOI: 10.1117/1.JEI.27.5.051211] [ABSTRACT FROM AUTHOR]

Recent studies have shown that spatial information is necessary for hyperspectral image classification, but how to appropriately integrate the distinct spectral and spatial information is still an open issue. Meanwhile, the illposed classification problem due to the limited labeled samples also results in deterioration of recognition performance. An integration of the labeled and unlabeled samples for class discriminant information through semisupervised learning is one of the promising solutions. An integration method of combining different kinds of spectral-spatial features through a semisupervised tritraining learning scheme is proposed. In the method, the dimensionality reduction process is first conducted for a hyperspectral image to reduce the data amount. Subsequently, an extended attribute profile (EAP) is used for texture extraction and spectral-spatial feature stacking. Three different attributions including area, moment of inertia, and diagonal of the box bounding the region are calculated based on the reduced image. Finally, the three kinds of concatenated features are used as inputs of tritraining with three supervised classifiers. These classifiers are trained and refined by the labeled and unlabeled samples in the tritraining process, and an improvement in the final classification accuracy is achieved. In tritraining, to address the problem of the invalid estimation of the classifier error due to the limited labeled samples, a technique based on the posterior probabilities of samples is adopted to estimate the error. Experiments on three benchmark hyperspectral datasets indicate that the proposed method can effectively integrate the information from the spectra, texture, labeled, and unlabeled samples for land cover classification. ©20I8 SPIE and IS&T [DOI: 10.1117/1 .JEI.27.4.043033] [ABSTRACT FROM AUTHOR]

The local binary pattern (LBP) is a simple yet efficient texture operator, and the completed local binary pattern (CLBP) is a completed modeling for LBP that has been adopted in many texture classification methods. However, existing CLBP operators are sensitive to noise and they cannot extract the regional structure information efficiently. To overcome these disadvantages, we propose a circular regional mean completed local binary pattern (CRMCLBP) by introducing a circular regional mean operator to modify the traditional CLBP. We also present two encoding schemes for CRMCLBP. The proposed CRMCLBP not only achieves rotation invariance and completed representation capability but also has high robustness to image noise. In order to evaluate the performance, we compare the CRMCLBP with recent state-of-the-art methods by extensive experiments on two popular texture databases including Outex database and Columbia-Utrecht reflection and texture database. Excellent experimental results demonstrate that the proposed CRMCLBP is comparable with recent state-of-the-art texture descriptors and superior to other approaches for robustness. © 2018 [ABSTRACT FROM AUTHOR]

Brain tissue analysis is highly desired in neurosurgery, such as tumor resection. To guarantee best life quality afterward, exact navigation within the brain during the surgery is essential. So far, no method has been established that perfectly fulfills this need. Optical coherence tomography (OCT) is a promising three-dimensional imaging tool to support neurosurgical resections. We perform a preliminary study toward in vivo brain tumor removal assistance by investigating meningioma, healthy white, and healthy gray matter. For that purpose, we utilized a commercially available OCT device (Thorlabs Callisto) and measured eight samples of meningioma, three samples of healthy white, and two samples of healthy gray matter ex vivo directly after removal. Structural variations of different tissue types, especially meningioma, can already be seen in the raw OCT images. Nevertheless, an automated differentiation approach is desired, so that neurosurgical guidance can be delivered without a-priori knowledge of the surgeon. Therefore, we employ different algorithms to extract texture features and apply pattern recognition methods for their classification. With these postprocessing steps, an accuracy of nearly 98% was found. [ABSTRACT FROM AUTHOR]

A low-resolution depth map can be upsampled through the guidance from the registered highresolution color image. This type of method is so-called guided depth map upsampling. Among the existing methods based on Markov random field (MRF), either data-driven or model-based prior is adopted to construct the regularization term. The data-driven prior can implicitly reveal the relation between color-depth image pair by training on external data. The model-based prior provides the anisotropic smoothness constraint guided by highresolution color image. These types of priors can complement each other to solve the ambiguity in guided depth map upsampling. An MRF-based approach is proposed that takes both of them into account to regularize the depth map. Based on analysis sparse coding, the data-driven prior is defined by joint cosparsity on the vectors transformed from color-depth patches using the pair of learned operators. It is based on the assumption that the cosupports of such bimodal image structures computed by the operators are aligned. The edge inconsistency measurement is explicitly calculated, which is embedded into the model-based prior. It can significantly mitigate texture-copying artifacts. The experimental results on Middlebury datasets demonstrate the validity of the proposed method that outperforms seven state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The imaging and digital processing of anisotropic optical properties of in vivo skin may be helpful in the assessment of its heterogeneous anatomy and physiological functioning. The interaction between polarized light and this organ provides information about the structural order beneath the epidermal layer. However, penetration depth is limited by optical absorption and scattering that are related to local skin characteristics such as pigment density, connective tissue organization, and cutaneous layer thickness. In this work, in vivo healthy skin of a murine model was analyzed using Mueller imaging polarimetry. A gray-level co-occurrence matrix analysis of diattenuation, birefringence, and depolarization measures in four different skin locations showed texture properties that can be used to distinguish local physiology. [ABSTRACT FROM AUTHOR]

The practical identification of individuals using facial recognition techniques requires the matching of faces with specific expressions to faces from a neutral face database. A method for facial recognition under varied expressions against neutral face samples of individuals via recognition of expression warping and the use of a virtual expression-face database is proposed. In this method, facial expressions are recognized and the input expression faces are classified into facial expression groups. To aid facial recognition, the virtual expression-face database is sorted into average facial-expression shapes and by coarse- and fine-featured facial textures. Wrinkle information is also employed in classification by using a process of masking to adjust input faces to match the expression-face database. We evaluate the performance of the proposed method using the CMU multi-PIE, Cohn-Kanade, and AR expression-face databases, and we find that it provides significantly improved results in terms of face recognition accuracy compared to conventional methods and is acceptable for facial recognition under expression variation. [ABSTRACT FROM AUTHOR]

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images. [ABSTRACT FROM AUTHOR]

Color texture classification plays an important role in computer vision applications because texture and color are two fundamental visual features. To classify the color texture via extracting discriminative color texture features in real time, we present an approach of combining the fine texture and coarse color features for color texture classification. First, the input image is transformed from RGB to HSV color space to separate texture and color information. Second, the scale-selective completed local binary count (CLBC) algorithm is introduced to extract the fine texture feature from the V component in HSV color space. Third, both H and S components are quantized at an optimal coarse level. Furthermore, the joint histogram of H and S components is calculated, which is considered as the coarse color feature. Finally, the fine texture and coarse color features are combined as the final descriptor and the nearest subspace classifier is used for classification. Experimental results on CUReT, KTH-TIPS, and New-BarkTex databases demonstrate that the proposed method achieves state-of-the-art classification performance. Moreover, the proposed method is fast enough for real-time applications. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively. [ABSTRACT FROM AUTHOR]

The morphology of the human ear presents rich and stable information embedded on the curved 3-D surface and has as a result attracted considerable attention from forensic scientists and engineers as a biometric recognition modality. However, recognizing a person's identity from the morphology of the human ear in unconstrained environments, with insufficient and incomplete training data, strong person-specificity, and high within-range variance, can be very challenging. Following our previous work on ear recognition based on local texture descriptors, we propose to use anatomical and embryological information about the human ear in order to find the autonomous components and the locations where large interindividual variations can be detected. Embryology is particularly relevant to our approach as it provides information on the possible changes that can be observed in the external structure of the ear. We experimented with three publicly available databases, namely: IIT Delhi-1, IIT Delhi-2, and USTB-1, consisting of several ear benchmarks acquired under varying conditions and imaging qualities. The experiments show excellent results, beyond the state of the art. [ABSTRACT FROM AUTHOR]

We propose an image upsampling algorithm to increase the depth image resolution while preserving edge information and protecting from the texture copy problem. The proposed upsampler selectively combines the spatial, depth range, and color range weight functions for cost calculation according to the weight selection algorithm. In particular, a color range weight function is proposed to reduce the texture copy problem caused by distinct patterns in the color image. Simulation results show that the proposed algorithm outperforms other conventional upsamplers. [ABSTRACT FROM AUTHOR]

The existing methods -- such as sieving, microscope, light scattering, sedimentation, and electrical induction for pigment size detection -- require sampling or scattering the mineral pigments, which will inevitably cause damage to the films painted by mineral pigments. A new detection method based on run length texture analysis is proposed to nondestructively detect the pigment size in the mineral paint film. The films painted by mineral pigments with preknown pigment sizes are contactlessly captured by CCD microscope under diffused light. Gray transform, histogram equalization, and median filtering are implemented to preprocess the captured images, and then the run length texture parameters are extracted from the preprocessed images. A parametric relationship between the extracted parameters and the preknown size is established to predict the pigment size in mineral paint film nondestructively. Burnt carnelian is selected as the sample to verify the feasibility of the proposed method. Results show that the max detection error of the proposed method is 5.548 μm and can be applied to the size detection of the mineral pigments used in mineral paint film. [ABSTRACT FROM AUTHOR]

Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%. [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]

Texture description is a challenging problem with color images. Despite some attempts to include colors in local binary patterns (LBPs), no proposal has emerged as a color counterpart of grayscale LBPs. This is because colors are defined by vectors that are not naturally ordered and several ways exist to compare them. We propose an LBP extension that takes the vector information of color into account due to a color order. As several color orders are available and the selection of the most suitable one is difficult, we combine two of them in a texture descriptor called "mixed color order LBPs." This small-size feature provides good performance on several benchmark databases for two classification problems with regard to larger-size LBP-based features Of color textures. [ABSTRACT FROM AUTHOR]

The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Lymphatic and peripheral nervous system imaging is of prime importance for monitoring various important pathologic processes including cancer development and metastasis, and response to therapy. Optical coherence tomography (OCT) is a promising approach for this imaging task but is challenged by the near-transparent nature of these structures. Our aim is to detect and differentiate semi-transparent materials using OCT texture analysis, toward label-free neurography and lymphography. We have recently demonstrated an innovative OCT texture analysis-based approach that used speckle statistics to image lymphatics and nerves in-vivo that does not rely on negative contrast. However, these two near-transparent structures could not be easily differentiated from each other in the texture analysis parameter space. Here, we perform a rigorous follow-up study to improve upon this differentiation in controlled phantoms mimicking the optical properties of these tissues. The results of the three-parameter Rayleigh distribution fit to the OCT images of six types of tissue-mimicking materials varying in transparency and biophysical properties demonstrate clear differences between them, suggesting routes for improved lymphatics-nerves differentiation. We demonstrate a novel OCT texture analysis-based lymphatics-nerves differentiation methodology in tissue-simulating phantoms. Future work will focus on longitudinal in-vivo lymphangiography and neurography in response to cancer therapeutics toward adaptive personalized medicine. [ABSTRACT FROM AUTHOR]

The representation of a three-dimensional (3-D) scene is essential in multiview imaging technologies. We present a unified geometry and texture representation based on global resampling of the scene. A layered data map representation with a distance-dependent nonuniform sampling strategy is proposed. It is capable of increasing the details of the 3-D structure locally and is compact in size. The 3-D point cloud obtained from the multilayered data map is used for view rendering. For any given viewpoint, image synthesis with different levels of detail is carried out using the quadtree-based nonuniformly sampled 3-D data points. Experimental results are presented using the 3-D models of reconstructed real objects. [ABSTRACT FROM AUTHOR]

Texture and color are important features in analyzing images of natural scenes. Fractal descriptors based on quaternion Fourier transform and local polynomial regression are proposed for color texture image analysis. First, considering the relation between the power spectrum and frequency in the quaternion Fourier transform domain, we proposed fractal dimensions of a color image using a fast quaternion Fourier transform. Second, local polynomial regression is applied to estimate log(spectrum)-log(frequency) curve, which is not usually linear in a natural texture image. Finally, a local polynomial regression curve is defined as fractal descriptors for the color image classification problem with multiclasses. The experimental results show that our proposed approach is more effective than other color texture analysis methods both in the correct classification rate and the duration. [ABSTRACT FROM AUTHOR]

Infrared texture is an important feature in identifying scenery. To simulate infrared image texture effectively at different distances, we propose a model of infrared image texture generation based on scenery space frequency and the image pyramid degradation principle. First, we build a spatial frequency filter model based on imaging distance, taking into account the detector's maximum spatial frequency, and use the filter to process a "zero" distance infrared image texture. Second, taking into consideration the actual temperature difference of the scenery's details due to variation of the imaging distance and the effect of atmospheric transmission, we compare the actual temperature difference with the minimum resolvable temperature difference of the thermal imaging system at a specific frequency and produce a new image texture. The results show that the simulated multiresolution infrared image textures produced by the proposed model are very similar (lowest mean square error = 0.51 and highest peak signal-to-noise ratio = 117.59) to the images captured by the thermal imager. Therefore, the proposed model can effectively simulate infrared image textures at different distances. [ABSTRACT FROM AUTHOR]

Quantitative methods for noninvasive diagnosis of scars are a challenging issue in medicine. This work aims to implement a texture analysis method for quantitatively discriminating abnormal scars from normal scars based on second-harmonic generation (SHG) images. A local difference local binary pattern (LD-LBP) operator combined with a wavelet transform was explored to extract diagnosis features from scar SHG images that were related to the alteration in collagen morphology. Based on the quantitative parameters including the homogeneity, directional and coarse features in SHG images, the scar collagen SHG images were classified into normal or abnormal scars by a support vector machine classifier in a leave-one-out cross-validation procedure. Our experiments and data analyses demonstrated apparent differences between normal and abnormal scars in terms of their morphological structure of collagen. By comparing with gray level co-occurrence matrix, wavelet transform, and combined basic local binary pattern and wavelet transform with respect to the accuracy and receiver operating characteristic analysis, the method proposed herein was demonstrated to achieve higher accuracy and more reliable classification of SHG images. This result indicated that the extracted texture features with the proposed method were effective in the classification of scars. It could provide assistance for physicians in the diagnostic process. [ABSTRACT FROM AUTHOR]

Based on the computer texture analysis method, a new noncontact surface roughness measurement technique is proposed. The method is inspired by the nonredundant directional selectivity and highly discriminative nature of the wavelet representation and the capability of the Markov random field (MRF) model to capture statistical regularities. Surface roughness information contained in the texture features may be extracted based on an MRF stochastic model of textures in the wavelet feature domain. The model captures significant intrascale and interscale statistical dependencies between wavelet coefficients. To investigate the relationship between the texture features and surface roughness Ra, a simple research setup, which consists of a charge-coupled diode camera without a lens and a diode laser, was established, and the laser speckle texture patterns are acquired from some standard grinding surfaces. The research results have illustrated that surface roughness Ra has a good monotonic relationship with the texture features of the laser speckle pattern. If this measuring system is calibrated with the surface standard samples roughness beforehand, the surface roughness actual value Ra can be deduced in the case of the same material surfaces ground at the same manufacture conditions. [ABSTRACT FROM AUTHOR]

An approach that addresses defect detection in textured surfaces based on the wavelet-domain hidden Markov tree (HMT) model is proposed. The proposed scheme includes two successive stages, i.e., training and inspection. During the training process, an HMT for the wavelet transform (WT) of an a priori acquired defect-free template image is modeled using the expectation-maximization (EM) algorithm. With the trained HMT, a log-likelihood map (LLM) that consists of the likelihood of each coefficient can be efficiently constructed. This LLM provides a good classifier for discriminating defects from regular textures. By comparing the LLM of any defective sample under inspection with that of the template, a thresholding process can typically set the coefficients corresponding to the regular texture background to zero, while preserving those corresponding to defective regions. Therefore, in a reconstructed image obtained by the inverse two-dimensional WT of the modified coefficients, the texture patterns will be significantly eliminated, whereas the defective regions will be distinctly highlighted. The performance of the proposed method has been extensively evaluated by a variety of samples with different defect types, shapes, sizes, and texture backgrounds. Experimental results in comparison with other methods demonstrate the effectiveness of the proposed method on defect detection in textured surfaces. [ABSTRACT FROM AUTHOR]

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D ImFs), from finer to large scale. A DEMD-based (2-D)² linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the (2-D)²LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based (2-D)²LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition. [ABSTRACT FROM AUTHOR]

We propose an innovative method that reduces the size of three-dimensional (3-D) volume data while preserving important features in the data. Our method quantifies the importance of features in the 3-D data based on gray level co-occurrence matrix texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact compared with the previous methods. Although an additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data. [ABSTRACT FROM AUTHOR]

An efficient down/upsampling method to compress a depth map efficiently within the high-efficiency video coding (HEVC) framework is presented. A different edge-preserving depth upsampling method is proposed by using both the texture and depth information. We take into account the edge similarity between depth maps and their corresponding texture images as well as the structural similarity among depth maps to build a weight model. Based on the weight model, the optimal minimum mean square error upsampling coefficients are estimated from the local covariance coefficients of the downsampled depth map. The upsampling filter is combined with HEVC to increase coding efficiency. The objective results demonstrate that we achieve a maximum bit rate saving of 32.2% compared to full resolution method and 27.6% compared to a competing depth down/upsampling method on depth bit rate. The subjective evaluation showed that our proposed method achieves better quality in synthesized views than existing methods do. [ABSTRACT FROM AUTHOR]

A novel method to correct texture variations resulting from scale magnification, narrowing caused by cropping into the original size, or spatial rotation is discussed. The variations usually occur in images captured by a camera using different focal lengths. A representative region-matched algorithm is developed to improve texture classification after magnification, narrowing, and spatial rotation. By using a minimum ellipse, a representative region-matched algorithm encloses a specific region extracted by the J-image segmentation algorithm. After translating the coordinates, the equation of an ellipse in the rotated texture can be formulated as that of an ellipse in the original texture. The rotated invariant property of ellipse provides an efficient method to identify the rotated texture. Additionally, the scale-variant representative region can be classified by adopting scale-invariant parameters. Moreover, a hybrid texture filter is developed. In the hybrid texture filter, the scheme of texture feature extraction includes the Gabor wavelet and the representative region-matched algorithm. Support vector machines are introduced as the classifier. The proposed hybrid texture filter performs excellently with respect to classifying both the stochastic and structural textures. Furthermore, experimental results demonstrate that the proposed algorithm outperforms conventional design algorithms. [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]

To improve the detection performance for perturbed quantization (PQ) class [PQ, energy-adaptive PQ (PQe), and texture-adaptive PQ (PQt)] stego images, a detection method based on possible change modes is proposed. First, by using the relationship between the changeable coefficients used for carrying secret messages and the second quantization steps, the modes having even second quantization steps are identified as possible change modes. Second, by referencing the existing features, the modified features that can accurately capture the embedding changes based on possible change modes are extracted. Next, feature sensitivity analyses based on the modifications performed before and after the embedding are carried out. These analyses show that the modified features are more sensitive to the original features. Experimental results indicate that detection performance of the modified features is better than that of the corresponding original features for three typical feature models [Cartesian calibrated PEVny (ccPEV), Cartesian calibrated co-occurrence matrix features (CF), and JPEG rich model (JRM)], and the integrated feature consisting of enhanced histogram features (EHF) and the modified JRM outperforms two current state-of-the-art feature models, namely, phase aware projection model (PHARM) and Gabor rich model (GRM). [ABSTRACT FROM AUTHOR]

Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM; this has been compared with a nonadaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists’ visual predictions. [ABSTRACT FROM AUTHOR]