Search

According to dichromatic reflection model, the previous methods of specular reflection separation in image processing often separate specular reflection from a single image using patch-based priors. Due to lack of global information, these methods often cannot completely separate the specular component of an image and are incline to degrade image textures. In this paper, we derive a global color-lines constraint from dichromatic reflection model to effectively recover specular and diffuse reflection. Our key observation is from that each image pixel lies along a color line in normalized RGB space and the different color lines representing distinct diffuse chromaticities intersect at one point, namely, the illumination chromaticity. For pixels along the same color line, they spread over the entire image and their distances to the illumination chromaticity reflect the amount of specular reflection components. With global (non-local) information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixelwise way for a single image, and it is suitable for real-time applications. Our experimental results on synthetic and real images show that our method performs better than the state-of-the-art methods to separate specular reflection. [ABSTRACT FROM PUBLISHER]

Feature extraction methods for images are becoming increasingly popular because selected image region provide information about the color and intensity. Texture analysis and classification are difficult which have been observed by image processing scientists since late 80s. It can be done in many areas such as face recognition, pattern recognition, and object recognition etc. In this paper, a review of texture feature extraction methods. [ABSTRACT FROM AUTHOR]

Texture segmentation is a very important subject in the fields of computer vision. In order to segment the textures, active contour model based on nonlocal means method and tikhonov regularization is proposed. In detail, a new nonlocal tikhonov regularization smoothness term is added. The nonlocal operator is based on the image slice similarity. So better segmentation accuracy can be achieved for the images which contain special texture features. The good matter of our method is not only nonlocal operator added but also the original tikhonov regularization smoothness item based on the pixel values retained. The nonlocal operator is time consuming, while the reserved smoothness term can save time to some extent. The traditional active contour model can only be used to segment the conventional images. The nonlocal active contour model can dispose the textures well. What's more, in order to improve the computation efficiency, this paper designs the Split-Bregman algorithm. At last, our performance is demonstrated by segmenting many real texture images. [ABSTRACT FROM AUTHOR]

Purpose This paper aims to present a technical approach to evaluate the quality of textures obtained by an inkjet during binder jetting in 3D printing on a powder bed through contours detection to improve the quality of the surface printed according to the result of the assembly between the inkjet and a granular product.Design/methodology/approach The manufacturing process is based on the use of computer-aided design and a 3D printer via binder jetting. Image processing measures the edge deviation of a texture on the granular surface with the possibility of implementing a correction in an active assembly through a “design for manufacturing” (DFM) approach. Example application is presented through first tests.Findings This approach observes a shape alteration of the printed image on a 3D printed product, and the work used the image processing method to improve the model according to the DFM approach.Originality/value This paper introduces a solution for improving the texture quality on 3D printed products realized via binder jetting. The DFM approach proposes an active assembly by compensating the print errors in upstream of a product life cycle. [ABSTRACT FROM AUTHOR]

Multiple feature extraction and combination is one of the most important issues in the content-based image retrieval (CBIR). In this paper, we propose a new content-based image retrieval method based on an efficient combination of shape and texture features. As its shape features, exponent moments descriptor (EMD), which has many desirable properties such as expression efficiency, robustness to noise, geometric invariance, fast computation etc., is adopted in RGB color space. As its texture features, localized angular phase histogram (LAPH) of the intensity component, which is robust to illumination, scaling, and image blurring, is used in hue saturation intensity (HSI) color space. The combination of above shape and texture information provides a robust feature set for color image retrieval. Experimental results on well known databases show significant improvements in retrieval rates using the proposed method compared with some current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a simple, efficient, yet robust approach, named joint-scale local binary pattern (JLBP), for texture classification. In the proposed approach, the joint-scale strategy is developed firstly, and the neighborhoods of different scales are fused together by a simple arithmetic operation. And then, the descriptor is extracted from the mutual integration of the local patches based on the conventional local binary pattern (LBP). The proposed scheme can not only describe the micro-textures of a local structure, but also the macro-textures of a larger area because of the joint of multiple scales. Further, motivated by the completed local binary pattern (CLBP) scheme, the completed JLBP (CJLBP) is presented to enhance its power. The proposed descriptor is evaluated in relation to other recent LBP-based patterns and non-LBP methods on popular benchmark texture databases, Outex, CURet and UIUC. Generally, the experimental results show that the new method performs better than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this paper, we aim at super-resolving a low-resolution texture under the assumption that a high-resolution patch of the texture is available. To do so, we propose a variational method that combines two approaches that are texture synthesis and image reconstruction. The resulting objective function holds a nonconvex energy that involves a quadratic distance to the low-resolution image, a histogram-based distance to the high-resolution patch, and a nonlocal regularization that links the missing pixels with the patch pixels. As for the histogram-based measure, we use a sum of Wasserstein distances between the histograms of some linear transformations of the textures. The resulting optimization problem is efficiently solved with a primal-dual proximal method. Experiments show that our method leads to a significant improvement, both visually and numerically, with respect to the state-of-the-art algorithms for solving similar problems. [ABSTRACT FROM AUTHOR]

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods. [ABSTRACT FROM PUBLISHER]

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available. [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]

Natural images exhibit a high degree of complexity, randomness and irregularity in color and texture, however fractal can be an effective tool to describe various irregular phenomena in nature. Fractal dimensions are important because they can be defined in connection with real-world data, and they can be measured approximately by means of experiments. In this paper, we proposed a fractal dimension estimation method for RGB color images. In the proposed method, we present a hyper-surface partition method which considers the hyper-surface as continuous and divide the image into nonoverlapped blocks. We also defined a counting method in color domain. To validate the proposed method, experiments were carried on two types of color images: synthesized fractal images and natural RGB color images. The experimental results demonstrate that the proposed method is effective and efficient. The behaviors of the proposed method on the rescale images are also shown in the paper. And it can be performed as a reliable FD estimation approach for the RGB color images. [ABSTRACT FROM AUTHOR]

In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants. [ABSTRACT FROM PUBLISHER]

Tangles are a form of structured pen-and-ink 2D art characterized by repeating, recursive patterns. We present a method to procedu-rally generate tangle drawings, seen as recursively split sets of arbitrary 2D polygons with holes, with anisotropic and non-stationary features. We formally model tangles with group grammars, an extension of set grammars, that explicitly handles the grouping of shapes necessary to represent tangle repetitions. We introduce a small set of expressive geometric and grouping operators, showing that they can respectively express complex tangles patterns and sub-pattern distributions, with relatively simple grammars. We also show how users can control tangle generation in an interactive and intuitive way. Throughout the paper, we show how group grammars can, in few tens of seconds, produce a wide variety of patterns that would take artists hours of tedious and time-consuming work. We then validated both the quality of the generated tangles and the efficiency of the control provided to the users with a user study, run with both expert and non-expert users. [ABSTRACT FROM AUTHOR]

Wood is an important decorative material prized for its unique appearance. It is commonly rendered using artistically authored 2D color and bump textures, which reproduces color patterns on flat surfaces well. But the dramatic anisotropic specular figure caused by wood fibers, common in curly maple and other species, is harder to achieve. While suitable BRDF models exist, the texture parameter maps for these wood BRDFs are difficult to author -- good results have been shown with elaborate measurements for small flat samples, but these models are not much used in practice. Furthermore, mapping 2D image textures onto 3D objects leads to distortion and inconsistencies. Procedural volumetric textures solve these geometric problems, but existing methods produce much lower quality than image textures. This paper aims to bring the best of all these techniques together: we present a comprehensive volumetric simulation of wood appearance, including growth rings, color variation, pores, rays, and growth distortions. The fiber directions required for anisotropic specular figure follow naturally from the distortions. Our results rival the quality of textures based on photographs, but with the consistency and convenience of a volumetric model. Our model is modular, with components that are intuitive to control, fast to compute, and require minimal storage. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient computer-aided mass classification method in digitized mammograms using Association rule mining, which performs benign-malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Association rule mining (ARM) exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Correlated association rule mining was proposed for classifying the marked regions into benign and malignant and 98.6% sensitivity and 97.4% specificity is achieved that is very much promising compare to the radiologist's sensitivity 75%. [ABSTRACT FROM AUTHOR]

Point-based matching methods usually hold limitation in dealing with low texture scenes. In this paper, a robust affine-invariant lines matching method is proposed. The method commences with line segments extraction. All the extracted line segments are grouped into salient lines and general lines. Accordingly, the matching procedure includes salient lines matching and general lines matching. In salient lines matching, affine invariants are calculated and the matched salient line correspondences are the basis of the general lines matching. Each general line is clustered into a matched salient line according to a certain rule. Taking each salient line as the root, together with all the general lines clustered to it, a control network is constructed. Finally, the general lines matching procedure is performed between the two subnetworks whose roots are correspondences. Experimental results show that our proposed method can successfully process local distortion and improve the matching performance in low texture areas. [ABSTRACT FROM AUTHOR]

Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods. [ABSTRACT FROM PUBLISHER]

The modern trend of diversification and personalization has encouraged people to boldly express their differentiation and uniqueness in many aspects, and one of the noticeable evidences is the wide variety of hairstyles that we could observe today. Given the needs for hairstyle customization, approaches or systems, ranging from 2D to 3D, or from automatic to manual, have been proposed or developed to digitally facilitate the choice of hairstyles. However, nearly all existing approaches suffer from providing realistic hairstyle synthesis results. By assuming the inputs to be 2D photos, the vividness of a hairstyle re-synthesis result relies heavily on the removal of the original hairstyle, because the co-existence of the original hairstyle and the newly re-synthesized hairstyle may lead to serious artifact on human perception. We resolve this issue by extending the active shape model to more precisely extract the entire facial contour, which can then be used to trim away the hair from the input photo. After hair removal, the facial skin of the revealed forehead needs to be recovered. Since the skin texture is non-stationary and there is little information left, the traditional texture synthesis and image inpainting approaches do not fit to solve this problem. Our proposed method yields a more desired facial skin patch by first interpolating a base skin patch, and followed by a non-stationary texture synthesis. In this paper, we also would like to reduce the user assistance during such a process as much as possible. We have devised a new and friendly facial contour and hairstyle adjusting mechanism that make it extremely easy to manipulate and fit a desired hairstyle onto a face. In addition, our system is also equipped with the functionality of extracting the hairstyle from a given photo, which makes our work more complete. Moreover, by extracting the face from the input photo, our system allows users to exchange faces as well. In the end of this paper, our re-synthesized results are shown, comparisons are made, and user studies are conducted as well to further demonstrate the usefulness of our system. [ABSTRACT FROM AUTHOR]

The scope of this research is to evaluate three different haptic feedback methods for texture discrimination in virtual environments. In particular, a Phantom force feedback device, a custom-made vibrotactile dataglove and paper palpable prototypes have been used. This paper describes a new study which corroborates the results of an initial experiment (Martínez et al. in 2011 International Conference on Cyberworlds, pp. 62-68, ) and performs a more in-depth evaluation of some results of interest and, in particular, those based on gender. In the experiment expansion, the number of users has been increased, so both genders are even, and the texture identification strategies have been analyzed. Finally, statistical analyses have been conducted to assess the differences between both genders, showing a new path which could be explored with new experiments. In addition, the vibrotactile dataglove has proved to have a notable behavior in the detection of varying grating textures, and it is even useful to identify shapes. [ABSTRACT FROM AUTHOR]

In this paper, a novel rate control scheme with optimized bits allocation for the 3-D video coding is proposed. First, we investigate the R-D characteristics of the texture and depth map of the coded view, as well as the quality dependency between the virtual view and the coded view. Second, an optimal bit allocation scheme is developed to allocate target bits for both the texture and depth maps of different views. Meanwhile, a simplified model parameter estimation scheme is adopted to speed up the coding process. Finally, the experimental results on various 3-D video sequences demonstrate that the proposed algorithm achieves excellent R-D efficiency and bit rate accuracy compared to benchmark algorithms. [ABSTRACT FROM PUBLISHER]

The purpose of the present paper is to determine the best choices for color segmentation techniques as basis for skin detection and to review the main approaches in subsequent texture segmentation. The main goal is to determine which color space offers the best clustering for the skin color space and therefore a compared analysis of the most popular color spaces used in the literature is conducted. The results are statistically processed in order to offer the possibility of signficant conclusions to be drawn. However, the color segmentation alone does not provide eficiently accurate results when it comes to skin detection so it needs to be followed by eficient texture segmentation that would reduce false positives. [ABSTRACT FROM AUTHOR]

This paper introduces an automatic color design method that is driven by an importance function of the objects within a volumetric dataset. Our method allows the user to intuitively modify the object classification and the importance distribution function in the 2D rendered image. It automatically computes the transfer function, especially the color distribution, to convey the importance of the objects. In our approach, the importance of an object is represented as the attentiveness of a color. In addition, we preserve the color harmony in the rendered image in order to provide a visually pleasing result. In this paper, we propose a set of computational measurements to compute the color attentiveness and color harmony. Our color assignment algorithm supports arbitrary-dimensional transfer functions and obtains interactive frame rates. Our method involves three color spaces, namely Coloroid system, CIE LChuv, and Adobe RGB color space. It calculates the color attentiveness in CIE LChuv space, and the color harmony in Coloroid system. It, then, assigns the transfer function in a dual space of Adobe RGB space and renders the resulting image in Adobe RGB space. We conducted a detailed user study, which proves that our method successfully conveys the importance distributions. Our contribution in this paper is not only our importance driven approach, but also our computational measurements and our color assignment algorithm. [ABSTRACT FROM AUTHOR]

Background model updating is a vital process for any background subtraction technique. This paper presents an updating mechanism that can be applied efficiently to any background subtraction technique. This updating mechanism exploits the color and spatial features to characterize each detected object. Spatial and color features are used to classify each detected object as a moving background object, a ghost, or a real moving object. The starting position of each detected object is the cue for updating background images. In addition, this paper presents a hybrid scheme to detect and remove cast shadows based on texture and color features. The robustness of the proposed method and its effectiveness in overcoming challenging problems such as gradual and sudden illumination changes, ghost appearance, non-stationary background objects, the stability of moving objects most of the time, and cast shadows are verified quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach – The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings – The most important finding resulting from this study is that although the LBP/C and the multi-channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications – Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value – This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi-channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases. [ABSTRACT FROM AUTHOR]

This paper proposes an effective scale- and rotation-invariant local binary pattern (LBP) feature for texture classification. A circular neighboring set of an image pixel is defined as a scale-adaptive texton by taking into account the fundamental local structure property of the pixel. The scale space of a texture image is derived by the Laplacian of the Gaussian and then employed to determine the optimal scale of each pixel reflecting the characteristic length of the corresponding structure and determining the radius of the scale-adaptive texton. Different pixels have different optimal scales, resulting in the scale invariance. Contrary to the traditional LBP features that usually ignore global spatial information, the proposed method also defines subuniform patterns of each uniform pattern to improve the discrimination. For each uniform pattern, the subuniform pattern with the maximum statistical value is defined as the dominant orientation subuniform pattern. It is moved to the first column, and the others are circularly shifted. Experimental results demonstrate a good discrimination capability of the proposed scale- and rotation-invariant LBP in texture classification. Particularly, the LBP based on the scale-adaptive texton is promising to be powerful for texture description and scale-invariant texture classification, and the circular shift subuniform LBP can further improve the performance in the rotation-invariant texture classification. [ABSTRACT FROM AUTHOR]

Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions. [ABSTRACT FROM AUTHOR]

Aims: Texture analysis (TA) is becoming an increasingly used tool in radiological research. Some papers have been published on its use in ultrasound (US), but the way in which the machine settings affect the features has not yet been fully explored. With this research, we analyze how the time gain compensation (TGC) influences the features of the gray-level matrices in the abdominal US setting. Subjects and Methods: We analyzed the images acquired from the hepatorenal acoustic window of a healthy 29-year-old volunteer acquired with different TGC settings. TA was carried out using the LifeX software. Results: Several both 1st and 2nd order gray-level matrices features showed a strong correlation with TGC settings. Conclusions: TGC settings must be accounted for when carrying out further TA studies. [ABSTRACT FROM AUTHOR]

Nanopillar arrays structure deployed over a top photodetector surface exhibits high light coupling efficiency which is attributed to their unique optical and structural properties. The paper presents a proposed GaAs material based periodic pyramidal cut nanopillar structure array model deployed over a 50 µm × 50 µm detector's surface area. From the geometric analysis of the proposed model structure a maximum 99.8% light absorption has been achieved. Enhanced detector's performance in terms of 0.75 A/W responsivity, as well as 56.8% external quantum efficiency for a narrow 0.5 µm depletion width, has also been demonstrated. [ABSTRACT FROM AUTHOR]

Background: BRCA1-associated protein 1 (BAP1) mutation is an unfavorable factor for overall survival in patients with clear cell renal cell carcinoma (ccRCC). Radiomics literature about BAP1 mutation lacks papers that consider the reliability of texture features in their workflow.Purpose: Using texture features with a high inter-observer agreement, we aimed to develop and internally validate a machine learning-based radiomic model for predicting the BAP1 mutation status of ccRCCs.Material and Methods: For this retrospective study, 65 ccRCCs were included from a public database. Texture features were extracted from unenhanced computed tomography (CT) images, using two-dimensional manual segmentation. Dimension reduction was done in three steps: (i) inter-observer agreement analysis; (ii) collinearity analysis; and (iii) feature selection. The machine learning classifier was random forest. The model was validated using 10-fold nested cross-validation. The reference standard was the BAP1 mutation status.Results: Out of 744 features, 468 had an excellent inter-observer agreement. After the collinearity analysis, the number of features decreased to 17. Finally, the wrapper-based algorithm selected six features. Using selected features, the random forest correctly classified 84.6% of the labelled slices regarding BAP1 mutation status with an area under the receiver operating characteristic curve of 0.897. For predicting ccRCCs with BAP1 mutation, the sensitivity, specificity, and precision were 90.4%, 78.8%, and 81%, respectively. For predicting ccRCCs without BAP1 mutation, the sensitivity, specificity, and precision were 78.8%, 90.4%, and 89.1%, respectively.Conclusion: Machine learning-based unenhanced CT texture analysis might be a potential method for predicting the BAP1 mutation status of ccRCCs. [ABSTRACT FROM AUTHOR]

Brain tumor is one of the major causes of death among other types of the cancer because brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper, an efficient brain tumor detection system is proposed using combining optimal wavelet statistical texture features and recurrent neural network (RNN). The proposed system consists of four phases namely; feature extraction feature selection, classification and segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that, texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on oppositional gravitational search algorithm (OGSA). Then, selected features are given to the Recurrent Neural Network (RNN) classifier to classify an image as normal or abnormal. After the classification process, abnormal images are given to the segmentation stage to segment the ROI region with the help of modified region growing algorithm (MRG). The performance of the proposed methodology is analyzed in terms of different metrics and experimental results are compared with existing methods. [ABSTRACT FROM AUTHOR]

Image deblurring is a fundamental problem in imaging field which often needs to recover the important structure of images. This paper addresses the image deblurring problem by considering an image as a combination of its cartoon (the piecewise smooth part of the image) and texture (the oscillation part of the image) components. To recover both of these parts, we propose the use of coupled analysis-based sparse representations to regularize the cartoon structure and the texture part of the image. We apply anisotropic total variation with a quadratic term to enhance the edges existing in the cartoon part. Furthermore, we develop a multivariable Bregman optimization method to solve the proposed image restoration model by combining the alternating minimization method and the split Bregman iteration. The experiments show that the proposed algorithm not only performs well for image decomposition, but also outperforms the previously established methods in terms of the visual residual error, the structure similarity index and the peak signal-to-noise ratio for image deblurring. [ABSTRACT FROM AUTHOR]

In the last decades, iris features have been widely used in biometric systems. Because iris features are virtually unique for each person, their usage is highly reliable. However, biometric systems based on iris features are not completely fraud-resistant, as most systems use static images and do not distinguish between a live iris and a photograph. The iris structure and texture change with light variations, and traditional techniques for iris recognition always identify the iris texture in a controlled environment. However, in uncontrolled environments, live irises are recognized by their dynamic response to light: If the light changes, the pupils dilate or contract, and their texture dynamically changes. If a biometric system can identify people during the constriction or dilation time interval, that system will be more fraud-resistant. This paper proposes a new methodology to evaluate the "dynamic texture" from iris image sequences (motion analysis) and measure the discriminant power of these features for biometric system applications. We propose two new dynamic descriptors—dynamic local mapped pattern and dynamic sampled local mapped pattern—which are extensions of the local mapped pattern previously published for texture classification. We applied our proposed dynamic texture descriptors in a sequence of iris images segmented from video under light variation. Then, we compared our results with the well-known dynamic texture descriptor local binary pattern from three orthogonal planes (LBP-TOP). We used statistical measures to evaluate the performance of both descriptors and concluded that our methodology performed better than the LBP-TOP. Moreover, our descriptors can extract dynamic textures faster than the LBP-TOP. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of damaged buildings after an earthquake is critical for emergency response. As roof textures of damaged buildings are changed, damaged buildings may be accurately detected by the texture heterogeneity among buildings. However, existing researches have scarcely detected damaged buildings quantitatively by the texture heterogeneity among buildings from post-earthquake images. Therefore, this paper proposes an approach for the detection of earthquake-damaged buildings using texture feature contribution from post-earthquake images. In the proposed approach, a texture feature contribution index (TFCI) is defined by the information gain for selection of the optimal texture features, and a building texture damage index (TFdam) is defined for detection of damaged buildings. The proposed approach primarily comprises the following three steps: 1) building image objects are obtained using a pre-earthquake vector map to segment post-earthquake images; 2) the optimal texture features are selected by the TFCI to establish the texture feature vector; 3) damaged buildings are detected using the TFdam. The town of Leigu in Sichuan Province was selected as the study area to validate the performance of the proposed approach. The accuracy of 93.45% and kappa coefficient of 0.869 were achieved. Furthermore, the results indicate that the proposed approach can accurately detect earthquake-damaged buildings. [ABSTRACT FROM AUTHOR]

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, a framework for multiscale and multifeature normalized cut (MMNCut) segmentation is proposed for high spatial resolution (HSR) remote sensing images. Normalized cuts (NCuts), as a widely used segmentation method for natural images, can obtain a globally optimized segmentation result corresponding to the optimized partitions of a graph. However, it is difficult to apply the traditional NCuts directly to HSR images because of the huge computational complexity and the diversity of the characteristics of the land covers. In order to solve these problems, the proposed MMNCuts builds a multiscale graph based on superpixels, which can provide powerful grouping cues to guide the segmentation. Generated by different algorithms with varying parameters, superpixels can capture diverse and multiscale visual patterns of HSR images. In addition, the newly constructed graph integrates the multiscale information by considering various connection relationships. Meanwhile, the successful integration of the multifeature cues, including the spectral information, texture information, and structure information, from a large number of superpixels, helps to enhance the expression ability of the graph. Computationally, this leads to a much more efficient algorithm than the traditional NCuts, and in effect, the proposed method achieves a significantly better performance than the traditional approaches. The experimental results with three HSR image data sets demonstrate that the proposed MMNCut algorithm shows a competitive performance in both qualitative and quantitative evaluations when compared with the other state-of-the-art segmentation algorithms for HSR images. [ABSTRACT FROM AUTHOR]

Background: The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.Main Text: The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.Conclusions: The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research. [ABSTRACT FROM AUTHOR]

Lung sounds have information to seek abnormalities in the lung. With digital signal processing, the information in the lung sounds is extracted as the features in lung sound classification. In this paper, texture analysis was used to measure the complexity of lung sound as a feature in lung sound classification. Grey-Level Difference (GLD) method was performed on lung sounds with a number of different scales. Multi-scale GLD has produced accuracy up to 90.12% for five classes of data. Further, gradient entropy individually provided the highest accuracy up to 91.36% for the distance D = 20 and a scale of 1-10. [ABSTRACT FROM AUTHOR]

This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems. [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]

The state-of-the-art High Efficiency Video Coding (HEVC) standard adopts a hierarchical coding structure to improve its coding efficiency. This allows for the quantization parameter cascading (QPC) scheme that assigns quantization parameters (Qps) to different hierarchical layers in order to further improve the rate-distortion (RD) performance. However, only static QPC schemes have been suggested in HEVC test model, which are unable to fully explore the potentials of QPC. In this paper, we propose an adaptive QPC scheme for an HEVC hierarchical structure to code natural video sequences characterized by diversified textures, motions, and encoder configurations. We formulate the adaptive QPC scheme as a non-linear programming problem and solve it in a scientifically sound way with a manageable low computational overhead. The proposed model addresses a generic Qp assignment problem of video coding. Therefore, it also applies to group-of-picture-level, frame-level and coding unit-level Qp assignments. Comprehensive experiments have demonstrated that the proposed QPC scheme is able to adapt quickly to different video contents and coding configurations while achieving noticeable RD performance enhancement over all static and adaptive QPC schemes under comparison as well as HEVC default frame-level rate control. We have also made valuable observations on the distributions of adaptive QPC sets in the videos of different types of contents, which provide useful insights on how to further improve static QPC schemes. [ABSTRACT FROM PUBLISHER]

Speckle noise greatly limits both synthetic aperture radar (SAR) data human readability, especially for non-SAR-expert users, and performance of automatic processing and information retrieval procedures by computer programs. Therefore, despeckling of SAR images is an essential preprocessing step in SAR data analysis, processing, and modeling, as well as in information retrieval and inversion procedures. Up to now, one of the most accurate and promising despeckling approaches—among those based on a single SAR image—is the one relying on the nonlocal means concepts. However, at the best of our knowledge, most of the state of the art considers the despeckling problem only within a statistical framework, completely discarding the electromagnetic phenomena behind SAR imagery formation. In this paper, we introduce the novel idea of a physical-based despeckling, taking into account meaningful physical characteristics of the imaged scenes. This idea is realized via the implementation of a physical-oriented probabilistic patch-based (PPB) filter based on a priori knowledge of the underlying topography and analytical scattering models. This filter is suitable for SAR images of natural scenes presenting a significant topography. An adaptive version of the proposed scattering-based PPB filter for denoising of SAR images including both mountainous and flat areas is also developed. The performances of the proposed filter and its adaptive version are evaluated both qualitatively and quantitatively in numerical experiments using both simulated and actual SAR images. The proposed technique exhibits performance superior w.r.t. the standard PPB filter and comparable or, in some cases, superior to the state of the art, both in terms of speckle reduction and texture and detail preservation. [ABSTRACT FROM AUTHOR]

Subpixel mapping, which is a promising technique based on the assumption of spatial dependence, enhances the spatial resolution of images by dividing a mixed pixel into several subpixels and assigning each subpixel to a single land-cover class. The traditional subpixel mapping methods usually utilize the fractional abundance images obtained by a spectral unmixing technique as input and consider the spatial correlation information among pixels and subpixels. However, most of these algorithms treat subpixels separately and locally while ignoring the rationality of global patterns. In this paper, a novel subpixel mapping model based on sparse representation theory, namely, adaptive sparse subpixel mapping with a total variation model (ASSM-TV), is proposed to explore the possible spatial distribution patterns of subpixels by considering these subpixels as an integral patch. In this way, the proposed method can obtain the optimal subpixel mapping result by determining the most appropriate subpixel spatial pattern. However, the number of possible spatial configurations of subpixels can increase sharply with large-scale factors, and therefore, in ASSM-TV, the subpixel mapping is considered as a sparse representation problem. A preconstructed discrete cosine transform dictionary, which consists of piecewise smooth subpixel patches and textured patches, is utilized to express the original subpixel mapping observation in a sparse representation pattern. The total variation prior model is designed as a spatial regularization constraint to characterize the relationship between a subpixel and its neighboring subpixels. In addition, a joint maximum a posteriori model is proposed to adaptively select the regularization parameters. Compared with the other traditional and state-of-the-art subpixel mapping approaches, the experimental results using a simulated image, three synthetic hyperspectral remote sensing images, and two real remote sensing images demonstrate that the proposed algorithm can obtain better results, in both visual and quantitative evaluations. [ABSTRACT FROM PUBLISHER]

Oil-impregnated Kraft paper samples normally used for power transformer electrical insulation have been artificially aged thermally to produce a sample set with varying levels of insulation deterioration. The samples were aged in an oven at a temperature above the normal operating temperature of a power transformer. Digital images obtained from optical microscopy measurements on the paper samples have been analyzed using a texture analysis method which converts each image into a spatial gray level dependence matrix (SGLDM). The SGLDM contains information about the statistical variation of pixels gray-level intensities in an image and thereby information about the sample texture. Mathematical operators applied to the SGLDM have been used to extract 22 different statistical texture features for each sample image. Optical microscopy images of the thermally aged samples show that thermal deterioration of the insulation paper produces changes in morphology and physical structure. These changes are detectable by the statistical texture features extracted from the SGLDM texture analysis. Statistical classification is performed on the feature set to demonstrate that differentiation between oil-impregnated paper samples with different levels of thermal degradation is reliable with low error rates. Therefore, development of a practical method to assess condition of oil-impregnated paper insulation using optical microscopy and texture analysis is promising. [ABSTRACT FROM AUTHOR]

This paper presents a new diffusion coefficient which is based on second order derivative and local entropy information for image denoising. In the proposed model, a second order derivative term is introduced, which reduces the staircasing effect and preserves edge in a processed image. The local entropy information can preserve texture. The Perona-Malik model with a new diffusion coefficient improves the denoised effects, and prevents edges from being over-smoothed. Comparative experiments show that the proposed model obtains more satisfied results than the other two existing models. [ABSTRACT FROM AUTHOR]

This paper proposes a block-based selection random forest (BBSRF) for texture classification task using multi-fractal spectrum (MFS) feature descriptor. The random feature selection method for node splitting in random forest may omit some features which would be informative and critical to represent the instances. The BBSRF ensures that each feature would be considered via the block-based selection strategy. In BBSRF, all features are divided into $$k$$ blocks; next, we generate synthesis feature subset which is made up of all features in one block and $$m$$ random features from the remaining $$(k-1)$$ blocks; finally, each node splitting of the random tree is operated on one synthesis feature subset. After all blocks have been searched, all features are re-divided into new $$k$$ blocks. The above process works iteratively until the satisfactory result is obtained. Once the random trees have been built, a testing instance is classified by voting from them. We conducted the experiments on five texture benchmark datasets with the help of MFS feature. Experimental results demonstrate the excellent performance of the proposed method in comparison with state-of-the-art results on these datasets. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) needs computerized method to reduce the review time for its large image data. In this paper, we propose an improved bag of feature (BoF) method to assist classification of polyps in WCE images. Instead of utilizing a single scale-invariant feature transform (SIFT) feature in the traditional BoF method, we extract different textural features from the neighborhoods of the key points and integrate them together as synthetic descriptors to carry out classification tasks. Specifically, we study influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Comprehensive experimental results reveal that the best classification performance is obtained with the integrated feature strategy using the SIFT and the complete local binary pattern (CLBP) feature, the visual words with a length of 120, the patch size of 8*8, and the support vector machine (SVM). The achieved classification accuracy reaches 93.2%, confirming that the proposed scheme is promising for classification of polyps in WCE images. [ABSTRACT FROM AUTHOR]

Diminished reality aims to remove real objects from video images and fill in the missing regions with plausible background textures in real time. Most conventional methods based on image inpainting achieve diminished reality by assuming that the background around a target object is almost planar. This paper proposes a new diminished reality method that considers background geometries with less constraints than the conventional ones. In this study, we approximate the background geometry by combining local planes, and improve the quality of image inpainting by correcting the perspective distortion of texture and limiting the search area for finding similar textures as exemplars. The temporal coherence of texture is preserved using the geometries and camera pose estimated by visual-simultaneous localization and mapping (SLAM). The mask region that includes a target object is robustly set in each frame by projecting a 3D region, rather than tracking the object in 2D image space. The effectiveness of the proposed method is successfully demonstrated using several experimental environments. [ABSTRACT FROM PUBLISHER]

In this paper, we present an efficient level of detail algorithm for texture-based flow visualization. Our goal is to enhance visual perception and performance and generate smooth animation. To achieve our goal, we first model an adaptive input texture taking into account flow patterns to output view-dependent high-quality images. Then, we compute field lines only from sparse sampling points of the input noise texture for outputting volume line integral convolution textures and skip empty space utilizing two quantized binary histograms. To improve image quality, we implement anti-aliasing through adjusting the line integral convolution step size and thickness of trajectory lines with an opacity function. We further extend our solution to unsteady flow. Flow structures and evolution are clearly shown through smooth animation achieved with coherent evolution of particles, handling of discontinuous flow lines, and spatio-temporal linear constraint of the underlying noise volume. In the result section, we show high-quality level of detail of three-dimensional texture-based flow visualization with high performance. We also demonstrate that our algorithm can achieve smooth evolution for unsteady flow with spatio-temporal coherence. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]