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Ice storms can cause major power disruptions and detection of ice formation on power cables can avoid them by taking preventive actions such as removing the ice before a major problem occurs. In this paper, a computer vision solution was developed to detect ice on difficult imaging scenarios where illumination and variety of locations can change the image of the cable and background considerably. The methodology starts with edge detection and support vector regression was applied to predict the upper threshold for the Canny edge detector. We found that this improves the later classification accuracy. A total of 44 image features based on the gray level co-occurrence matrix, statistical features and Hough transform were extracted and then processed by principal component analysis and sequential forward selection to reduce the feature dimension. Final detection was performed using four different classifiers. It was found that eight features achieved optimal performance using a support vector machine. [ABSTRACT FROM AUTHOR]

The performance of machine learning (ML)-based path loss models relies heavily on the data they use at their inputs. Feature engineering is, therefore, essential for the model's success. In this letter, at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the segmentation-based fractal texture analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm, thus, delivers a new set of features, based on the image's texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, ML algorithms, and performance metrics. [ABSTRACT FROM AUTHOR]

Under long-term synergy effect of multi-factors, especially the thermal stress, insulating paper will be degraded and its insulation performance will decline due to carbonization and degradation of cellulose. This paper presents an optical approach for aging feature extraction of the insulating paper, where one of the image processing methods called texture analysis is utilized. By conducting laboratory accelerated thermal aging tests, insulating paper samples with different aging conditions for both Nomex and Kraft, evaluated with the aging time, are prepared. After taking optical microscopic images of insulating paper samples belong to different aging groups, up to 14 texture features are extracted using the gray-level co-occurrence matrix (GLCM). With different feature selection methods applied, several of them are finally selected to represent the aging condition of insulating. Numerical tests with both supervised and unsupervised algorithms, as well as a linear regression method verifies the validity of these features in characterizing the aging condition of the insulating paper. [ABSTRACT FROM PUBLISHER]

This paper presents a fully automatic method for the separation of diffuse and specular reflection components from a single image. Overall, the mechanisms in which the available methods operate on are computationally costly and do not translate well to modern hardware-implemented image processing pipelines, such as the ones present in consumer electronics. Consequently, the objective of this article is to introduce a simple yet effective method for specular highlight removal. It is based on the dichromatic reflection model and operates through histogram matching in the YCbCr color space. The proposed method performs in real-time. It only uses global image statistics and point-wise intensity transformations. Experimental evaluation shows that the proposed approach has competitive results in comparison to state-of-the-art methods. Limitations of the proposed approach are seldom and are common to most methods available. The proposed method, however, achieves better quality results with much less computational cost, thus enabling feasibility in systems with limited processing power.

We present a novel methodology to procedurally restore facade texture from images, where severe occlusions and lighting variations occur, making the textures difficult to reconstruct effectively. A refinement strategy is designed, which includes an iterative weighted-average algorithm, to restore and update the high-quality consensus texture of the targeted building. Our approach combines component-based structure analysis and rule-based texture recovery techniques that are suitable for removing the occluded areas from a given building facade. We demonstrate our framework on several real-world buildings with varying amounts of occlusion, and show that our approach can be used to generate 3-D buildings with far more aesthetic quality than the previous approaches.

In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback–Leibler divergences between the input and sample local energy histograms on all the wavelet subbands. It is demonstrated by various experiments that our proposed method obtains a satisfactory texture classification accuracy in comparison with several current state-of-the-art texture classification approaches. [ABSTRACT FROM PUBLISHER]

Characterizing spatial information as reinforcement of spectral signatures can largely assist the performance in hyperspectral image (HSI) classification. Markov random fields (MRFs) are probabilistic image texture models and are capable of encoding contextual dependencies through characterizing local conditional probabilities. As a representative standardized measure of dispersion of image probability distributions, the coefficient of variation (CoV) can be a useful tool for characterizing spatial heterogeneity. Their parameter derivation processes also share strong compatibility with convolutional neural networks (CNNs) that specify spatial correlations in local neighborhoods. In this work, we propose an MRF and CoV-based spectral–spatial convolutional network (MRF-CoV-CNN) for HSI classification. MRF models and CoVs are characterized as measures of spatial distributions and further combined with spectral information. Then the proposed MRF-CoV-CNN takes the fused features as input and produces reliable classification results. Comprehensive experiments have been conducted on the Pavia University dataset and the Salinas dataset to evaluate the proposed method both visually and quantitatively. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar (SAS) allows for high-resolution imagery of the seafloor with a quality that approaches that of an optical camera. Identifying seabed types within SAS images can prove very useful in a setting where autonomous underwater vehicles (AUVs) are used, since information about the bottom type can be used to determine the behavior of the AUV. In this article, we present a new algorithm for the automatic detection of different seabed types based on SAS image texture. Using a Gaussian process classification (GPC) model, we were able to predict the seabed type, as well as an uncertainty estimate on this prediction. The GPC-based model is able to learn the most informative textures from a training data set, and by construction is robust against outliers or data points that are very different from those in the training set. An additional benefit of the model is that it can be easily configured to hit the desired tradeoff between computational complexity and accuracy. [ABSTRACT FROM AUTHOR]

Nonrigid image registration plays an important role in the field of computer vision and medical application. The methods based on Demons algorithm for image registration usually use intensity difference as similarity criteria. However, intensity based methods can not preserve image texture details well and are limited by local minima. In order to solve these problems, we propose a Gabor feature based LogDemons registration method in this article, called GFDemons. We extract Gabor features of the registered images to construct feature similarity metric since Gabor filters are suitable to extract image texture information. Furthermore, because of the weak gradients in some image regions, the update fields are too small to transform the moving image to the fixed image correctly. In order to compensate this deficiency, we propose an inertial constraint strategy based on GFDemons, named IGFDemons, using the previous update fields to provide guided information for the current update field. The inertial constraint strategy can further improve the performance of the proposed method in terms of accuracy and convergence. We conduct experiments on three different types of images and the results demonstrate that the proposed methods achieve better performance than some popular methods. [ABSTRACT FROM AUTHOR]

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective blind quality assessment method for screen content images (SCIs), called perceptual quality measure by spatial continuity (PQSC). With the center-surround mechanism in the human visual system (HVS), the proposed method extracts the statistical features on chromatic and textural variations in SCIs to measure the visual distortion. First, by considering the chromatic continuity between spatially adjacent pixels, photo-metric invariant chromatic descriptors are extracted as zero-order and first-order features. Second, motivated by the perceptual mechanism that the HVS is sensitive to image texture variation, we employ local ternary pattern operator to effectively depict the spatial continuity of texture. With these extracted chromatic and textural features, we further adopt histogram to compute the statistical chromatic and textural features. Support vector regression (SVR) is used to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate that the performance of our method is superior to the current blind image quality assessment methods, even better than some full reference image quality assessment counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we aim to handle the intraclass variation resulting from the geometric transformation and the illumination change for more robust texture classification. To this end, we propose a novel feature descriptor called Radon representation-based feature descriptor (RRFD). RRFD converts the original pixel represented images into Radon-pixel images by using the Radon transform. The new Radon-pixel representation is more informative in geometry and has a much lower dimension. Subsequently, RRFD efficiently achieves affine invariance by projecting an image (or an image patch) from the space of Radon-pixel pairs onto an invariant feature space by using a ratiogram, i.e., the histogram of ratios between the areas of triangle pairs. The illumination invariance is also achieved by defining an illumination invariant distance metric on the invariant feature space. Comparing to the existing Radon transform-based texture features, which only achieve rotation and/or scaling invariance, RRFD achieves affine invariance. The experimental results on CUReT show that RRFD is a powerful feature descriptor that is suitable for texture classification. [ABSTRACT FROM AUTHOR]

The coherent imaging method of synthetic aperture radar (SAR) brings SAR images with strong and randomly distributed speckle, which causes great interference to subsequent applications. To deal with the affected images, we propose a multiconnection network incorporating wavelet features (MCN-WF) to despeckle the images and then evaluate the results. On the one hand, simplified dense connections in and among Dense Blocks (DBs) utilize the features extracted from the network at different scales to produce despeckled images with more details. On the other hand, performing feature pre-extraction on images by wavelet transform can not only indirectly control the convergence direction of the network by modifying the loss function but also reduce the size of the feature maps to accelerate the speed of the network processing. The experimental results show that the new method has a better performance in terms of despecking, image texture structure preservation, and processing efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of deep correlation features on image style classification. Inspired by that, correlation between feature maps can effectively describe image texture, and we design various correlations and transform them into style vectors, and investigate classification performance brought by different variants. In addition to intralayer correlation, interlayer correlation is proposed as well, and its effectiveness is verified. After showing the effectiveness of deep correlation features, we further propose a learning framework to automatically learn correlations between feature maps. Through extensive experiments on image style classification and artist classification, we demonstrate that the proposed learnt deep correlation features outperform several variants of convolutional neural network features by a large margin, and achieve the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning task, including multi-way classification. Sparse sampling, on the other hand, provides a highly efficient technique to reconstruct signals in the spatial-temporal domain from just a few carefully-chosen samples. Here, we present a new method, referred to as the Sparse-TDA algorithm, that combines favorable aspects of the two techniques. This combination is realized by selecting an optimal set of sparse pixel samples from the persistent features generated by a vector-based TDA algorithm. These sparse samples are selected from a low-rank matrix representation of persistent features using QR pivoting. We show that the Sparse-TDA method demonstrates promising performance on three benchmark problems related to human posture recognition and image texture classification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods. [ABSTRACT FROM PUBLISHER]

Image feature extraction and similarity measure in feature space are active research topics. They are basic components in a content-based image retrieval (CBIR) system. In this letter, we present a new statistical model-based image feature extraction method in the wavelet domain and a novel Kullback divergence-based similarity measure. First, a Gaussian mixture model (GMM) and a more systematic generalized Gaussian mixture model (GGMM) are employed to describe the statistical characteristics of the wavelet coefficients and the model parameters are employed to construct a compact image feature space. A nontrivial expectation-maximization (EM) algorithm for the GGMM model is derived. Subsequently, a new Kullback divergence-based similarity measure with lowcomputation cost is derived and analyzed. The Brodatz texture image database and some other image databases are used to evaluate the retrieval performance based on the presented new methods. Experimental results indicate that the GMM and the GGMM-based image texture features are very effective in representing multiscale image characteristics and that the new methods outperforms other conventional wavelet-based methods in retrieval performance with a comparable level of computational complexity. It is also demonstrated that for image features extracted by the new statistical models, the similarity measure based on Kullback divergence is more effective than conventional similarity measures. [ABSTRACT FROM AUTHOR]

In this letter, we propose to use an enhanced version of volumetric directional pattern to efficiently extract rich spatial context information in the hyperspectral imagery (HSI). The proposed technique fuses the texture information from three consecutive bands in the input HSI. The extracted local image texture features for each pixel of interest are then fed into an extreme learning machine classifier to assign object category. The experimental results on three standard hyperspectral data sets demonstrate the effectiveness of the proposed method for HSI classification compared with that of a set of state-of-the-art spatial extraction methods. [ABSTRACT FROM PUBLISHER]

The widely used energy features extracted from the wavelet domain can effectively represent the common image textures. However, they are not robust to the rotated textures. In this letter, we propose a multiscale rotation-invariant representation (MRIR) of textures by using multiscale sampling. Particularly, a multiscale wavelet transform is used to decompose the magnitude pattern (MP) mapping of a texture. Furthermore, the sign pattern (SP) mapping of a texture is used as a step function, which is further sampled and used to fit the wavelet subbands of the MP mapping for computing the sampled directional mean vectors (SDMVs) of the subbands. Moreover, we construct frequency vectors (FVs) of those SP mappings for capturing the structural information of textures. Finally, we can obtain the MRIR vector of an image texture by concatenating those SDMVs and FVs for texture classification. The comprehensive experimental results demonstrate that our proposed approach outperforms six representative texture classification methods. [ABSTRACT FROM PUBLISHER]

Single-image super-resolution (SISR) techniques have found wide applications in semiconductor defect inspection. Enhancing image resolution to improve inspection sensitivity and accuracy holds great significance. A novel SISR algorithm, called cross-convolutional residual network (CCRN), is proposed in this study. CCRN comprises a cross-convolutional module (CCM), which incorporates a cross-sharing mechanism that facilitates the fusion of features from different stages, enabling the extraction of more information from the image. Moreover, a global residual aggregation structure (GRA) is introduced. GRA captures and transfers different levels of residual features acquired from learning each CCM to the reconstruction layer. Experimental results demonstrate that the proposed SR algorithm outperforms existing state-of-the-art SR algorithms in terms of both visual and quantitative metrics when applied to optical, SEM, and TEM images of microfluidic chips, CMOS image sensors, and quantum dots, respectively. Additionally, CCRN significantly improves the accuracy of defect classification and inspection of unpatterned wafers, as evaluated using the WM-811K dataset. Notably, an increase in local defection testing accuracy from 79.00% to 89.00% and an improvement in classification accuracy from 93.69% to 96.06% are achieved. These findings underscore the potential applications of the proposed algorithm in improving semiconductor defect inspection and classification accuracies. [ABSTRACT FROM AUTHOR]

Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples. [ABSTRACT FROM PUBLISHER]

Unsharp masking (USM) sharpening is a basic technique for image manipulation and editing. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. After USM sharpening, overshoot artifacts, which shape image texture, are generated along image edges. By utilizing the special characteristic of the texture modification caused by the USM sharpening, a novel method called edge perpendicular binary coding is proposed in this letter to detect USM sharpening. Extensive experiments have been conducted to show the superiority of the proposed method over the existing methods. [ABSTRACT FROM AUTHOR]