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The traditional image texture extraction algorithm, most of the lifting of the texture is a single scale and direction of the texture. When you change the scale and direction, the corresponding texture will change as well. This is the lack of the traditional texture extraction method. In this paper, aiming at the single direction of the traditional texture extraction, a novel image texture feature extraction algorithm based on Gabor filter and CS-LBP operator is proposed. The extracted frequency components are transformed to the time domain to achieve the texture extraction. Based on the principle of polarization, the curvature factor is introduced based on the traditional Gabor model, which solves the problem that the Gabor filter can’t extract the local image warp and distant scene blurred. The experimental results show that the proposed method uses the Gabor filter and the LBP operator to extract the polarized image texture effectively than the traditional image texture extraction algorithm. [ABSTRACT FROM AUTHOR]

Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features and the combinations of multiple algorithms, while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism and, therefore, can be used as a useful tool for both the structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the the University of Illinois at Urbana-Champaign texture database suggests the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval. [ABSTRACT FROM PUBLISHER]

Dense matching of remote sensing images is a key step in the generation of accurate digital surface models. The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency, so it is widely used in close-range, aerial and satellite image matching. Based on the analysis of the original semi-global matching algorithm, this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture. The method includes 4 parts; (T) Precise orientation. Aiming at the system error in the image orientation model, the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images, and the sub-pixel positioning accuracy is obtained; (2) Epipolar image generation. After the original image is divided into blocks, the epipolar image is generated based on the projection trajectory method; (3) Image dense matching. In order to reduce the size of the cost space and calculation time, the image is pyramided and then semi-globally matched layer by layer. In the matching process, the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects; @ Generate DSM. In order to test the matching effect, the weighted median filter algorithm is used to filter the disparity map, and the DSM is obtained based on the principle of forward intersection. Finally, the paper uses the matching results of WordView3 and Ziyuan No. 3 stereo image to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

A new bilateral frame rate upconversion algorithm using the image texture complexity characteristic is proposed. In the proposed algorithm, as the cost function is adjusted according to the image texture complexity, the accuracy of motion estimation (ME) is improved. In the experimental results, the proposed algorithm outperforms the conventional bilateral ME process by 0.32 dB. [ABSTRACT FROM AUTHOR]

Convolutional neural networks for single-image super-resolution have been widely used with great success. However, most of these methods use L1 loss to guide network optimization, resulting in blurry restored images with sharp edges smoothed. This is because L1 loss limits the optimization goal of the network to the statistical average of all solutions within the solution space of that task. To go beyond the L1 loss, this paper designs an image super-resolution algorithm based on second-order gradient loss. We impose additional constraints by considering the high-order gradient level of the image so that the network can focus on the recovery of fine details such as texture during the learning process. This helps to alleviate the problem of restored image texture over-smoothing to some extent. During network training, we extract the second-order gradient map of the generated image and the target image of the network by minimizing the distance between them, this guides the network to pay attention to the high-frequency detail information in the image and generate a high-resolution image with clearer edge and texture. Besides, the proposed loss function has good embeddability and can be easily integrated with existing image super-resolution networks. Experimental results show that the secondorder gradient loss can significantly improve both Learned Perceptual Image Patch Similarity (LPIPS) and Frechet Inception Distance score (FID) performance over other image super-resolution deep learning models. [ABSTRACT FROM AUTHOR]

The texture of ground-based nephogram is abundant and multiplicity. Many cloud textures are not as clear as artificial textures. A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image. Grunwald-Lentikov (G-L) and Grunwald-Lentikov (R-L) fractional differential operators are applied to the enhancement algorithm of ground-based nephogram. An operator mask based on adaptive differential order is designed. The corresponding mask template is used to process each pixel. The results show that this method can extract image texture and edge details and simplify the process of differential order selection. [ABSTRACT FROM AUTHOR]

The human visual system unconsciously determines the color of the objects by "discounting" the effects of the illumination, whereas machine vision systems have difficulty performing this task. Color constancy algorithms assist computer vision pipelines by removing the effects of the illuminant, which in the end enables these pipelines to perform better on high-level vision tasks based on the color features of the scene. Due to its benefits, numerous color constancy algorithms have been developed, and existing techniques have been improved. Combining different strategies and investigating new methods might help us design simple yet effective algorithms. Thereupon, we present a color constancy algorithm based on the outcomes of our previous works. Our algorithm is built upon the biological findings that the human visual system might be discounting the illuminant based on the highest luminance patches and space-average color. We find the illuminant estimate based on the idea that if the world is gray on average, the deviation of the brightest pixels from the achromatic value should be caused by the illuminant. Our approach utilizes multi-scale operations by only considering the salient pixels. It relies on varying surface orientations by adopting a block-based approach. We show that our strategy outperforms learning-free algorithms and provides competitive results compared to the learning-based methods. Moreover, we demonstrate that using parts of our strategy can significantly improve the performance of several learning-free methods. We also briefly present an approach to transform our global color constancy method into a multi-illuminant color constancy approach. [ABSTRACT FROM AUTHOR]

Image inpainting is a kind of method to restore and correct the missing information or remove the redundant interference information from the image. This method is of great significance in the research of the image restoration. And dealing with art pictures, computer graphics and image technology can be used to show the original style and appearance of the damaged pictures. This paper first summarizes the process which common algorithm is used to restore image information, then analyzes the shortcomings of these algorithms in the judgment of image texture and edge part and puts forward by the Criminisi algorithm which adding differential factors in the original algorithm and optimizes the priority model based on image texture and edge features. The proposed algorithm has obvious advantages in identification ability, and it also upgrades the search matching as well as overcomes the drawbacks of the original algorithm. Finally, the improved algorithm is tested, and the experimental results show that the algorithm successfully overcome the drawbacks of the classical algorithm, and better restore the picture. [ABSTRACT FROM AUTHOR]

Mapping hazelnut orchards can facilitate land planning and utilization policies, supporting the development of cooperative precision farming systems. The present work faces the detection of hazelnut crops using optical and radar remote sensing data. A comparative study of Machine Learning techniques is presented. The system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud tools. We provide a dataset of 62,982 labeled samples, with 16,561 samples belonging to the ‘hazelnut’ class and 46,421 samples belonging to the ‘other’ class, collected in 8 heterogeneous geographical areas of the Viterbo province. Two different comparative tests are conducted: firstly, we use a Nested 5-Fold Cross-Validation methodology to train, optimize, and compare different Machine Learning algorithms on a single area. In a second experiment, the algorithms were trained on one area and tested on the remaining seven geographical areas. The developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut mapping. From the results, it emerges that Random Forest is the classifier with the highest generalizability, achieving the best performance in the second test with an accuracy of 96% and an F1 score of 91% for the ‘hazelnut’ class. [ABSTRACT FROM AUTHOR]

In this work, bioengineering methods that can be used to quantitatively analyze videocapsule endoscopy images that have been acquired from celiac patients versus controls are described. For videocapsule endoscopic analysis, each patient swallows a capsule which contains an imaging device and light source. In celiac and control patients, images are acquired and analyzed at the level of the small intestine. The data used for videocapsule analysis consisted of high resolution images of dimension 576 × 576 pixels, acquired twice per second. The goal of the quantitative analysis is to detect abnormality in celiac patient images as compared with controls. Several types of abnormality can exist at the level of the small intestine in celiac patients. In untreated patients, and often even after treatment with a gluten-free diet, there can be villous atrophy, as well as presence of fissures and a mottled appearance. To detect and discern these abnormalities, several methods of statistical and structural feature extraction and selection are described. It was found that there is a significantly greater variation in image texture and average brightness level in celiac patients as compared with controls (p < 0.05). Celiac patients have a longer dominant period as compared with controls, averaging 6.4 ± 2.6 seconds versus 4.7 ± 1.6 seconds in controls (p = 0.001). This suggests that overall motility is slower in the celiac patients. Furthermore, the mean number of villous protrusions per image was found to be 402.2±15.0 in celiac patients versus 420.8±24.0 in control patients (p < 0.001). The average protrusion width was 14.66±1.04 pixels in celiacs versus 13.91±1.47 pixels in controls (p = 0.01). The mean protrusion height was 3.10±0.26 grayscale levels for celiacs versus 2.70±0.43 grayscale levels for controls (p < 0.001). Thus celiac patients tended to have fewer protrusions, and these were more varied in shape, tending to be blunted, as compared with controls, which more often had fine, uniform protrusions. A variety of computerized methods are now available to quantitate videocapsule images for comparison of celiac versus control patients. Since these methods are based on computer algorithms, they can be automated and there is no variation in the results due to observer bias. These methods readily lend themselves to automation, so that it may be possible to map the entire small intestine for presence of abnormality in real-time. It is also possible to develop an automated, quantitative clinical score which can be displayed with real-time update during the procedure. This would be useful to determine progress in celiac patients on a gluten-free diet, and to better understand the properties of the healing process in these patients. [ABSTRACT FROM AUTHOR]

Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information. [ABSTRACT FROM AUTHOR]

While image texture is effective for use in pattern recognition and image analysis algorithms, textural features are time-consuming to calculate on standard CPUs. Therefore, we present novel implementations of textural feature algorithms on graphics processors (GPUs), enabling fast color and texture analysis. Since different textural feature calculations can exhibit diverse characteristics, we focus on using general and algorithm-specific techniques to exploit the inherent parallelism and computational power of a GPU. Common operations required during the textural feature pipeline range from streaming computations to recursive procedures, from arithmetically intensive transcendental functions to matrix operations. Some of these kernels are well-suited to GPUs, while others require considerable programming effort to fully exploit the memory hierarchy due to their memory usage patterns. In this paper, different strategies for computing textural features on GPUs are compared with counterpart implementations on multicore CPUs, and experimental results show GPU results reaching 500× speedup for certain operations. [ABSTRACT FROM AUTHOR]

The dual-tree quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory for the 2-D Hilbert transform and can be computed using a dual-tree filter bank with linear computational complexity. To demonstrate the properties of the QWT's coherent magnitude/phase representation, we develop an efficient and accurate procedure for estimating the local geometrical structure of an image. We also develop a new multiscale algorithm for estimating the disparity between a pair of images that is promising for image registration and flow estimation applications. The algorithm features multiscale phase unwrapping, linear complexity, and sub-pixel estimation accuracy. [ABSTRACT FROM AUTHOR]

A prototype, content-based image retrieval system has been built employing a client/server architecture to access supercomputing power from the physician's desktop. The system retrieves images and their associated annotations from a networked microscopic pathology image database based on content similarity to user supplied query images. Similarity is evaluated based on four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric. Current retrieval accuracy varies across pathological categories depending on the number of available training samples and the effectiveness of the feature set. The distance measure of the search algorithm was validated by agglomerative cluster analysis in light of the medical domain knowledge. Results show a correlation between pathological significance and the image document distance value generated by the computer algorithm. This correlation agrees with observed visual similarity. This validation method has an advantage over traditional statistical evaluation methods when sample size is small and where domain knowledge is important. A multi-dimensional scaling analysis shows a low dimensionality nature of the embedded space for the current test set. [ABSTRACT FROM AUTHOR]

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected. [ABSTRACT FROM AUTHOR]

Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model's output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%. [ABSTRACT FROM AUTHOR]

In order to improve the design and development efficiency of colored spun knitted products, a computer simulation algorithm is proposed. Aiming at the problem that the low-frequency color information in colored spun knitted fabrics cannot be effectively separated by a single frequency filter, the approximate component of Dual-Tree Complex Wavelet is adopted to characterize, analyze and visualize the color information. Meanwhile, an image colorization algorithm is constructed by simulating the coloring law of two-component dyed fibers on the fabric surface, replacing the original color information with the target color information, and then reconstructing the fabric image via the Dual-Tree Complex Wavelet, so as to realize the computer simulation of two-component colored spun knitted fabric. On this basis, a quantitative analysis is conducted on the simulation results via the multi-dimensional texture and color similarity indexes. Then, the significance test is carried out for the evaluation results. As indicated in the results, compared with the traditional frequency domain filtering method and the wavelet transform method, the computer simulation algorithm established in this paper can realize the computer simulation of multi-type "fancy color" effect of colored spun knitted fabrics. The simulation results of this scheme improve 4.57% and 1.87% in texture similarity, 10.23% and 6.01% in color similarity, 3.31 dB and 5.69 dB in PSNR, and 0.0161 and 0.0108 in SSIM, respectively. As shown in the significance test results, the difference significance level of the evaluation results is less than 0.05. Therefore, the simulation effect of this method is significantly improved compared with the two comparison methods. [ABSTRACT FROM AUTHOR]

Objective: To quantitatively compare DLIR and ASiR-V with realistic anatomical images. Methods: CT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (fpeak), mean SSIM (MSSIM), and mean GM (MGM) were used as quantitative metrics to compare image quality, for each anatomical region, protocol, algorithm, dose level, and slice thickness. Results: Image noise had a strong (R2 > 0.99) power law relationship with dose for all algorithms. For the abdomen and chest, fpeak shifted from 0.3 (FBP) down to 0.15 mm-1 (ASiR-V 100%) with increasing ASiR-V strength but remained 0.3 mm-1 for all DLIR levels. fpeak shifted down for the brain protocol with increasing DLIR levels. Three levels of DLIR produced similar image noise levels as ASiR-V 40%, 80%, and 100%, respectively. DLIR had lower MSSIM but higher MGM than ASiR-V while matching imaging noise. Conclusion: Compared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment. Key Points: • DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V. • DLIR has additional edge enhancement as compared to ASiR-V. • DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation. [ABSTRACT FROM AUTHOR]

Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images. [ABSTRACT FROM AUTHOR]

This paper presents two methods to deal with the problem that traditional image denoising algorithms may easily neglect image texture details. The first one is global adaptive fractional integral algorithm (GAFIA) which deals with common noises. It selects the optimal integral order of each pixel based on the local average gradient. The second is image denoising and enhancement algorithm based on adaptive fractional calculus of small probability strategy (AFC-SPS) which deals with salt & pepper noise. It regards the appearance of noise points as small probability events, divides them, and segments the image edges and weak textures by the improved two-dimensional Otsu algorithm. Then, the function of adaptive fractional order is constructed. Experimental results show that, both of the methods have good image denoising effect, and the AFC-SPS algorithm has a better effect than other methods in enhancing the edge and preserving the texture. [ABSTRACT FROM AUTHOR]

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics. [ABSTRACT FROM AUTHOR]

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted. [ABSTRACT FROM AUTHOR]

Abstract: As an emerging biometric technology, palmprint recognition has been extensively researched due to its easy collection, user friendliness, high verification accuracy and reliability. Blanket dimension is a commonly used fractal dimension and has the multi-resolution characteristics with which the image texture information can be better extracted. In this work, palmprint recognition with blanket dimension and its expansions was investigated. The efficiencies of horizontally and vertically expanded blanket dimensions for extracting the directional feature of palmprint were compared, and a palmprint recognition algorithm based on horizontally expanded blanket dimension (HEBD) was proposed according to the comparison results. Furthermore, a multi-scale HEBD (MHEBD) algorithm for palmprint recognition was also presented, and the MHEBD was demonstrated to be more effective than the single-scale HEBD for feature extraction. The algorithm was evaluated on Hong Kong Polytechnic University (PolyU) database (v2) and CASIA database. The experimental results indicate that the multi-scale HEBD can extract the palmprint features effectively and efficiently with a high recognition rate and less processing time. [Copyright &y& Elsevier]

This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12 % . [ABSTRACT FROM AUTHOR]

Image super-resolution reconstruction (SR) aims to use a specific algorithm to restore a low-resolution blurred image in the same scene into a high-resolution clear image. Due to its wide application value and theoretical value, image super-resolution reconstruction technology has become a research hotspot in the field of computer vision and image processing, and has attracted widespread attention from researchers. Compared with traditional methods, deep learning methods have shown better reconstruction effects in the field of image super-resolution reconstruction, and have gradually developed into the mainstream technology. Therefore, this paper classifies the image super-resolution reconstruction problem systematically according to the structure of the network model, and divides it into two categories: the super-division method based on the convolutional neural network model and the super-division method based on the generative confrontation network model. The main image super-resolution reconstruction methods are sorted out, several more important deep learning super-resolution reconstruction models are described, the advantages and disadvantages of different algorithms and the applicable application scenarios are analyzed and compared, and the different types of super-resolution algorithms are discussed. The method of mutual fusion and image and video quality evaluation, and a brief introduction to commonly used data sets. Finally, the potential problems faced by the current image super-resolution reconstruction technology are discussed, and a new outlook for the future development direction is made. [ABSTRACT FROM AUTHOR]

Lesion relates to the unnatural growth of tissues in which skin lesions are most common. It may have two types: benign and malignant. Malignant lesion has the highest death rate compared to benign lesion due to their major metastases. Metastasis is nothing but the capacity to enter distant organs. There are a variety of diagnostic features to differentiate both. The most popular approach is the histological approach through which the impacted tissues are taken and examined underneath a microscope. As it is an invasive technique and the chances of spreading to other natural underlying structures, other diagnostic approaches have come into play. This work is one of them, which is a non-invasive technique and is detected by a computer vision algorithm. To determine where such a lesion is malignant or benign, the extraction feature utilizes a mixture of both the Histograms of oriented gradients (HOG) and the Grayscale Level Co-occurrence Matrix (GLCM) functions. Which are applied to the Convolutional neural network. Convolutional neural network (CNN) obtained an average accuracy of 98.32 percent on a dataset of 4100 images, which is greater than the existing method. [ABSTRACT FROM AUTHOR]

Abstract: Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90. [Copyright &y& Elsevier]

Abstract: One of the principal causes for image quality degradation is blur. This frequent phenomenon is usually a result of misfocused optics or camera motion, and it is very difficult to undo. Beyond the impaired visual quality, blurring causes problems to computer vision algorithms. In this paper, we present a simple yet powerful image descriptor, which is robust against the most common image blurs. The proposed method is based on quantizing the phase information of the local Fourier transform and it can be used to characterize the underlying image texture. We show how to construct several variants of our descriptor by varying the technique for local phase estimation and utilizing the proposed data decorrelation scheme. The descriptors are assessed in texture and face recognition experiments, and the results are compared with several state-of-the-art methods. The difference to the baseline is considerable in the case of blurred images, but also with sharp images our method gives a highly competitive performance. [Copyright &y& Elsevier]

Over the past years, image manipulation tools have become widely accessible and easier to use, whichmade the issue of image tampering farmore severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is transposed onto another image. The proposed study consists of four features extraction stages used to extract the important features from suspicious images, namely, Fractal Entropy (FrEp), local binary patterns (LBP), Skewness, and Kurtosis. The main advantage of FrEp is the ability to extract the texture information contained in the input image. Finally, the "support vector machine" (SVM) classification is used to classify images into either spliced or authentic. Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods. Overall, the proposed algorithm achieves an ideal balance between performance, accuracy, and efficacy, which makes it suitable for real-world applications. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. The use of MRI is linked to the use of computer vision algorithms and data mining techniques. Thus, the success of MRI could not be understood without referring to the implementation of powerful algorithms to handle all data generated. In this study, a new texture algorithm was developed in order to obtain new features based on the radial distribution of the textures from MRI. This new algorithm is called Radial Texture Algorithm (RTA) and it was compared with four classical texture features approaches. The results obtained allow describing the semantic context of the texture features for this new algorithm and the relationship among the features from this algorithm and the classical approaches. Besides, the results obtained by means of these computer vision algorithms were correlated with the results obtained by means of physico-chemical and sensory parameters of the Iberian loins. All of computer vision algorithms achieved correlation coefficients higher than 0.500, noting that RTA reached the highest correlation coefficients in almost all cases. These high correlation coefficients confirm the proposed algorithm as an alternative to the other computational texture features approaches in order to compute the physico-chemical and sensory parameters of meat products in an accurate, fast, non-destructive and efficient way. [ABSTRACT FROM AUTHOR]

Radiomics uses computers to extract a large amount of information from different types of images, form various quantifiable features, and select relevant features using artificial-intelligence algorithms to build models, in order to predict the outcomes of clinical problems (such as diagnosis, treatment, prognosis, etc.). The study of liver diseases by radiomics will contribute to early diagnosis and treatment of liver diseases and improve survival and cure rates of liver diseases. This field is currently in the ascendant and may have great development in the future. Therefore, we summarize the progress of current research in this article and then point out the related deficiencies and the direction of future research. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the physical image quality of a commercially available image‐based iterative reconstruction (IIR) system for two object contrasts to resemble a soft tissue (60 HU) and an enhanced vessel (270 HU), and compare the results with those of filtered back projection (FBP) and iterative reconstruction (IR). A 192‐slice computed tomography (CT) scanner was used for data acquisitions. IIR images were processed from the FBP images. Task‐based in‐plane transfer function (TTF) and slice sensitivity profile (SSPtask) were measured from rod objects inside of a 25‐cm diameter water phantom at four dose levels (2.5, 5, 10, and 20 mGy). Noise power spectrum (NPS) was measured from the water‐only part. System performance (SP) function was calculated as TTF2/NPS over FBP, IR, and IIR for comparison. In addition, an image subtraction was performed using images of rod objects, a bar‐pattern phantom, and a clinical abdomen case to observe the noise reduction performance of IIR. As a results, IIR mostly preserved TTF and SSPtask of FBP, whereas IR exhibited enhanced TTF at 10 and 20 mGy for 60 HU contrast and at all doses for 270 HU contrast. SP of IIR at 2.5, 5, 10 mGy (half doses) were similar to those of FBP at 5, 10, 20 mGy, respectively. IR exhibited enhanced SP at medium to high frequencies. The subtracted images showed weak remained edge signals in the bar‐pattern and abdominal images. In conclusion, IIR uniformly improved the task‐based image quality of FBP over the entire frequency range, whereas IR improved the characteristics over medium to high frequencies. The dose reduction potential of IIR estimated from SP is approximately 50%, when allowing the slight signal reductions. [ABSTRACT FROM AUTHOR]

High efficiency video coding (HEVC), which is the newest video coding standard currently, achieves the best coding efficiency compared with all the other existing video coding standards. However, the computational complexity of the typical HEVC encoder dramatically increases because of the recursive searching scheme for finding the best coding unit (CU) partitions. In this paper, an adaptive fast CU size decision algorithm for HEVC Intra prediction is proposed based on CU complexity classification (CC) by using machine learning (ML) technology. Firstly, certain image features are extracted to characterize the CU complexity, which has a strong relationship with CU partitions, and then, the support vector machine is employed to analyze and construct the classification model according to the CU complexity. Finally, the proposed adaptive fast CU size decision algorithm, named as CCML, is released based on the complexity classification. The experimental results show that the proposed algorithm could achieve around 60% encoding time reduction for various test video sequences on average with only 1.26% Bjontegaard delta bit rate increase compared with the reference test model HM15.0 of HEVC. [ABSTRACT FROM AUTHOR]

The geometry of impounded surfaces is a key tool to reservoir storage management and projection. Yet topographic data and bathymetric surveys of average-aged reservoirs may be absent for many regions worldwide. This paper examines the potential of contour line interpolation (TOPO) and Structure from Motion (SfM) photogrammetry to reconstruct the topography of existing reservoirs prior to dam closure. The study centres on the Paso de las Piedras reservoir, Argentina, and assesses the accuracy and reliability of TOPO- and SfM- derived digital elevation models (DEMs) using different grid resolutions. All DEMs were of acceptable quality. However, different interpolation techniques produced different types of error, which increased (or decreased) with increasing (or decreasing) grid resolution as a function of their nature, and relative to the terrain complexity. In terms of DEM reliability to reproduce area-elevation relationships, processing-related disagreements between DEMs were markedly influenced by topography. Even though they produce intrinsic errors, it is concluded that both TOPO and SfM techniques hold great potential to reconstruct the bathymetry of existing reservoirs. For areas exhibiting similar terrain complexity, the implementation of one or another technique will depend ultimately on the need for preserving accurate elevation (TOPO) or topographic detail (SfM). [ABSTRACT FROM AUTHOR]

Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively. [ABSTRACT FROM AUTHOR]

Synthesis, characterization and phase-transition studies in pure and 20 µl citrate-capped Au nanodispersed liquid crystalline (LC) N-(p-n-hexyloxybenzylidene)-p-n-octyloxy aniline, 6O.O8, compounds are carried out using a polarizing microscope attached with hot stage and camera. The polarizing microscopy technique is used to measure the phase-transition temperatures. Differential scanning calorimetry is used to determine the transition temperatures and enthalpy values. By the dispersion of citrate-capped Au nanoparticles into the LC compounds, the nematic transition temperature is decreased. Furthermore, statistical parameters-based image enhancement algorithm is proposed to identify the crystalline phases in the pure and nano-doped LC compound. Empirical result analysis clearly reveals the robustness of this method over existing image enhancement methods. [ABSTRACT FROM AUTHOR]

Seam-line-based image fusion technology is widely used in unmanned aerial vehicle (UAV) image mosaicking for its superiority to effectively avoid the ghosting phenomenon. Existing algorithms typically determine a seam-line by minimizing the sum of the mismatching values on the line. However, the strategy of directly summing the mismatch scores is inadequate because a seam-line segment with significant mismatches, even if it is short, is more visible than a lengthy one with small differences. Seeking the minimal solution also results in these methods having large computation cost. To this end, we propose a novel seam-line determination method based on minimal connected area searching and minimum spanning tree to eliminate observable visual discontinuity and improve the time efficiency. The proposed method first finds the minimal connected area by eliminating occlusions across the overlapping area. It then applies the minimum spanning tree algorithm to find the optimal seam-line. Finally, it searches the seam-line around the equidistant lines to reduce the minimum connected area, thus reducing the computation. The experimental results on various types of UAV images demonstrate that the proposed method achieves excellent performance and competitive computational efficiency relative to the state-of-the-art methods for image mosaicking. [ABSTRACT FROM AUTHOR]

Saliency region detection methods have become one of the hotspots in the field of image processing as an important method to improve the real-time and accurate analysis of massive data. Integrating more effective prior knowledge is a viable direction for improving the performance of saliency region detection methods. Most of the methods based on background prior and boundary connectivity prior assume the boundary area of the image as the background, by restraining the background to highlight the salient area. When the boundary area of the image does not describe the background well (such as a large difference in border area features), if the entire frame of the image is put together to compute the background feature, the calculation of the background feature will be inaccurate. In view of the above shortcomings, this paper proposed a saliency region detection method based on background and spatial position. This method carried on the image boundary super pixel clustering, determined the background feature according to the clustering center, and used the difference between the super pixel on the image and the background super pixel, and its spatial position to calculate the salient of the super pixels. This approach used MATLAB to program and experiment. The method was compared with a series of the state-of-the-art methods. The AUC of proposed algorithm reaches 0.839, and the MAE is 0.220, showing the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For image enhancement method based on the fractional order differential, it is difficult to artificially give the optimal order of the fractional differential which can make the image enhancement effect better, and it is hard to ensure the enhancement of the target object while preserving the information of background pixels if the entire image is filtered by a fixed differential order. In order to solve this problem, the image is segmented into the object area and the background area according to the Otsu threshold algorithm based on Markov Random Field firstly. On the basis of the principle of the fractional differential for image enhancement, a piecewise function is established by combining with the different characteristics of pixels in each area, then the best order of fractional differential in the two areas can be determined adaptively. Thus, a novel adaptive fractional order differential algorithm for image enhancement on the basis of segmentation is put forward. Several fog-degraded traffic images are selected for experiments and processed by three other algorithms. The results of comparison exhibit the superiority of our algorithm. [ABSTRACT FROM AUTHOR]

In order to make the RBF hidden layer centres being established more adaptively and avoid the blindness, this paper proposes a fusion algorithm in order to optimize the parameters of the RBF neural network used in recognizing the state of coal flotation. Firstly, in the optimization algorithm, the improved immune algorithm was used to determine the center position and the number of hidden layer of RBF neural network. Before this, the immune algorithm has been improved in several aspects, such as the initial population selection algorithm and the method for segment selection of affinity thresholds. In addition, the antibody removal mechanism, antibody immune mechanism and antibody concentration regulation principle had also been added in immune algorithm. Secondly, in virtue of combining a fuzzy C-means clustering algorithm, the centers of the hidden layer were optimized accurately. Through the sample verification, the RBF neural network obtained by the fusion algorithm was proved to have been improved significantly in the accuracy of identifying the coal flotation state and has better generalization ability. [ABSTRACT FROM AUTHOR]

In order to improve the robustness of existing reversible watermarking algorithms and strengthen the imperceptibility of watermarked images, a robust reversible watermarking algorithm based on redundant integer wavelet transform and compressed sensing is proposed. Firstly, the algorithm selects the high capacity embedding regions in the original image and then redundant integer wavelet transform is conducted within the selected areas, and wavelet coefficients matrices are obtained after the sparse. After that, the two intermediate-frequency parts of each wavelet coefficient matrix are conducted by compressed sensing with the same observation matrix, and the two generated compressed observation values are merged. Finally, the watermark is embedded into the observation value of intermediate-frequency coefficient part, to recover the sparse signal by using the reconstruction algorithm, and then the watermarked image is obtained through the redundant integer wavelet inverse transform. Simulation results indicate that the algorithm can not only realize blind extraction, but also significantly improve robustness, imperceptibility and embedded watermark capacity compared with other similar algorithms. It is easy to implement. What's more, the generated watermarked images have high quality and can completely lossless restore the original carrier image without any attacks. [ABSTRACT FROM AUTHOR]

The quality of olive fruit and its virgin olive oil is a main concern for consumers and fruit industrial companies. The effectiveness and fast detection of olive's skin defects is the most decisive factor in determining its quality. It is necessary to design and implement image processing tools for segmentation and correct classification of the different fresh incoming olive batches. In this paper, we propose a new automatic image segmentation algorithm, based on discrete wavelets transform. The aim of the segmentation algorithm is to discriminate between olives and the background with the challenge of irregular and dispersive lesion borders, low contrast, artifacts in the olive fruit and variety of colors within the interest region. The second part of our work proposes a scheme for olive fruit classification. The classifier first identifies the olive fruit color and then, based upon discrete wavelets transform and Tamura statistical texture features, the healthy olive fruit is distinguished from the damaged one. The new texture feature vector is, then, compared with the robust Local Binary Pattern feature vector. The simplicity of our segmentation and classification algorithms makes them appropriate for designing a productive and profitable computer vision machine. [ABSTRACT FROM AUTHOR]

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It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset. [ABSTRACT FROM AUTHOR]

Supplementary information, such as multi-temporal spectral data and textural features, has the potential to improve land cover classification accuracy. However, given the larger volumes of remote sensing data, it is difficult to utilize all the features of remote sensing big data having different times and spatial resolutions. Inefficiency is also a large problem when dealing with large area land cover mapping. In this study, a new mode of incorporating spatial and temporal dependencies in a complex region employing the random forests (RFs) classifier was utilized. To map land covers, spring and autumn spectral images and their spectral indexes, textural features obtained from Landsat 5 were selected, and an importance measure variable was used to reduce the data's dimension. In addition to randomly selecting the variable, we used random sampling to furthest decrease the generalization error in RF. The results showed that utilizing random sampling, multi-temporal spectral image and texture features, the classification of the Wuhan urban agglomeration, China, using RF performed well. The RF algorithm yielded an overall accuracy of 89.2% and a Kappa statistic of 0.8522, indicating high model performance. In addition, the variable importance measures demonstrated that the type of textural features was extremely important for intra-class separability. The RF model has transitivity. The algorithm can be extended by choosing a set of appropriate features for signature extension over large areas or in time-series of Landsat imagery. Land cover mapping might be more economical and efficient if no-cost imagery is used. [ABSTRACT FROM AUTHOR]

In this paper, a method is proposed to reduce artifact effect for images. The proposed approach first decomposes an original image into four subband images. The powers of the four sub-images are calculated and compared with a power threshold accompanying entropy measure to decide that whether the sub-images are reserved or not. The pixels of the reserved sub-images are then compared with a pixel threshold accompanying different entropy measure in order to determine that the pixel is discarded or not. The experimental results of this approach in comparison with other methods are performed both subjectively and qualitatively and shown that the proposed algorithms could reconstruct the desired recovered performance subjectively and objectively. [ABSTRACT FROM AUTHOR]