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The article presents a new model for image texture classification based on wavelet transformation and singular value decomposition. The proposed model is capable of improving recognition rates using a lower number of parameters on large databases.

The Wold decomposition theorem was applied to discrete-time fractional Gaussian noise (DGFN) so that the spectral properties could be examined. The regular part of DFGN was noted and its PSD was carefully computed. A new fractal dimension estimator was developed from the relationship between the regular PSD versus frequency and the fractal dimension of DFGN. The proposed algorithm withstands amplitude scaling and shifting.

Texture synthesis is a necessary component of realistic scene generation. In particular, it is necessary for the simulation of image backgrounds for the testing of automatic target recognizers. We present a synthesis-by-analysis model for texture replication or simulation. This model can closely replicate a given textured image or produce another image which, although distinctly different from the original, has the same general visual characteristics and the same first and second-order gray-level statistics as the original image. In effect, such a synthetic image looks like a continuation of the original scene; as if another picture of the scene were taken adjacent to the original. The texture synthesis algorithm proposed herein contains three distinct components: A moving-average (MA) filter, a filter excitation function, and a gray-level histogram. The analysis portion of the texture synthesis algorithm derives the three from a given image. The synthesis portion convolves the MA filter kernel with the excitation function, adds noise, and modifies the histogram of the result. The advantages of this texture model over others include conceptually and computationally simple and robust parameter estimation, inherent stability, parsimony in the number of parameters, and synthesis through convolution. We 1) describe a procedure for deriving the correct MA kernel using a signal enhancement algorithm; 2) demonstrate the effectiveness of the model by using it to mimic several diverse textured images; 3) discuss its applicability to the problem of infrared background simulation; and 4) include detailed algorithms for the implementation of the model.

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 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 a speedup of 500 times for certain operations. [ABSTRACT FROM PUBLISHER]

The commonly used artificial visual interpretation and existing object-oriented computer automatic recognition methods have some disadvantages, such as low efficiency and insufficient accuracy in recognizing earthquake-induced building damage in unmanned aerial vehicle (UAV)-based images. In this paper, we report the latest progress in research on machine learning algorithms in artificial intelligence, then propose a new method of recognizing earthquakeinduced building damage. Using the bag of words (BoW) model, scale-invariant feature transformation (SIFT) characteristics were clustered to build an eigenvector tag library with K clustering centers as visual words. After images were expressed by visual words as eigenvectors with unified dimensions, a histogram intersection kernel (HIK) was then employed to construct the histogram intersection kernel support vector machine (HIK-SVM) to classify images and recognize earthquake-induced building damage. Building damage due to the magnitude 6.0 earthquake that occurred in Luxian, Sichuan Province on September 16, 2021 was analyzed as an example. When the proposed method was applied to recognize earthquake damage using UAV-based images, the average recognition accuracy reached 91.7%. The experimental results verified the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios. [ABSTRACT FROM AUTHOR]

This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center symmetric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%. • Propose a computer vision method for classifying concrete spall severity. • Extreme Gradient Boosting Machine and deep neural networks are employed. • Use local binary pattern variants as image texture descriptors. • The boosting machine is finetuned by the Aquila optimizer. • The proposed method achieves a high classification accuracy rate = 0.99. [ABSTRACT FROM AUTHOR]

Printed circuit board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving, so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods; however, they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduces the number of trainable ML parameters and, thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection. The study results indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities. [ABSTRACT FROM AUTHOR]

With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%. [ABSTRACT FROM AUTHOR]

In the history of human society, information is an indispensable part of development, and extracting useful data from the huge amount of data can effectively solve some real-life problems. At the same time, with the continuous improvement of modern technology and computer hardware and equipment, the demand for information subjects is getting higher and higher. The field of human research is gradually moving in the direction of multiscientific exploration and deepening of related work. The related information disciplines have been studied by more scholars and experts in the field to a greater extent and put forward more demanding theories and methods. In the process of human social development, things are constantly changing and updating, which makes data mining technology more and more attention. This article first describes the relevant basic theoretical knowledge of 3D reconstruction, and secondly, it analyzes the big data platform technology, which mainly includes the analysis of the big data platform architecture, the description of the Hadoop distributed architecture, and the analysis of the HBase nonrelational database. Finally, it studies the video image feature extraction technology of video content and uses this to study the design of a big data image processing platform for 3D reconstruction models. [ABSTRACT FROM AUTHOR]

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction. [ABSTRACT FROM AUTHOR]

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of “big imaging data,” as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available. [ABSTRACT FROM AUTHOR]

According to the type of sequences used, either morphological or dynamic functional study can be performed using magnetic resonance imaging (MRI). The aim of this study is to find out if vascular information found, in dynamic MR sequences, already exists in anatomical MR sequences in the particular case of Legg-Calve-Perthes disease (LCPD). LCPD is due to a loss of circulation to the femoral head in a growing child resulting in avascular necrosis and leading to possible distortion of size and shape of the proximal lemur. MRI acquisitions consist in performing two anatomical sequences and one dynamic sequence with a gadolinium-diethylenetriamiue pentaacetic acid (Gd-DTPA) injection. Five new parametric images characterizing hyper-and hypo-vascularized areas are computed from the dynamic MR sequence. For each new image, the two corresponding anatomical images are found and registered. Then, four types of regions of interest (ROIs) are extracted: healthy hyper-and hypo-vascularized areas and pathological hyper-and hypo-vascularized areas. First-order statistical parameters and texture parameters (Haraliek's method, run length method, fractal parameters, autoregressive factors and Laws' texture energy method) are computed in each ROI. Then, a statistical study based on a T test is performed. Results show that some parameters could discriminate the four ROI types. Hence, dynamic vascular image and intrinsic anatomical image characteristics seem to be correlated. Finally, the disease can be evaluated with objective parameters using only anatomical sequences. Index Terms--Image texture analysis, Legg-Calve-Perthe disease, magnetic resonance imaging, vascular imaging.

We propose a method of computer processing of images of cracks. To discern cracks on an image, a segmentation method which includes an analysis of the brightness density function and binary transformation is used. To determine orientations of a crack, we calculate the locus of points of its skeleton with their subsequent approximation by the method of least squares. As a measure of damage, the area of cracks within the boundaries of the analyzed region was used. We investigate images on the surface of a roll of a continuous billet casting machine. By the moment the roll was removed from service, it had been operated in 4500 heats (the weight of which was 350 t). The cause of its removal from service was the cracking of its working surface. [ABSTRACT FROM AUTHOR]

An object-based stereo image coding algorithm is proposed. The algorithm relies on modeling of the object structure using 3-D wire-frame models and motion estimation using globally rigid and locally deformable motion models. Algorithms for the estimation of motion and structure parameters from stereo images are described. Motion parameters are used to construct predicted images by mapping the image texture on the object surface. Coding of object parameters, appearing background regions, and prediction errors are investigated, and experimental results with standard stereo image sequences depicting general scenes are presented. The proposed algorithm is seen to be very efficient for applications such as stereoscopic video transmission. Furthermore, it is especially well suited to advanced applications such as generation and transmission of intermediate views for multiview receiver systems and applications in which objectwise editing of the bit stream is required. The latter include video production using preanalyzed scenes and virtual reality applications. Index Terms - 3-D models, object-based coding, stereo sequences.

As the availability of powerful 3-D scene renderers grows, the demand for high visual quality 3-D scenes is increasing. Besides more detailed geometric and texture information, this presupposes the ability to map dynamic textures. This is obviously needed to model movies, computers, and TV screens but also, for example, for the landscape as seen from inside a moving vehicle or shadow and lighting effects that are not modeled separately. Downloading the complete scene to the user, before letting him interact with the scene, becomes very impractical and inefficient with huge scenes. If, as is often the case, a back channel is available, on-demand downloading allows the user to start interacting with the scene immediately. Specifically for dynamic texture, if we know the viewpoint of the user (or several users), we can code the texture taking into account the viewing conditions, i.e., coding and transmitting each part of the texture with the required resolution only. Applications that would benefit from view-dependent coding of dynamic textures include (but are not limited to) multiplayer three-dimensional (3-D) games, walkthroughs of dynamic constructions or scenes, and 3-D simulations of dynamic systems. In this paper, the feasibility of such a scheme based on an adapted optimal level allocation video codec is shown together with the huge data-rate reductions that can be achieved with it [ABSTRACT FROM PUBLISHER]