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In the flotation process, the flotation froth texture is an indicator of the flotation state. To recognize the flotation state based on texture features accurately and to provide guidance for production operations, this paper proposes a method for flotation froth image texture extraction based on the deterministic tourist walks algorithm. First, a weighted graph model of a froth image is built using deterministic tourist walks. Next, the degree distribution and the unit intensity distribution of the weighted graph are extracted. The contrast of the node degree and the contrast of the node unit intensity are calculated as the texture feature indexes. The texture feature indexes are used for flotation production state classification and recognition. The experimental results demonstrate that the proposed method can extract froth image texture features accurately and provide effective guidance for flotation production. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]

The utilisation of magnetic resonance imaging (MRI) images for the automated detection of Alzheimer's disease has garnered significant attention in recent years. This interest stems from the progress made in machine learning techniques and the possible application of such methods in the field of diagnostics. This study aims to evaluate the performance of 16 histogram-based image texture descriptors and features extracted from 18 pre-trained convolutional neural networks in characterising brain patterns observed in 2D slices of MRI images. The primary objective is to determine the most effective feature types for this task. The characteristics were taken from the magnetic resonance imaging (MRI) dataset given by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The study involved the calculation of features on 2D axial, coronal, and sagittal slices, followed by classification using five binary machine learning algorithms. The objective was to differentiate between individuals with normal cognitive function and those diagnosed with Alzheimer's disease. The proposed methodology additionally facilitated the identification of specific brain areas to be selected for each axis, in order to achieve optimal accuracy. This involved determining the matching feature and classifier combinations. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a key component of many social media analysis projects. Additionally, prior research has concentrated on a single modality in particular, such as text descriptions for visual information. In contrast to standard image databases, social images frequently connect to one another, making sentiment analysis challenging. The majority of methods now in use consider different images individually, rendering them useless for interrelated images. We proposed a hybrid Arithmetic Optimization Algorithm- Hunger Games Search (AOA-HGS)-optimized Ensemble Multi-scale Residual Attention Network (EMRA-Net) technique in this paper to explore the modal correlations including texts, audio, social links, and video for more effective multimodal sentiment analysis. The hybrid AOA-HGS technique learns complementary and comprehensive features. The EMRA-Net uses two segments, including Ensemble Attention CNN (EA-CNN) and Three-scale Residual Attention Convolutional Neural Network (TRA-CNN), to analyze the multimodal sentiments. The loss of spatial domain image texture features can be reduced by adding the Wavelet transform to TRA-CNN. The feature-level fusion technique known as EA-CNN is used to combine visual, audio, and textual information. The proposed method performs significantly better than the existing multimodel sentimental analysis techniques of HALCB, HDF, and MMLatch when evaluated using the Multimodal Emotion Lines Dataset (MELD) and EmoryNLP datasets. Also, even though the size of the training set varies, the proposed method outperformed other techniques in terms of recall, accuracy, F score, and precision and takes less time to compute in both datasets. [ABSTRACT FROM AUTHOR]

Aiming at the poor anti-jamming effect of traditional fuzzy c-means clustering image segmentation method, a fuzzy c-means clustering image segmentation algorithm based on Hidden Markov model is proposed. Hidden Markov model is used to extract the model state, observation state, initial probability, transition probability and visible symbol probability parameters of fuzzy c-means clustering image. According to these parameters, the image texture is measured, and the image mean is removed to eliminate the influence of brightness. Then the image features are extracted by combining the standard deviation. Through the color space and convert it from RGB model to his model. On the basis of removing the image noise, the image is smoothed to achieve image segmentation. Experimental results show that the algorithm has good segmentation effect, smooth segmentation boundary, little influence of image noise and strong robustness. [ABSTRACT FROM AUTHOR]

The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. [ABSTRACT FROM AUTHOR]

Pattern recognition technologies aim to develop new techniques for transforming original data into other representations that honestly illustrate their content and can then be used to analyze and understand the original data. In the field of computer vision based on images, which is the dominant technology for machine vision solutions, image texture is a rich information source, used in many feature extraction techniques to obtain salient and distinctive features that were later used in pattern recognition applications. However, depending on the application, the extracted feature vector may be subject to some constraints such as length and accuracy. This work aims to develop a new texture-based feature extraction technique that can provide a feature vector with a high degree of distinction that can be controlled in terms of length and accuracy. The new scheme, called adjustable local binary pattern (A-LBP) or adjacent block features based LBP, derived from the original LBP, uses neighboring blocks and a linear relationship between the features of these blocks to form a binary codeword's that can be used to represent the image. In our study, the block feature is extracted using the discrete cosine transform. To evaluate the performance of the proposed scheme, an A-LBP based biometric and bio-watermarking systems were developed. [ABSTRACT FROM AUTHOR]

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved. [ABSTRACT FROM AUTHOR]

Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick & SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to ∼ 19.5 × and ∼ 37 × speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research. [ABSTRACT FROM AUTHOR]

Identifying locations of protein expression in live cells plays an important role in several medical applications ranging from early disease diagnosis to monitoring effectiveness of drugs. Protein localization is directly related to their cell types. Today florescence imaging is widely used to understand biology at the cellular level. Hence, cell phenotype classification in fluorescence microscope images, is related with protein localization. Today it is performed by human, which is very time consuming with low accuracy. According to the visual structure, it can be seen that samples of a unique cell type have quite similar texture, but the texture of different cell types, are very different. In this respect, texture information can be used more widely than shape or color information, to classify types. Local ternary pattern is a noise-resistant texture descriptor that provides discriminative features. In this paper a local texture analysis descriptor is proposed titled multi threshold uniform-based local ternary patterns with notation MT-ULTP. MT-ULTP extracts local significant texture information in different locality levels. In this respect, local ternary patterns are extracted in different thresholds and finally the occurrence probability of the uniform patterns is extracted as features. MT-ULTP is a skillful combination of LTP and MLBP with novelty in feature extracting and local pattern selecting. Performance of the proposed descriptor is evaluated on 2d-hela dataset in terms of accuracy. 2d-hela is the benchmark dataset of cell phenotype images. Experimental results show that MT-ULTP provides higher classification rates than very well-known texture descriptors such as lbp-like descriptors. In other experiments, it has been shown that ignoring uniform textural patterns in the image analysis can increase the accuracy of cell phenotype classification and some other computer vision-based applications. The results also showed that extraction uniform patterns based on a combination of thresholds provide better results than the simple form in local ternary patterns. The proposed image texture descriptor is a general case which can be used in many computer vision applications to describe the image contents. [ABSTRACT FROM AUTHOR]

The multimodal biometrics is mainly used for the purpose of person certification and proof. Lot of biometrics is used for human authentication. In which ear and fingerprint are efficient one. There are three vital phases involved in the biometric detection which include the Preprocessing, Feature extraction and the classification. Initially, preprocessing is done with the help of median filter which lends a helping hand to the task of cropping the image for choosing the position. Then, from the preprocessed Finger print and ear image texture and shape features are extracted. In the long run, the extracted features are integrated. The integrated features, in turn, are proficiently classified by means of the optimal neural network (ONN). Here, the NN weights are optimally, selected with the help of firefly algorithm (FF). The biometric image is classified into fingerprint and ear if the identical person images are amassed in one group and the uneven images are stored in a different group. The performance of the proposed approach is analyzed in terms of evaluation metrics. [ABSTRACT FROM AUTHOR]

Facial expression is a powerful way for human emotional communications. According to various applications, automatic facial expression recognition becomes an interesting problem for researchers in different areas. An automatic facial expression recognition system recognizes the expressed emotion in input facial image using several processing stages. Feature extraction is a vital step in facial expression recognition. One of the most widely used techniques for feature extraction in machine vision is utilizing Gabor filter which is sensitive to lines at various orientations. The disadvantage of Gabor filters is their computational cost and large feature vector length. Inspiring the human vision system and stimulations of complex cells, in this paper, firstly, facial image is convolved with Gabor filters. Then, the achieved convolution matrices are properly coded based on the maximum and minimum responses. Finally, the feature vector is obtained by calculating the histogram of these codes. The length of achieved histogram for 16 and 8 Gabor filters are 240 and 56, respectively, which is considerably less than keeping all Gabor responses. The proposed method is (person-independently) evaluated on four facial expression recognition datasets including CK+, SFEW, MMI, and RAF-DB. The experimental results show that the proposed method outperforms existing image texture descriptors in facial expression recognition in both controlled and uncontrolled images. [ABSTRACT FROM AUTHOR]

With the development of image feature matching technology, feature matching algorithms based on deep learning have achieved excellent results, but in scenarios with low texture or extreme perspective changes, the matching accuracy is still difficult to guarantee. In this paper, a superresolution reconstruction method based on a Residual-ESPCN (efficient subpixel convolutional neural network) approach is proposed based on LoFTR (local feature matching with transformers). The superresolution method is used to improve the interpolation method used in ASFF (adaptive spatial feature fusion) to increase the image resolution, enhance the detailed information of the image, and make the extracted features richer. Then, ASFF is introduced into the local feature extraction module of LoFTR, which can alleviate the inconsistency problem of information transmission between different scale features of the feature pyramid and lessen the amount of information lost during transmission from low- to high-resolution levels. Moreover, to improve the adaptability of the algorithm to different scenarios, OTSU is introduced to adaptively calculate the threshold of feature matching. The experimental results show that in different indoor or outdoor scenarios, our proposed algorithm for matching features can effectively improve the adaptability of feature matching and can achieve good results in terms of the area under the curve (AUC), accuracy and recall. [ABSTRACT FROM AUTHOR]

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images. [ABSTRACT FROM AUTHOR]

Splicing Image Forgery Identification controls and picture tampering with no proof being left behind has turned out to be exceptionally moderate and exceedingly utilized, because of presence of to a great degree intense altering apparatuses, for example, Adobe Photoshop. Along these lines, there has been a quick expansion of the digitally adjusted pictures on the Internet and prevailing press. The genuineness of a digital image experiences extreme dangers because of the ascent of capable digital image altering devices that effectively adjust the image substance without leaving any obvious hints of such changes. The splicing forgery should be possible by replicated a one/more area from source image and pasted into an objective picture to create a composite image called spliced image. In this manner, this sort of forgery is viewed as challenge issue difficult from tamper identification perspective. To influence the issue most exceedingly awful some to post preparing impacts, for example, blurring, JPEG compression, rotation and scaling perhaps presented in the spliced image. This study aims to perform quantification and data analysis following feature extraction using computational techniques to detect interesting textural and anatomical changes, these extracted features then can be used as a key to distinguish between different classes (Splicing Image and non-Splicing image). This paper presents a new framework to identify the spliced image by exploiting the image texture features, and to automatically identification of spliced images. To accurately identify the spliced image, the proposed solution uses different texture features to capture deferent texture related to the edge of the object and the colour features. We have combine these features to produce a good vector to describe the splice object. Two models have been proposed in this paper first combine the vectors of the three features and feed them to the ANN classifier. Second, use the majority voting of the result of three features to take the decision. This is followed by a ANN classifier; in this model we have trained the system with 30 training 20% validation 50% testing. We evaluated the effectiveness of the classification framework for identifying spliced images by compare the result with the manual label which is done by the people who have created the data sets. In this approach we have combine different features to capture different information and feed them to the neural network to identify the splicing image. The findings outcome from this study have shown an improved approach that automatically splicing image forgery identification. We have evaluated the splicing image forgery identification using the texture features. The identification accuracy in the technique used is about 98.06%., with 99.03% sensitivity and 96.07% specificity. [ABSTRACT FROM AUTHOR]

It is of great interest in spectral-spatial features classification for hyperspectral images (HSI) with high spatial resolution. This paper presents a novel Spectral-spatial classification method for improving hyperspectral image classification accuracy. Specifically, a new texture feature extraction algorithm exploits spatial texture feature from spectrum is proposed. It employs local binary patterns (LBPs) in order to extract the image texture feature with respect to spectrum information diversity (SID) to measure the differences of spectrum information. The classifier adopted in this work is support vector machine (SVM) because of its outstanding classification performances. In this paper, two real hyperspectral image datasets are used for testing the performance of the proposed method. Our experimental results from real hyperspectral images indicate that the proposed framework can enhance the classification accuracy compare to traditional alternatives. [ABSTRACT FROM AUTHOR]

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, "non-enhanced, and edematous" areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709–0.963) and 0.739 (0.581–0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC). Methods: One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours. Results: In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively). Conclusion: Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance. [ABSTRACT FROM AUTHOR]

The CNN framework has gained widespread attention in texture feature analysis; however, handcrafted features still remain advantageous if computational cost needs to take precedence and in cases where textures are easily extracted with few intra-class variation. Among the handcrafted features, the local binary pattern (LBP) is extensively applied for analysing texture due to its robustness and low computational complexity. However, in local difference vector, it only utilizes the sign component, resulting in unsatisfactory classification capability. To improve classification performance, most LBP variants employ multi-feature fusion. Nevertheless, this can lead to redundant and low-discriminative sub-features and high computational complexity. To address these issues, we propose the neighbourhood feature-based local binary pattern (NF-LBP). Inspired by gradient's definition, we extract the neighbourhood feature in a local region by simply using the first-order difference and 2-norm. Next, we introduce the neighbourhood feature (NF) pattern to describe intensity changes in the neighbourhood. Finally, we combine the NF pattern with the local sign component and the centre pixel component to create the NF-LBP descriptor. This approach provides better complementary texture information to traditional local sign pattern and is less sensitive to noise. Additionally, we use an adaptive local threshold in the encoding scheme. Our experimental results of classification accuracy and F1 score on five texture databases demonstrate that our proposed NF-LBP method attains outstanding texture classification performance, outperforming existing state-of-the-art approaches. Furthermore, extensive experimental results reveal that NF-LBP is strongly robust to Gaussian noise and salt-and-pepper noise. [ABSTRACT FROM AUTHOR]

Accurately predicting the future trend of a time series holds immense importance for decision-making and planning across various domains, including energy planning, weather forecasting, traffic warning, and other practical applications. Recently, deep learning methods based on transformers and time convolution networks (TCN) have achieved a surprising performance in long-term sequence prediction. However, the attention mechanism for calculating global correlation is highly complex, and TCN methods do not fully consider the characteristics of time-series data. To address these challenges, we introduce a new learning model named wavelet-based Fourier-enhanced network model decomposition (W-FENet). Specifically, we have used trend decomposition and wavelet transform to decompose the original data. This processed time-series data can then be more effectively analyzed by the model and mined for different components in the series, as well as capture the local details and overall trendiness of the series. An efficient feature extraction method, Fourier enhancement-based feature extraction (FEMEX), is introduced in our model. The mechanism converts time-domain information into frequency-domain information through a Fourier enhancement module, and the obtained frequency-domain information is better captured by the model than the original time-domain information in terms of periodicity, trend, and frequency features. Experiments on multiple benchmark datasets show that, compared with the state-of-the-art methods, the MSE and MAE of our model are improved by 11.1 and 6.36% on average, respectively, covering three applications (i.e. ETT, Exchange, and Weather). [ABSTRACT FROM AUTHOR]

The challenge of image reconstruction from very-low-resolution images is made exceedingly difficult by multiple degradation factors in practical applications. Traditional methods do not consider the interactions between these degradation factors, so the results are often insufficient. To reconstruct low-resolution blurry images, both super-resolution and deblurring processes must be applied. In this paper, we propose a joint super-resolution and a deblurring model with integrated processing of the degradation factors to obtain better image quality. The joint model includes two branches, a super-resolution module and deblurring module, and both of them share the same feature extraction module. The super-resolution module consists of multiple layers of residual blocks. The deblurring module supports the robustness of the super-resolution module through feature feed-back in the learning process, by introducing an image blurring feature description into the feature representation. To create modules with high magnification, the base two-branch model is also used in two stages with scale recursion. A second-stage deblurring module receives the output of the first-stage super-resolution module and improves the deblurring capability when the image is further magnified. The modules enhance each other, significantly improve the quality of very-low-resolution text images, and maintain a low model complexity. A step-by-step training strategy is applied to reduce second-stage training difficulty. Experiments show that our approach significantly outperforms state-of-the-art methods in terms of image quality and optical character recognition accuracy, and with a lower computational cost. [ABSTRACT FROM AUTHOR]

Today, with the rapid growth of communication technology and the development of social networks and smartphones, the amount of data stored by users in the form of images has significantly increased. Therefore, several researchers have focused on the field of image retrieval in the past decade. Image retrieval means retrieving the most similar images to the query sample from a vast image database in terms of content and semantic. So far, various image retrieval methods based on feature engineering or deep features have been proposed. Most methods that use handcrafted features, despite their lower runtime, usually provide lower performance than deep learning-based approaches. In this paper, to increase the efficiency of handcrafted features in image retrieval, their combination with the output of residual blocks in deep neural networks is used in the feature fusion phase. In this paper, the efficiency of feature generation layers in common deep networks such as residual, classical sequential convolution and bottleneck has been analyzed in order to increase the efficiency of image retrieval systems based on handcrafted features. For this purpose, in most popular deep networks, the layers that perform the classification task are removed, and the output of feature generation layers at different depths, with the help of a flattened layer, is converted to feature vectors that can be connected to handcrafted made numerical features in retrieval systems. A variety of popular handcrafted features in the spatial domain, such as color and texture and wavelet in frequency, are employed to generate handcrafted features. This paper presents a novel hybrid feature set based on a combination of classical feature engineering techniques and deep convolutional neural networks suitable for image retrieval. The efficiency of the proposed method is evaluated in terms of precision and recall metrics on the benchmark datasets, such as Corel-1 and -5k. The proposed method provided an accuracy of 96.68 and 94.56% on the Corel-1k and Corel-5k, respectively. The comparative results showed that combining residual block feature maps with handcrafted features compared to bottleneck and sequential classical convolution layers increases the final performance of the image retrieval system. The comparison of the proposed method with state-of-the-art machine learning and deep learning-based techniques shows that the proposed method provides promising results in terms of precision and recall rates. [ABSTRACT FROM AUTHOR]

Although great progress has been made in facial expression recognition, it still faces challenges such as occlusion and pose changes in real-world scenario. To address this issue, we propose a simple yet effective multi-granularity and multi-scale feature fusion network (MM-Net) to achieve robust expression recognition without either manually extracting local patches or designing complex sub-networks. Specifically, we use a puzzle generator to divide the image into local regions of different granularity, which are then randomly shuffled and reorganized to form a new input image. By feeding the facial puzzles in order from fine-grained to coarse-grained, the network progressively mines the local fine-grained information, the coarse-grained information, and the global information. Besides, considering the subtle inter-class variation characteristic of different expressions, we use the multi-scale feature fusion strategy in the shallow feature extraction module to obtain global features with detailed information for capturing the subtle differences in facial expression images. Extensive experimental results on three in-the-wild FER benchmarks demonstrate the superiority of the proposed MM-Net compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep learning shows great potential in low-light image enhancement, which can improve image brightness and contrast while keeping image natural. However, due to the lack of prior knowledge extracted manually or excessive amplification of noise, these methods result in poor quality of enhanced images. To solve these challenging problems, this paper proposes a dual-branch grayscale image enhancement network based on dense residual and attention mechanism. Low-light grayscale image is used as input. Firstly, features that fuse deep and shallow information are extracted through dense residual convolution branch network; secondly, texture features are extracted through U-Net branch network combined with attention mechanism; then the extracted features are integrated, and finally luminance is adjusted by a brightness adjustment module to output an enhanced grayscale image. In addition, a joint loss function is designed to measure the network training loss from brightness, texture, contrast and noise. A large number of quantitative and qualitative experiments on LOL and VE-LOL datasets show that the proposed method improves Peak Signal to Noise Ratio Index and Structural Similarity Index by 19.65 - 59.76% and 5.61 - 85.53%, respectively, compared with EnlightenGAN, KinD++, RUAS, LLFlow, etc. The proposed method is superior to the most famous methods, thanks to the deep feature extraction and fusion capability of dense residual convolutional network and texture extraction capability of U-Net network combined with attention mechanism. [ABSTRACT FROM AUTHOR]

Biometric data (facial, voice, fingerprint, and retinal scans, for example) are widely used in identification due to their unique and irreversible nature. Facial recognition technologies are employed in a wide range of applications due to their contactless nature and convenience. However, technological advancements and the availability of access to personal information have rendered these biometric systems susceptible to attacks utilizing fake faces. As a result, the issue of anti-spoofing has emerged as a critical one in the field of facial recognition. This study proposes a joint face presentation attack (FPA) detection method based on face-weighted multi-color multi-level LBP features extracted from the combination of device-dependent HSV and device-independent L*a*b* color spaces. The facial images were converted to HSV and L*a*b* color spaces. Three levels of regional LBP features were extracted from each color channel and then concatenated. Finally, a Multi-Color Multi-Level LBP (MCML_LBP) feature vector was obtained. In addition, the Face Weighted MCML_LBP feature vector was produced (FW_MCML_LBP) by adding the LBP histogram extracted from the central region of the normalized image. The feature vectors are used to train an SVM classifier after reducing their size using PCA. Twenty-five different test scenarios were subjected to experimentation on the CASIA and Replay-Attack databases. 2.11% EER and 0.19% HTER were achieved on CASIA (Overall) and Replay-Attack (Grandtest) databases, respectively, using the L*a*b color space and the proposed feature extraction method. The results of the study showed that the proposed method was successful in FPA detection compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In multi-focus image fusion, different targets often have different sizes, and the network with poor multi-scale feature extraction ability will inevitably lead to the omission of the source image information. Inspired by this, we propose a network that uses the double multi-scale feature pyramid to extract multi-scale features. We design an effective channel compression excitation module and a channel spatial attention module, which form the semantic segmentation mechanism. The mechanism can efficiently extract multi-scale feature maps, maximize the global information of the source image and ignore similar information. We introduce a joint loss function and use post-processing operations to generate smooth decision maps and fused images. The proposed SFPN is compared with the seven existing MFIf methods in terms of six objective quantitative metrics and subjective visual effects and achieves superior performance. [ABSTRACT FROM AUTHOR]

Purpose: To construct a radiomics-clinical nomogram model for predicting the risk of local and metastatic recurrence within 3 years after radical cystectomy (RC) of bladder cancer (BCa) based on the radiomics features and important clinical risk factors for arterial computed tomography (CT) images and to evaluate its efficacy. Methods: Preoperative CT datasets of 134 BCa patients (24 recurrent) who underwent RC were collected and divided into training (n = 93) and validation sets (n = 41). Radiomics features were extracted from a 1.5 mm CT layer thickness image in the arterial phase. A radiomics score (Rad-Score) model was constructed using the feature dimension reduction method and a logistic regression model. Combined with important clinical factors, including gender, age, tumor size, tumor number and grade, pathologic T stage, lymph node stage and histology type of the archived lesion, and CT image signs, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and validation sets. Decision curve analyses (DCA) the potential clinical usefulness. Results: The radiomics model is finally linear combined by 8 features screened by LASSO regression, and after coefficient weighting, achieved good predictive results. The radiomics nomogram developed by combining two independent predictors, Rad-Score and pathologic T stage, was developed in the training set [AUC, 0.840; 95% confidence interval (CI) 0.743–0.937] and validation set (AUC, 0.883; 95% CI 0.777–0.989). The calibration curve showed good agreement between the predicted probability of the radiomics-clinical model and the actual recurrence rate within 3 years after RC for BCa. DCA show the clinical application value of the radiomics-clinical model. Conclusion: The radiomics-clinical nomogram model constructed based on the radiomics features of arterial CT images and important clinical risk factors is potentially feasible for predicting the risk of recurrence within 3 years after RC for BCa. [ABSTRACT FROM AUTHOR]

The traditional local binary pattern (LBP) is susceptible to the influence of the centre pixel and noise and cannot accurately identify wire rope surface defects. To solve this problem, an image segmentation-based central multiscale local binary pattern (ISCM-LBP) and grey level cooccurrence matrix (GLCM) feature fusion method is proposed in this paper for defect recognition. Image segmentation and multiple scales are introduced into the local binary pattern algorithm to improve the image detail description and suppress noise sensitivity. Second, the centre pixel is connected with the neighbourhood pixel to enhance the robustness of the centre pixel. To further improve the image integrity description, PCA dimensionality reduction and GLCM feature fusion are performed on the features extracted by the ISCM-LBP algorithm, and the steel wire rope surface defects are identified by a support vector machine classifier. Experimental results show that the overall recognition rate reaches 97.5%, which is at least 5% higher than that of other algorithms and can effectively identify various defects on the surface of wire rope. [ABSTRACT FROM AUTHOR]

As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed. [ABSTRACT FROM AUTHOR]

Image segmentation is widely used in life. Generally speaking, the segmentation results are divided into good and bad quality, so it is very important to propose an effective method to evaluate the quality of image segmentation. This paper proposed a framework based on edge detection and feature extraction for evaluating the quality of image segmentation. The framework belongs to unsupervised evaluation, the operation is simple and easy to implement, and readers can add or subtract methods in the framework according to specific circumstances. To prove the effectiveness of the proposed framework, we tested on four different datasets. In addition, we compare the proposed framework with some classic and newer evaluation methods. Experimental results show that the proposed framework is suitable for many types of images, and its performance is better than some existing metrics. [ABSTRACT FROM AUTHOR]

The prominence of deep learning models for classification of hyperspectral images is directly proportional to their ability to exploit spatial context and spectral bands jointly. The effectiveness of these deep learning models, however, is heavily reliant on a good amount of labelled training samples. In contrast, one of the biggest challenges with hyperspectral images is limited labelled samples availability as getting the samples annotated is a time consuming and labor-intensive process. Traditional machine learning algorithms are available for classification with a higher training time and very deep pre-trained networks like GoogleNet and VGGNet did not work well for hyperspectral image classification. The idea of one shot classification has been quite motivating in recent years to deal with the problems of limited labelled samples, imbalanced distribution of samples leading to poor classification results and overfitting. To implement one shot classification model and overcome these challenges, the proposed work is based on Siamese network that can work with limited samples or imbalanced samples. The proposed Siamese network has a handcrafted feature generation network that extracts discriminative features from the image. Experimental findings on two benchmark hyperspectral datasets demonstrate that the proposed network is capable of improving the classification performance with an overall accuracy of 95.17 and 93.25 for Pavia U and Indian Pines dataset respectively with a small scale trained data. [ABSTRACT FROM AUTHOR]

Objectives: Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies. Methods: We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse. Results: We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset. Conclusion: RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics. Critical relevance statement: This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models. Key points: - Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction. [ABSTRACT FROM AUTHOR]

The combination of feature extraction and classification methods is an often used approach for wafer map failure pattern recognition. Recently, the convolutional neural network (CNN) method is used to raw wafer map data without feature extraction. CNN can improve the accuracy, but the drawback is the expensive computation cost. In our study, we have extracted local property analysis based features and used them with the traditional classification methods, ensemble methods, and CNN for wafer map failure pattern recognition. The decision tree showed better performance than the other methods when evaluating single traditional classification methods. Therefore, we used the decision tree as the base classifier of the proposed ensemble learning. We built a constituent model on a single type of feature set first and then made an ensemble of models to produce the final decision. We found that it is better than directly building a model on total features. Also, we have tested and compared CNN on extracted feature sets and raw wafer map image data. The results showed that raw image based CNN outperforms extracted features under the same number of epochs. However, the training cost of the raw image is much expensive. Furthermore, the performance of extracted features becomes closer to the raw image when increasing the number of epochs with a much lower training cost. [ABSTRACT FROM AUTHOR]

Compared with modification-based steganography, the coverless steganography models have stronger anti-detection performance. However, the limitations of low embedding capacity and image quality are existing in current coverless steganographic models. The image style transfer is a generation task that translates a style to another while maintaining the structure and semantics of the original image. The stable structures that can be used as cover to hide secret for steganography. In this paper, a generative steganographic model based on style transfer and feature-wise deep fusion is proposed, which can achieve a large embedding capacity and high generation quality. In embedding stage, the channel reduction net is designed to distill structural features from image, and the fusion net is proposed to fuse the secret matrix and distilled structural features. The embedded structural features are participated in style transfer to finish steganography. In secret recovery stage, the two-level extraction model is adopted. The structural features are extracted from stego image by residual network, then the embedded secret message is distilled from the extracted features. Compared with existing steganographic model, the proposed model is convenient applied without code-book or database, and the experimental results show that the model achieves a larger capacity, higher security and visual quality. [ABSTRACT FROM AUTHOR]

Micro-Expressions (MEs) are the instantaneous and subtle facial movement that conveys crucial emotional information. However, traditional neural networks face difficulties in accurately capturing the delicate features of MEs due to the limited amount of available data. To address this issue, a dual-branch attention network is proposed for ME recognition, called IncepTR, which can capture attention-aware local and global representations. The network takes optical flow features as input and performs feature extraction using a dual-branch network. First, the Inception model based on the Convolutional Block Attention Module (CBAM) attention mechanism is maintained for multi-scale local feature extraction. Second, the Vision Transformer (ViT) is employed to capture subtle motion features and robustly model global relationships among multiple local patches. Additionally, to enhance the rich relationships between different local patches in ViT, Multi-head Self-Attention Dropping (MSAD) is introduced to drop an attention map randomly, effectively preventing overfitting to specific regions. Finally, the two types of features could be used to learn ME representations effectively through similarity comparison and feature fusion. With such combination, the model is forced to capture the most discriminative multi-scale local and global features while reducing the influence of affective-irrelevant features. Extensive experiments show that the proposed IncepTR achieves UF1 and UAR of 0.753 and 0.746 on the composite dataset MEGC2019-CD, demonstrating better or competitive performance compared to existing state-of-the-art methods for ME recognition. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs). Methods: Data of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs. Results: A total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively). Conclusion: Radiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs. Clinical relevance statement: Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT. Key points: • Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes. • Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects. • Radiomics helps us get to the histological view in a non-invasive approach. [ABSTRACT FROM AUTHOR]

Image processing is currently developing as a unique and the inventive field in computer research and applications in the modern area. Most image processing algorithms produce a large quantity of data as an outcome, which is termed as Big-data. These algorithms process and store bulky information either as structured or unstructured data. The use of big data analytics to mine the data produced by image processing technology has huge potential in areas like education, governments, medical establishments, production units, finance and banking, and retail business centers. This paper well defined the innovations made in Big Data analytics and image processing. In this study, a novel data classification approach especially for image analytics is proposed. To improve image quality, pre-processing is applied to huge data that has been gathered. Then, most relevant features like spatial information, texture GLCM, and color and shape features are extracted from the pre-processed image. Since the dimensions of the features are huge in size, an adaptive map-reduce framework with Improved Shannon Entropy has been introduced to lessen the dimensionality of the extracted features. Then, in the big data classification phase, an optimized deep learning classifier deep convolutional neural network (DCNN) is introduced to classify the images accurately. The weight function of the DCNN is fine-tuned using the newly proposed dragonfly updated mothsearch (DAUMS) Algorithm to enhance the classification accuracy and to solve the optimization problems of the research work. The moth search algorithm and dragonfly algorithm are both concepts in this hybrid algorithm DAUMS. [ABSTRACT FROM AUTHOR]

Extracting discriminative features using vision transformer is a popular research direction for person re-identification. However, feature extraction of existing vision transformer is relatively simple. To solve this problem, we design a vision transformer with multiple granularities for person re-identification. We propose three stages of multi-granularity feature extraction, including stage1 (shuffle), stage2 (split and concat) and stage3 (refine and enhance), which help the model to extract local and global fine features in strips. The highlight block is added in stage3 to enhance features by mathematical variation, which will highlight and strengthen the core features that are beneficial to classification. In addition, the loss function is optimized by introducing Circle Loss on top of ID Loss and Triplet Loss, and using the weighted sum of the three as the final loss function. Finally, we evaluated the performance of our method on three standard benchmark datasets: Market-1501, DukeMTMC-reID and MSMT17, and experimental results show that our method is superior to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Image retrieval is one of the hot research topics in computer vision which has been paid much attention by researchers in the last decade. Image retrieval refers to retrieving more similar images to the query form a huge image database. It is used widely in different scopes such as medical and search engines. Texture and color information play an important role in image content recognition. So, in this paper an innovative approach is proposed based on a combination of color and texture features. In this respect, an extended version of local neighborhood difference patterns (ELNDP) is proposed for the first time to achieve discriminative features. The ELNDP exploits the advantages of LBP and LNDP texture descriptors. Also, for global features extraction, optimized color histogram features in HSV color space are used to extract color features. Finally, the extended Canberra distance metric is used to retrieve more relevant images which is not sensitive to lower values like classic Canberra. The performance of the proposed approach is evaluated on five benchmark datasets such as Corel 1 K, 5 K, 10 K, STex and Colored Brodatz. The results are evaluated in terms of average precision rate (APR), average recall rate (ARR), The experimental results show that the proposed approach provides higher retrieval performance in comparison with state-of-the-art methods in this area such as machine learning-based and deep learning-based approaches. [ABSTRACT FROM AUTHOR]

Mealiness is a phenomenon in which intercellular adhesions in apples loosen during storage, causing a soft and floury texture at the time of eating, and leading to lower consumer preference. Although apples can be stored and commercially sold throughout the year, the occurrence of mealiness is not monitored during storage. Therefore, the objective of this research was to non-destructively estimate the mealiness of apple fruit by means of laser scattering measurement. This method is based on laser light backscattering imaging but can quantify a wider range of backscattered light than the conventional method by utilizing high dynamic range (HDR) rendering techniques. Lasers with wavelengths of 633 nm and 850 nm were used as a light source, and after acquiring backscattered images, profiles, and images were obtained. Profile features such as curve fitting coefficients and profile slopes and image features such as statistical image features and texture features were extracted from the profiles and images, respectively. PLS, SVM, and ANN models were used for the estimation of mealiness. The results of the estimation based on these features showed that the ANN model combining both wavelengths had a higher performance (R = 0.634, RMSE = 7.621) than the models constructed from features of single wavelength measurements. In order to further improve the performance of the model, we applied various ensemble learning methods to combine different estimation models. As a result, the ensemble model showed the highest performance (R = 0.682, RMSE = 7.281). These results suggest that laser scattering measurement is a promising method for estimating apple fruit mealiness. [ABSTRACT FROM AUTHOR]

This study presents a method for resampling detection. By combining texture analysis with resampling detection, the task of resampling detection is considered as a texture classification problem. In other words, the influence of resampling operations on a raw single-sampled image is viewed as an alteration of the image texture in a fine scale. First, local linear transform is used to obtain textural detail sub-bands. A 36-D feature vector is then extracted from the normalized characteristic function moments of textural detail sub-bands to train a support vector machine classifier. Finally, experimental results are reported on three databases, with each having almost 10,000 images. Comparison with the previous study reveals that the proposed method is effective for resampling detection. In addition, extensive experiments on cover and stego bitmap images illustrate that the proposed method is essential for constructing accurate targeted and blind steganalysis methods for heterogeneous images, raw single-sampled images, and images resampled at different scales. [ABSTRACT FROM AUTHOR]

The diagnostic phase of the treatment process is essential for patient guidance and follow-up. The accuracy and effectiveness of this phase can determine the life or death of a patient. For the same symptoms, different doctors may come up with different diagnoses whose treatments may, instead of curing a patient, be fatal. Machine learning (ML) brings new solutions to healthcare professionals to save time and optimize the appropriate diagnosis. ML is a data analysis method that automates the creation of analytical models and promotes predictive data. There are several ML models and algorithms that rely on features extracted from, for example, a patient's medical images to indicate whether a tumor is benign or malignant. The models differ in the way they operate and the method used to extract the discriminative features of the tumor. In this article, we review different ML models for tumor classification and COVID-19 infection to evaluate the different works. The computer-aided diagnosis (CAD) systems, which we referred to as classical, are based on accurate feature identification, usually performed manually or with other ML techniques that are not involved in classification. The deep learning-based CAD systems automatically perform the identification and extraction of discriminative features. The results show that the two types of DAC have quite close performances but the use of one or the other type depends on the datasets. Indeed, manual feature extraction is necessary when the size of the dataset is small; otherwise, deep learning is used. [ABSTRACT FROM AUTHOR]

Effective segmentation of skin scars is an important part of judicial scar identification. The current evaluation procedure mainly focuses on visual examination, and the accuracy needs to be improved. This paper proposes an unsupervised scar segmentation method based on saliency detection, which can correctly extract scars in complex scar conditions. The image Gaussian pyramid feature map is extracted for feature extraction, and the clustering algorithm is used to fuse the contrast features and spatial features to establish the skin scar saliency map. Finally, the skin scar region is segmented through post-processing. The experimental results show that the precision of the proposed method is improved by 18.4 % over the suboptional method compared with seven unsupervised methods on private datasets provided by a local forensic department, which proves that the proposed method supports the optimization and improvement of skin scar segmentation. [ABSTRACT FROM AUTHOR]

Objective assessment of image quality seeks to predict image quality without human perception. Given that the ultimate goal of a blind/no-reference image quality assessment (BIQA) algorithm is to provide a score consistent with the subject's prediction, it makes sense to design an algorithm that resembles human behavior. Recently, a large number of image features have been introduced to image quality assessment. However, only a few of these features are generated by using the computational mechanisms of the visual cortex. In this paper, we propose bioinspired algorithms to extract image features for BIQA by simulating the visual cortex. We extract spatial features like texture and energy from images by mimicking the retinal circuit. We extract spatial-frequency features from images by imitating the simple cell of the primary visual cortex. And we extract color features from images by employing the color opponent mechanism of the biological vision system. Then, by using the statistical features derived from these physiologically plausible features, we train a support vector regression model to predict image quality. The experimental results show that the proposed algorithm is more consistent with subjective evaluations than the comparison algorithms in predicting image quality. [ABSTRACT FROM AUTHOR]

Feature extraction is an important part for coal and gangue recognition, which has direct impact on accuracy of recognition. However, the existing feature extraction methods for coal and gangue are not ideal, and so a feature extraction method with multi-fractal is proposed based on energy field normalization for target recognition of coal and gangue in this paper. In the method, the concept of target energy field is established based on 3D grey surface, and the normalized target energy is calculated by using pixels. Then, after analysis of the feature extraction process, a feature extraction algorithm with multi-fractal is proposed based on energy field, in which 3D grey surface is divided by different grid forms, and the pixels in grid are counted to obtain the probability density distribution matrix of pixels. The results of multiple feature extraction is observed visually from probability density distribution, spatial feature distribution, and multi-fractal spectrum to illustrate the measurability of the method for target textures, which is the quantitative attribute of feature extraction. In the experiment, this method is used to quantitatively measure grey texture, and the effectiveness of measured features in coal and gangue recognition is compared with other methods. The experiment results show that the method can achieve effective quantitative measurement for coal and gangue texture, and the recognition accuracy is higher than other methods. [ABSTRACT FROM AUTHOR]

At present, the trend of familiarization of malicious code is becoming more and more obvious, and the research on the homology of malware (the classification of malicious code family) is of great significance for maintaining network security. In order to better express the overall characteristics of malicious code and improve the effect of detection and homology analysis, this paper proposes a method for detection and homology analysis of malware based on heterogeneous graphs of assembly instructions (AIHGAT). We take the assembly instructions of malicious families as the research object and analyze the importance and correlation of assembly instructions of different malicious families. The malware detection and homology analysis are carried out in three aspects: feature extraction, feature preprocessing, and model construction. In the feature extraction of malicious code, in order to alleviate the problem that it is difficult to extract static features of malicious samples that contain countermeasures such as packing and obfuscation, we obtain binary files from dynamic memory through sandbox and then, analyze its assembly instruction set. In feature preprocessing, we divide the assembly instructions into N-tuples and construct a heterogeneous graph based on assembly instructions according to the internal correlation of the gene sequence composed of the assembly N-grams features. Finally, in terms of model construction, we analyze the homology determination effect of the traditional graph neural network and construct the Graph Attention Network based on residual connection named ResGAT to analyze the homology of malicious code. The experimental results show that the ResGAT can gather the core characteristics of malicious families and enhance the ability to recognize malicious family variants. Our model has an accuracy rate of 98.83%, which is better than traditional machine learning detection methods, and can effectively determine the homology of malicious code families. [ABSTRACT FROM AUTHOR]

Background: Accurate prediction of the grade of invasive ductal carcinoma (IDC) before treatment is vital for individualized therapy and improving patient outcomes. This study aimed to develop and validate a mammography-based radiomics nomogram that would incorporate the radiomics signature and clinical risk factors in the preoperative prediction of the histological grade of IDC. Methods: The data of 534 patients from our hospital with pathologically confirmed IDC (374 in the training cohort and 160 in the validation cohort) were retrospectively analyzed. A total of 792 radiomics features were extracted from the patients' craniocaudal and mediolateral oblique view images. A radiomics signature was generated using the least absolute shrinkage and selection operator method. Multivariate logistic regression was adopted to establish a radiomics nomogram, the utility of which was evaluated using a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results: The radiomics signature was found to have a significant correlation with histological grade (P < 0.01), but the efficacy of the model is limited. The radiomics nomogram, which incorporated the radiomics signature and spicule sign into mammography, showed good consistency and discrimination in both the training cohort [area under the curve (AUC) = 0.75] and the validation cohort (AUC = 0.75). The calibration curves and DCA demonstrated the clinical usefulness of the proposed radiomics nomogram model. Conclusions: A radiomics nomogram based on the radiomics signature and spicule sign can be used to predict the histological grade of IDC and assist in clinical decision-making for patients with IDC. [ABSTRACT FROM AUTHOR]

Since diffusion-weighted imaging (DWI) images are high-dimensional medical images with rich texture features, existing traditional zero-watermarking algorithms cannot effectively protect the copyright of DWI images while satisfying the conditions of equilibrium and distinguishability. In this paper, a robust zero-watermarking algorithm for DWI images based on multiscale feature fusion is proposed to achieve effective copyright protection of DWI images. Firstly, a Siamese network for multiscale feature extraction based on the imaging characteristics of DWI images is proposed to extract robust features of two-dimensional slice sequences in each diffusion gradient direction by using different transforms. Next, to address the low distinguishability of extracted features due to the similar anatomical structure of different people under the same medical imaging mode, a variety of features based on texture and spatial correlations are integrated as a prior knowledge features into the network to enhance the distinguishability of zero-watermarking. Then, the dimensionality of the extracted features is reduced by using a clustering algorithm, and the key slice images are selected in each category of each diffusion gradient direction by using the clustering results. The eigenvalues of the eigenmatrix of the key slices are extracted by singular value decomposition to generate a binary feature map according to the gray average of the eigenvalue matrix. Finally, the binary feature map and the watermarking signals are modulated by logic chaos, and the zero-watermarking information is generated by performing the XOR operation. The experimental results show that the proposed algorithm is robust against various intentional or unintentional attacks on medical image processing, such as noise, filtering, JPEG compression and geometric attacks. The extraction accuracy rate of watermarking is above 97%, and it is more robust than traditional robust zero-watermarking methods. [ABSTRACT FROM AUTHOR]