Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-...Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.展开更多
According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GL...According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.展开更多
Surgical excision is an effective treatment for oral squamous cell carcinoma(OSCC),but exact intraoperative differentiation OSCC from the normal tissue is the first premise.As a noninvasive imaging technique,optical c...Surgical excision is an effective treatment for oral squamous cell carcinoma(OSCC),but exact intraoperative differentiation OSCC from the normal tissue is the first premise.As a noninvasive imaging technique,optical coherence tomography(OCT)has the nearly same resolution as the histopathological examination,whose images contain rich information to assist surgeons to make clinical decisions.We extracted kinds of texture features from OCT images obtained by a home-made swept-source OCT system in this paper,and studied the identification of OSCC based on different combinations of texture features and machine learning classifiers.It was demonstrated that different combinations had different accuracies,among which the combination of texture features,gray level co-occurrence matrix(GLCM),Laws'texture measnres(LM),and center symmetric auto-correlation(CSAC),and SVM as the classifier,had the optimal comprehensive identification effect,whose accuracy was 94.1%.It was proven that it is feasible to distinguish OSCC based on texture features in OCT images,and it has great potential in helping surgeons make rapid and accurate decisions in oral clinical practice.展开更多
Objective To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery(T2-FLAIR)images.Methods We acquired cerebral 3 D T2-FLAIR images of thirty patients who were ...Objective To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery(T2-FLAIR)images.Methods We acquired cerebral 3 D T2-FLAIR images of thirty patients who were diagnosed with ischemic white matter lesion(WML)with MR-1.5 T and MR-3.0 T scanners.Histogram texture features which included mean signal intensity(Mean),Skewness and Kurtosis,and gray level co-occurrence matrix(GLCM)texture features which included angular second moment(ASM),Contrast,Correlation,Inverse difference moment(IDM)and Entropy,of regions of interest located in the area of WML and normal white matter(NWM)were measured by ImageJ software.The texture parameters acquired with MR-1.5 T scanning were compared with MR-3.0 T scanning.Results The Mean of both WML and NWM obtained with MR-1.5 T scanning was significantly lower than that acquired with MR-3.0 T(P<0.001),while Skewness and Kurtosis between MR-1.5 T and MR-3.0 T scanning showed no significant difference(P>0.05).ASM,Correlation and IDM of both WML and NWM acquired with MR-1.5 T revealed significantly lower values than those with MR-3.0 T(P<0.001),while Contrast and Entropy acquired with MR-1.5 T showed significantly higher values than those with MR-3.0 T(P<0.001).Conclusion MR field strength showed no significant effect on histogram textures,while had significant effect on GLCM texture features of cerebral T2-FLAIR images,which indicated that it should be cautious to explain the texture results acquired based on the different MR field strength.展开更多
Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This stud...Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.展开更多
Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing...Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.展开更多
Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the proces...Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.展开更多
Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in moun...Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.展开更多
The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relati...The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relationship between strength and pore structure(e.g.,pore size and its distribution)were performed,but the micro-morphology characteristics have been rarely concerned.Texture describing the surface properties of the sample is a global feature,which is an effective way to quantify the micro-morphological properties.In statistical analysis,GLCM features and Tamura texture are the most representative methods for characterizing the texture features.The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope,respectively.The texture features of different SEM images were calculated based on image analysis technology,and then the correlation between these parameters and the strength was analyzed.It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB.There is a significant correlation between the texture features and the unconfined compressive strength,and the prediction of strength is feasible using texture parameters of the CPB microstructure.展开更多
To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage ...To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.展开更多
Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of vis...Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.展开更多
In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake media.DeepFakes,a deep learning based forgery technology,can tamper with the face ...In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake media.DeepFakes,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes.The spread of face manipulation videos is very easy to bring fake information.Therefore,it is important to develop effective detection methods to verify the authenticity of the videos.Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are insufficient.Therefore,in this paper,a new method is proposed to detect DeepFake videos.Firstly,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face region.Then,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame level.The experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.展开更多
In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture ...In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.展开更多
Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level S...Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.展开更多
Purpose-Extracting suitable features to represent an image based on its content is a very tedious task.Especially in remote sensing we have high-resolution images with a variety of objects on the Earth’s surface.Maha...Purpose-Extracting suitable features to represent an image based on its content is a very tedious task.Especially in remote sensing we have high-resolution images with a variety of objects on the Earth’s surface.Mahalanobis distance metric is used to measure the similarity between query and database images.The low distance obtained image is indexed at the top as high relevant information to the query.Design/methodology/approach-This paper aims to develop an automatic feature extraction system for remote sensing image data.Haralick texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree(QT)decomposition are developed as feature set to represent the input data.The extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user interface.Findings-The developed retrieval system performance has been analyzed using precision and recall and F1 score.The proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction methods.Originality/value-The main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT decomposition.The features required to represent the image is 207 which is very less dimension compare to other texture methods.The performance shows superior than the other state of art methods.展开更多
Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries.This study purposes a bronchoscopic lumen feature matching network(BLFM-Net)based on deep learning to address the ...Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries.This study purposes a bronchoscopic lumen feature matching network(BLFM-Net)based on deep learning to address the challenges of image noise,anatomical complexity,and the stringent real-time requirements.The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules.The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing.The feature extraction module derives multi-dimensional features,such as centroids,area,and shape descriptors,from dehazed images.The Faster RCNN Object detection module detects bronchial regions of interest and generates bounding boxes to localize key areas.The feature matching module accelerates the process by combining detection boxes,extracted features,and a KD-Tree(K-Dimensional Tree)-based algorithm,ensuring efficient and accurate regional feature associations.The BLFM-Net was evaluated on 5212 bronchoscopic images,demonstrating superior performance compared to traditional and other deep learning-based image matching methods.It achieved real-time matching with an average frame time of 6 ms,with a matching accuracy of over 96%.The method remained robust under challenging conditions including frame dropping(0,5,10,20),shadowed regions,and variable lighting,maintaining accuracy of above 94%even with the frame dropping of 20.This study presents BLFM-Net,a deep learning-based matching network designed to enhance and match bronchial features in bronchoscopic images.The BLFM-Net shows improved accuracy,real-time performance,and reliability,making a valuable tool for bronchoscopic surgeries.展开更多
This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture f...This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.展开更多
Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes resea...Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.展开更多
In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to t...In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.展开更多
Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divid...Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.展开更多
基金The National Key Research and Development Program of China under contract No.2023YFC3008204the National Natural Science Foundation of China under contract Nos 41977302 and 42476217.
文摘Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.
基金supported by China Postdoctoral Science Foundation(No.20110491510)Program for Liaoning Excellent Talents in University(No.LJQ2011027)+1 种基金Anshan Science and Technology Project(No.2011MS11)Special Research Foundation of University of Science and Technology of Liaoning(No.2011zx10)
文摘According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.
基金This study was supported by the National Natural Science Foundation of China(No.61875092)Science and Technology Support Program of Tianjin(17YFZCSY00740)+1 种基金the Beijing-Tianjin-Hebei Basic Research Cooperation Special Program(19JCZDJC65300)the Fundamental Research Funds for the Central Universities,Nankai University(63201178).
文摘Surgical excision is an effective treatment for oral squamous cell carcinoma(OSCC),but exact intraoperative differentiation OSCC from the normal tissue is the first premise.As a noninvasive imaging technique,optical coherence tomography(OCT)has the nearly same resolution as the histopathological examination,whose images contain rich information to assist surgeons to make clinical decisions.We extracted kinds of texture features from OCT images obtained by a home-made swept-source OCT system in this paper,and studied the identification of OSCC based on different combinations of texture features and machine learning classifiers.It was demonstrated that different combinations had different accuracies,among which the combination of texture features,gray level co-occurrence matrix(GLCM),Laws'texture measnres(LM),and center symmetric auto-correlation(CSAC),and SVM as the classifier,had the optimal comprehensive identification effect,whose accuracy was 94.1%.It was proven that it is feasible to distinguish OSCC based on texture features in OCT images,and it has great potential in helping surgeons make rapid and accurate decisions in oral clinical practice.
文摘Objective To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery(T2-FLAIR)images.Methods We acquired cerebral 3 D T2-FLAIR images of thirty patients who were diagnosed with ischemic white matter lesion(WML)with MR-1.5 T and MR-3.0 T scanners.Histogram texture features which included mean signal intensity(Mean),Skewness and Kurtosis,and gray level co-occurrence matrix(GLCM)texture features which included angular second moment(ASM),Contrast,Correlation,Inverse difference moment(IDM)and Entropy,of regions of interest located in the area of WML and normal white matter(NWM)were measured by ImageJ software.The texture parameters acquired with MR-1.5 T scanning were compared with MR-3.0 T scanning.Results The Mean of both WML and NWM obtained with MR-1.5 T scanning was significantly lower than that acquired with MR-3.0 T(P<0.001),while Skewness and Kurtosis between MR-1.5 T and MR-3.0 T scanning showed no significant difference(P>0.05).ASM,Correlation and IDM of both WML and NWM acquired with MR-1.5 T revealed significantly lower values than those with MR-3.0 T(P<0.001),while Contrast and Entropy acquired with MR-1.5 T showed significantly higher values than those with MR-3.0 T(P<0.001).Conclusion MR field strength showed no significant effect on histogram textures,while had significant effect on GLCM texture features of cerebral T2-FLAIR images,which indicated that it should be cautious to explain the texture results acquired based on the different MR field strength.
基金Supported by the National Key Basic Research Development Pro-gram (2009CB421302 )National Natural Science Foundation ofChina (40861020,40961025,40901163)+1 种基金Natural Science Foun-dation of Xinjiang (200821128 )Open Foundation of State KeyLaboratory of Resources and Environment Information ystems(2010KF0003SA)
文摘Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.
基金This research was funded by the Faculty Program Grant(GPF096C-2020),University of Malaya,Malaysia.
文摘Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R236),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.
基金supported jointly by Key Laboratory of Geo-special Information Technology, Ministry of Land and Resources (Grant No. KLGSIT2013-12)Knowledge Innovation Program (Grant No. KSCX1-YW-09-01) of Chinese Academy of Sciences
文摘Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.
基金Project(51722401)supported by the National Natural Science Foundation for Excellent Young Scholars of ChinaProject(FRF-TP-18-003C1)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(51734001)supported by the Key Program of National Natural Science Foundation of China
文摘The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relationship between strength and pore structure(e.g.,pore size and its distribution)were performed,but the micro-morphology characteristics have been rarely concerned.Texture describing the surface properties of the sample is a global feature,which is an effective way to quantify the micro-morphological properties.In statistical analysis,GLCM features and Tamura texture are the most representative methods for characterizing the texture features.The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope,respectively.The texture features of different SEM images were calculated based on image analysis technology,and then the correlation between these parameters and the strength was analyzed.It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB.There is a significant correlation between the texture features and the unconfined compressive strength,and the prediction of strength is feasible using texture parameters of the CPB microstructure.
基金funding by the National Natural Science Foundation of China(Nos.51474039 and 51404046)the Project of Shanxi Provincial Federation of Coalbed Methane Research(No.2013012010)the Science Foundation of North University of China(No.XJJ2016033)
文摘To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.
基金Supported by the National Natural Science Foundation of China (No. 61032001, No.61002045)
文摘Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.
基金supported by the National Natural Science Foundation of China(Nos.U2001202,62072480,U1736118)the National Key R&D Program of China(Nos.2019QY2202,2019QY(Y)0207)+1 种基金the Key Areas R&D Program of Guangdong(No.2019B010136002)the Key Scientific Research Program of Guangzhou(No.201804020068).
文摘In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake media.DeepFakes,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes.The spread of face manipulation videos is very easy to bring fake information.Therefore,it is important to develop effective detection methods to verify the authenticity of the videos.Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are insufficient.Therefore,in this paper,a new method is proposed to detect DeepFake videos.Firstly,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face region.Then,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame level.The experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.
文摘In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.
文摘Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.
基金Satellite Application Centre partially funds this project,Indian Space Research Organization(ISRO)under the grant No:ISRO/RES/3/789/18-19.The authors are thankful to the agency for supporting this research.
文摘Purpose-Extracting suitable features to represent an image based on its content is a very tedious task.Especially in remote sensing we have high-resolution images with a variety of objects on the Earth’s surface.Mahalanobis distance metric is used to measure the similarity between query and database images.The low distance obtained image is indexed at the top as high relevant information to the query.Design/methodology/approach-This paper aims to develop an automatic feature extraction system for remote sensing image data.Haralick texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree(QT)decomposition are developed as feature set to represent the input data.The extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user interface.Findings-The developed retrieval system performance has been analyzed using precision and recall and F1 score.The proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction methods.Originality/value-The main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT decomposition.The features required to represent the image is 207 which is very less dimension compare to other texture methods.The performance shows superior than the other state of art methods.
基金funded by the National Natural Science Foundation of China(Grant No.52175028).
文摘Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries.This study purposes a bronchoscopic lumen feature matching network(BLFM-Net)based on deep learning to address the challenges of image noise,anatomical complexity,and the stringent real-time requirements.The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules.The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing.The feature extraction module derives multi-dimensional features,such as centroids,area,and shape descriptors,from dehazed images.The Faster RCNN Object detection module detects bronchial regions of interest and generates bounding boxes to localize key areas.The feature matching module accelerates the process by combining detection boxes,extracted features,and a KD-Tree(K-Dimensional Tree)-based algorithm,ensuring efficient and accurate regional feature associations.The BLFM-Net was evaluated on 5212 bronchoscopic images,demonstrating superior performance compared to traditional and other deep learning-based image matching methods.It achieved real-time matching with an average frame time of 6 ms,with a matching accuracy of over 96%.The method remained robust under challenging conditions including frame dropping(0,5,10,20),shadowed regions,and variable lighting,maintaining accuracy of above 94%even with the frame dropping of 20.This study presents BLFM-Net,a deep learning-based matching network designed to enhance and match bronchial features in bronchoscopic images.The BLFM-Net shows improved accuracy,real-time performance,and reliability,making a valuable tool for bronchoscopic surgeries.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60573172 and 60973152)the Superior University Doctor Subject Special Scientific Research Foundation of China (Grant No. 20070141014)the Natural Science Foundation of Liaoning Province of China (Grant No. 20082165)
文摘This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2022 Yeungnam University Research Grant.
文摘Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.
基金Supported by the Major Program of National Natural Science Foundation of China (No. 70890080 and No. 70890083)
文摘In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.
基金This work was supported by the National Natural Science Foundation of China[grant number 41101410]the Comprehensive Transportation Applications of High-resolution Remote Sensing program[grant number 07-Y30B10-9001-14/16]+1 种基金the Key Laboratory of Surveying Mapping and Geoinformation in Geographical Condition Monitoring[grant number 2014NGCM]the Science and Technology Plan of Sichuan Bureau of Surveying,Mapping and Geoinformation,China[grant number J2014ZC02].
文摘Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.
