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- 摘要
- 关键词
- 实验方案
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Texture features analysis on micro-structure of paste backfill based on image analysis technology; 基于图像识别技术的膏体微观结构纹理特征分析;
摘要： 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.
关键词： cement paste backfill,unconfined compressive strength,Tamura texture,microstructure,quantitative analysis,texture feature,GLCM feature
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Application of ANN and ANFIS for detection of brain tumors in MRIs by using DWT and GLCM texture analysis
摘要： In this work we combine different methodologies in order to develop algorithms for Computer-Aided Diagnosis (CAD) for brain tumors from the axial plane (T2 MRI). All methods utilize texture analysis by extracting features from raw data, without post-processing, based on different techniques, such as Gray Level Co-Occurrence Matrix (GLCM), or Discrete Wavelet Transform (DWT) and different classification methods, based on ANN or ANFIS. All of our proposed methodologies are developed, validated and verified on various sub data including 65% non-healthy MRIS. The total used database consists of 202 MRIs from non-healthy patients and 18 from healthy, segmented visually by an experienced neurosurgeon. Combining different subsets of features, our best results are by using 4 GLCM features for a 4 input and two hidden layers ANN, giving sensitivity 100%, specificity 77.8% accuracy 94.3%. It is proved that the input data to train such a CAD are considered to be unbiased if the ratio between healthy/un-healthy tissue MRIs is about 35%/65%, respectively.
关键词： MRI tumor CAD diagnosis,DWT,ANFIS,GLCM,ANN
更新于2025-09-23 15:23:52
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[IEEE 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) - Poznan, Poland (2018.9.19-2018.9.21)] 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) - On the influence of the image normalization scheme on texture classification accuracy
摘要： Texture can be a very rich source of information about the image. Texture analysis finds applications, among other things, in biomedical imaging. One of the widely used methods of texture analysis is the Gray Level Co-occurrence Matrix (GLCM). Texture analysis using the GLCM method is most often carried out in several stages: determination of areas of interest, normalization, calculation of the GLCM, extraction of features, and finally, the classification. Values of the GLCM based features depend on the choice of the normalization method, which was examined in this work. The normalization is necessary, since acquired images often suffer from noise and intensity artifacts. Certainly, the normalization will not eliminate these two effects, however it was demonstrated, that its application improves texture analysis accuracy. The aim of the work was to analyze the influence of different normalization methods on the discriminating ability of features estimated from the GLCM. The analysis was performed both for Brodatz textures and real magnetic resonance data. Brodatz textures were corrupted by three types of distortion: intensity nonuniformity, Gaussian noise and Rician Noise. Three types of normalizations were tested: min?max, 1?99% and +/?3σ.
关键词： normalization,classification,image processing,texture analysis,GLCM
更新于2025-09-23 15:23:52
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On the performance improvement of non-cooperative iris biometrics using segmentation and feature selection techniques
摘要： In this work, an improved segmentation methodology and a novel feature selection algorithm are proposed. From the input eye image, iris boundary is identified using Circular Hough Transform. A bounding box is defined using the radius obtained followed by iterative thresholding techniques to eliminate specular reflections, eyelids, eyelashes and pupil region. First-order and second-order statistical features are extracted from the segmented iris. For the first time, the statistical measure, Chi-square value is computed from GLCM as a new novel feature from iris images. Statistical dependency-based backward feature selection (SDBFS) algorithm is used to reduce the feature vector size. By operating on local features in reduced search space, computation complexity of segmentation is reduced with less mislocalisation count and eliminates pupil dilation effects. Results of SDBFS show the usefulness of minimal-useful features. Experimental results conducted on CASIA V1, V3-interval and UBIRIS V1 datasets show that statistical features in non-ideal iris images outperform some of the state-of-the-art methods.
关键词： backward feature selection,chi-square value,grey level co-occurrence matrix,iris recognition,GLCM,statistical dependency,Circular Hough Transform,segmentation
更新于2025-09-23 15:22:29
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A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
摘要： Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information. We characterise the spatial information by the texture features extracted from the grey level cooccurrence matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. For classification, we use support vector machine (SVM). The experiments are performed on three well-known hyperspectral benchmark datasets. The proposed algorithm is compared with the state of the art methods. The obtained results of this fusion show that our method outperforms the other approaches by increasing the classification accuracy in a good timing. This method may be improved for more performance.
关键词： hyperspectral images,spectral and spatial features,classification,SVM,mutual information,GLCM,grey level cooccurrence matrix,support vector machine
更新于2025-09-23 15:21:21
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Content Based Image Retrieval Using Gray Level Co-Occurance Matrix with SVD and Local Binary Pattern
摘要： In this paper, gray level co-occurrence matrix, gray level co-occurrence matrix with singular value decomposition and local binary pattern are presented for content based image retrieval. Based upon the feature vector parameters of energy, contrast, entropy and distance metrics such as Euclidean distance, Canberra distance, Manhattan distance the retrieval efficiency, precision, and recall of the images are calculated. The retrieval results of the proposed method are tested on Corel-1k database. The results after being investigated shows a significant improvement in terms of average retrieval rate, average retrieval precision and recall of different algorithms such as GLCM, GLCM & SVD, LBP with radius one and LBP with radius two based on different distance metrics.
关键词： GLCM,SVD and LBP,CBIR
更新于2025-09-23 15:21:21
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Cloud Masking Technique for High-Resolution Satellite Data: An Artificial Neural Network Classifier Using Spectral & Textural Context
摘要： Cloud masking is a very important application in remote sensing and an essential pre-processing step for any information derivation applications. It helps in estimation of usable portion of the images. Many popular spectral classi?cation techniques rely upon the presence of a short-wave infrared band or bands of even higher wavelength to differentiate between clouds and other land covers. However, these methods are limited to sensors equipped with higher wavelength bands. In this paper, a generic and ef?cient technique is attempted using the Cartosat-2 series (C2S) satellite which is having high-resolution multispectral sensor in the visible and near-infrared bands. The methodology is based on textural features from the available spectral context, and using a feedforward neural network for the classi?cation is proposed. The method was shown to have an overall accuracy of 97.98% for a large manually pre-classi?ed validation dataset with more than 2 million data points. Experimental results and cloud masks generated for various scenes show that the method may be viable as a reasonable cloud masking algorithm for C2S data.
关键词： Cloud masking,Feed forward network,High-resolution satellite data,Image classi?cation,Arti?cial neural network,GLCM texture
更新于2025-09-23 15:21:01
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Experimental Multiphase Estimation in an Integrated Reconfigurable Multi-Arm Interferometer
摘要： The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and ?ow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classi?cation steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results con?rm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classi?cation process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.
关键词： object-based image analysis (OBIA),textural rule-based classi?cation,gray-level cooccurrence matrix (GLCM),landslide mapping,remote sensing,GIScience
更新于2025-09-16 10:30:52
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - A New Strategy to Detect Lung Cancer on CT Images
摘要： Lung cancer has a very low cure rate in the advanced stages, with effective early detection, the survival rate of lung cancer could be highly raised. Detection of lung cancer in the early stages plays a vital role for human health. Computed tomography (CT) images, which provide electronic densities of tissues, are widely applied in radiotherapy planning. The proposed system based on CT technology consists of image acquisition, preprocessing, feature extraction, and classification. In the preprocessing stage, RGB images are converted to grayscale images, the median filter and the Wiener filter are used to uproot noises, Otsu thresholding method is applied to convert CT images, and REGIONPROPS function is used to exact body region from binary images. In the feature extraction stage, features, like Contrast, Correlation, Energy, Homogeneity, are extracted through statistic method Gray Level Co-occurrence Matrix (GLCM). In the final stage, extracted features, together with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN), are used to identify lung cancer from CT images. The performance of the proposed system shows satisfactory results of 96.32% accuracy on SVM and 83.07% accuracy on BPNN respectively.
关键词： BPNN,SVM,image processing,lung cancer detection,GLCM
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - SAR Image Matching Improvement Using Image Texture Analysis
摘要： Matching in high-resolution synthetic aperture radar (SAR) images while being more complicated compared to optical images, is especially important due to its numerous applications. The main aim of current research is to determine improvement of SAR image matching process by deploying texture analysis using gray level’s co-occurrence matrix (GLCM). Three parts of the pair of TerraSAR-X images are used to implement the methodology. The results show that for some areas with low texture, the conventional image matching algorithm is not able to detect corresponding points, while using other textural features in image matching process leads to improvement in quantity of acceptable matched points.
关键词： GLCM,textural feature,optical flow,SAR image matching
更新于2025-09-10 09:29:36