Development of a Gray-Level Co-Occurrence Matrix-Based Texture Orientation Estimation Method and Its Application in Sea Surface Wind Direction Retrieval From SAR Imagery

Zheng, Gang; Li, Xiaofeng; Zhou, Lei; Yang, Jianguo; Ren, Lin; Chen, Peng; Zhang, Huaguo; Lou, Xiulin

doi:10.1109/tgrs.2018.2812778

Cited by 49 publications

(24 citation statements)

References 54 publications

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“…Gray-Level Co-Occurrence Matrix (GLCM) digunakan karena metode ini bekerja untuk data citra gray. GLCM pada dasarnya adalah distribusi probabilitas gabungan tingkat abu-abu pada pasangan posisi yang memuaskan posisi relatif tertentu dalam suatu citra [13]. GLCM yang digunakan sebanyak 5 fitur antara lain fitur entropy, correlation, contrast, homogeneity dan variance.…”

Section: Ekstraksi Fitur Glcmunclassified

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni

Santoso

2020

SINTECH Journal

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In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

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Section: Ekstraksi Fitur Glcmunclassified

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni

Santoso

2020

SINTECH Journal

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“…The galactic co-occurrence matrix [34][35][36] is defined as the probability from grey-level i to a fixed position d = (Dx, Dy) to the grey-level j. The grey-level co-occurrence matrix is denoted by P d (i, j)(i, j = 0, 1, 2, .…”

Section: Grey-level Co-occurrence Matrix and Its Correlation Featuresmentioning

confidence: 99%

Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

Ding

Liu

2019

Applied Sciences

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Volumetric tooth wear measurement is important to assess the life of scraper conveyor sprocket. A shape from focus-based method is used to measure scraper conveyor sprocket tooth wear. This method reduces the complexity of the process and improves the accuracy and efficiency of existing methods. A prototype set of sequence images taken by the camera facing the sprocket teeth is collected by controlling the fabricated track movement. In this method, a normal distribution operator image filtering is employed to improve the accuracy of an evaluation function value calculation. In order to detect noisy pixels, a normal operator is used, which involves with using a median filter to retain as much of the original image information as possible. In addition, an adaptive evaluation window selection method is proposed to address the difficulty associated with identifying an appropriate evaluation window to calculate the focused evaluation value. The shape and size of the evaluation window are autonomously determined using the correlation value of the grey scale co-occurrence matrix generated from the measured pixels' neighbourhood pixels. A reverse engineering technique is used to quantitatively verify the shape volume recovery accuracy of different evaluation windows. The test results demonstrate that the proposed method can effectively measure sprocket teeth wear volume with an accuracy up to 97.23%. discussed the wear condition of a driving sprocket and the influence of wear on the sliding distance by taking the sliding speed and sliding distance of the meshing process as the index. Wang et al. [8] also analyzed the relationship between the deformation of a ring chain and driving sprocket wear by combining numerical analysis with experiments. However, these methods are not only tedious and time-consuming, they are also not sufficiently accurate or efficient.The research of computer vision in industry field has attracted more and more attention of many researchers. Alverdi et al. [9] proposed a new way of using images to model the kerf profile in abrasive water jet milling. Qian et al. [10] presented an algorithm to compute the axis and generatrix focus on complex surfaces or irregular surfaces. A new monitoring technique for burr detection was proposed for the optimization of drill geometry and process parameters [11]. In addition, as a relatively simple and practical 3D reconstruction technology, shape from focus (SFF) has been applied to tool wear measurements [12,13], LCD/TFT (Liquid Crystal Display/Thin-Film Technology) display manufacturing [14] and grinding wheel surface morphology [15], etc.To realize 3D surface topography restoration, in 1988, Darrell et al. [16] proposed using a Laplace operator-Gauss fitting method to search the clear frame of pixels in the sequence partial focus image according to image focusing information. In the 1990s, Nayar et al. [17,18] proposed an SFF-based method and obtained the height information of the corresponding surface of the window image by searching the image position cor...

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“…A simple but cost-effective towed video camera system was used to collect the geolocated images. Three approaches, the BOF [41,42] technique, HSV [43,44] color features, and GLCM [45,46] texture features were tested to extract attributes from these images for the semiautomatic classification of benthic cover. Moreover, a detailed analysis was conducted to identify the extracted attributes that would best increase the discrimination capability of the classifiers.…”

Section: Introductionmentioning

confidence: 99%

Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

2020

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The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

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Development of a Gray-Level Co-Occurrence Matrix-Based Texture Orientation Estimation Method and Its Application in Sea Surface Wind Direction Retrieval From SAR Imagery

Cited by 49 publications

References 54 publications

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

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