Uyghur document image retrieval based on gray gradient co-occurrence matrix

Ubul, Kurban; Yadikar, Nurbuya; Amat, Ayxamgul; Aysa, Alimjan; Yibulayin, Tuergen

doi:10.1109/cac.2015.7382600

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The color moment is a statistic used to describe image color features. For the texture features, the Gray Level Co-occurrence Matrix (GLCM) [ 51 ], Gray-Gradient Co-occurrence Matrix (GGCM) [ 52 ], Gray Level Difference Method (GLDM) [ 53 ], and Tamura texture [ 54 ] were selected. A GLCM describes the texture features of an image by calculating the gray level co-occurrence relationships between adjacent pixels in the image and counting the frequency of occurrence of these co-occurrence relationships in different directions and distances.…”

Section: Methodsmentioning

confidence: 99%

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

Chen,

Lin,

Sun

et al. 2024

Plants

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Ginkgo biloba L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a Ginkgo biloba. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling–background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants.

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Section: Methodsmentioning

confidence: 99%

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

Chen,

Lin,

Sun

et al. 2024

Plants

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“…The textural features are computed based on the graylevel gradient co-occurrence matrix (GGCM) proposed in Ubul et al [34]. Instead of only considering the graylevel distribution as in the gray-level co-occurrence matrix, GGCM considers the co-occurrence of specific gray-levels and gradient magnitudes.…”

Section: Feature Extraction Dimensionality Reduction and Classificationmentioning

confidence: 99%

“…GGCM parameters were derived based on the two-dimensional histogram h(L, Grad), where L denotes the gray level and Grad denotes the gradient magnitude. The following 15 parameters derived from h were extracted for each cell: small gradient dominance, large gradient dominance, gray asymmetry, gradient asymmetry, energy, gray-level mean, gradient mean, gray-level variance, gradient variance, correlation, gray-level entropy, gradient entropy, mixed entropy, inertia and homogeneity, as defined in Table 1 of [34].…”

Section: Feature Extraction Dimensionality Reduction and Classificationmentioning

confidence: 99%

Identification of Retinal Ganglion Cells from β-III Stained Fluorescent Microscopic Images

et al. 2020

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Optic nerve crush in mouse model is widely used for investigating the course following retinal ganglion cell (RGCs) injury. Manual cell counting from β-III tubulin stained microscopic images has been routinely performed to monitor RGCs after an optic nerve crush injury, but is time-consuming and prone to observer variability. This paper describes an automatic technique for RGC identification. We developed and validated (i) a sensitive cell candidate segmentation scheme and (ii) a classifier that removed false positives while retaining true positives. Two major contributions were made in cell candidate segmentation. First, a homomorphic filter was designed to adjust for the inhomogeneous illumination caused by uneven penetration of β-III tubulin antibody. Second, the optimal segmentation parameters for cell detection are highly imagespecific. To address this issue, we introduced an offline-online parameter tuning approach. Offline tuning optimized model parameters based on training images and online tuning further optimized the parameters at the testing stage without needing access to the ground truth. In the cell identification stage, 31 geometric, statistical and textural features were extracted from each segmented cell candidate, which was subsequently classified as true or false positives by support vector machine. The homomorphic filter and the online parameter tuning approach together increased cell recall by 28%. The entire pipeline attained a recall, precision and coefficient of determination (r 2 ) of 85.3%, 97.1% and 0.994. The availability of the proposed pipeline will allow efficient, accurate and reproducible RGC quantification required for assessing the death/survival of RGCs in disease models.

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Three-dimensional elemental imaging of material surface using image-assisted laser-induced breakdown spectroscopy

Deng

Chu

et al. 2020

Applied Surface Science

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Uyghur document image retrieval based on gray gradient co-occurrence matrix

Cited by 3 publications

References 12 publications

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning

Identification of Retinal Ganglion Cells from β-III Stained Fluorescent Microscopic Images

Three-dimensional elemental imaging of material surface using image-assisted laser-induced breakdown spectroscopy

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