Grey-Level Cooccurrence Matrix Performance Evaluation for Heading Angle Estimation of Moveable Vision System in Static Environment

Zainal, Zairulazha; Ramli, Rizauddin; Mustafa, Mohd. Marzuki

doi:10.1155/2013/624670

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Figure 1 shows two examples of our dataset. Figure 1 It is used to find out the texture feature of an image [16]. Except GLCM, there are other commonly-used features, for example, the wavelet features [17][18][19][20][21][22].…”

Section: Methodsmentioning

confidence: 99%

Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine

Wen¹,

Chen²,

Miao³

et al. 2018

Proceedings of the 8th International Conference on Education, Management, Information and Management Society (EMIM 2018)

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Abstract. The diagnosis of gingivitis often occurs years later by using a series of conventional oral examination, and they depended a lot on dental records which are physically and mentally laborious task for dentists. In this study, our research presented a new method to diagnose gingivitis, which is based on gray-level cooccurrence matrix (GLCM) and extreme learning machine (ELM). The experiments demonstrate that this method is more accurate and sensitive than two state-of-the-art approaches: naï ve Bayes classifier and wavelet energy.

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Section: Methodsmentioning

confidence: 99%

Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine

Wen¹,

Chen²,

Miao³

et al. 2018

Proceedings of the 8th International Conference on Education, Management, Information and Management Society (EMIM 2018)

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“…Grey-Level Co-occurrence Matrix (GLCM) known as grey-level spatial dependence matrix is a statistical method used for inspecting the spatial relationship between the pixels in the two dimensional grid (Vargas et al 2011). GLCM is the measure of occurrence of different combinations of the grey levels (intensity) appear in the image (Zainal et al 2013). Information about the spatial distribution and tonal variation can be estimated based on different textures of the features.…”

Section: Grey Level Co-occurrence Matrixmentioning

confidence: 99%

Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study over Adyar watershed, India

Sundaram

Devaraj

Yarrakula³

2023

Environ Sci Pollut Res

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Urban oods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban oods are frequently observed in many parts of the world. Flood events in Chennai city is a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover is focused in the present study. The present study aims at mapping ooded region using Sentinel 1 datasets over Chennai city. Series of Sentinel-1A image is collected before, during and after oods for mapping the extent of ood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of ooded extent from the SAR datasets. Analysis performed over the Chennai city provided promising results in the extraction of ooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of ooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of ooded pixels.

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“…It is a two-dimensional square matrix that gives a spatial arrangement between neighboring pixels. 32 This matrix is formed by finding the number of specified pixel pairs in the input image. The dimension of GLCM is V x × V x , where V x is the number of gray-level pixels in each row of an image.…”

Section: Textural Features Extractionmentioning

confidence: 99%

Classification of neovascularization on retinal images using extreme learning machine

Pappu¹,

Biswal²,

Gandhi

et al. 2020

Int J Imaging Syst Tech

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Proliferative diabetic retinopathy is the advanced stage of diabetic retinopathy (DR) resulting in the growth of abnormal vessels on the retinal surface termed as neovascularization. This article primarily deals with the timely detection and classification of retinal images into healthy, neovascularization on optic disc, and elsewhere using an extreme learning machine (ELM) classifier. Initially, a binary mask is employed to enhance the foreground pixels. The resultant image is processed for the extraction of a retinal vessel map using Hessian-based Frangi filter. For the classification of retinal images, an optimal feature set of 14 features including seven-moment invariant-based features are extracted from the vascular map using sequential recursive feature elimination algorithm. Further, the training of the ELM classifier is carried out using the K-fold cross-validation technique to improve the performance of the classifier.The proposed method achieves an average accuracy of 98% and an average error rate of less than 0.005 when tested on different globally accessible datasets. Apart from the different statistical results like sensitivity, specificity, and area under curve are estimated as 98.5%, 100%, and 94.2%, respectively, for validating the algorithm. Comparison results reveal that the ELM classifier outperforms the SVM classifier in terms of accuracy and error rate.

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Grey-Level Cooccurrence Matrix Performance Evaluation for Heading Angle Estimation of Moveable Vision System in Static Environment

Cited by 5 publications

References 13 publications

Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine

Gingivitis Identification via Grey-level Cooccurrence Matrix and Extreme Learning Machine

Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study over Adyar watershed, India

Classification of neovascularization on retinal images using extreme learning machine

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