Performance analysis of Gabor wavelet for extracting most informative and efficient features

Malathi, T.; Bhuyan, M. K.

doi:10.1007/s11042-016-3414-2

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…The frequency and direction of a Gabor filter are similar to those of the human visual system, which is particularly suitable for texture representation and discrimination. The Gabor filter is the result of the convolution of a Gaussian function and complex sine function in the Fourier domain . The representation of the 2D Gabor filter in 3D space is shown in Figure .…”

Section: Flame Image Texture Feature Extraction Based On Gabor-glcmmentioning

confidence: 99%

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Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O₂ Content and NO_x

Yang

et al. 2022

ACS Omega

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Flame image feature extraction is the basis for boiler combustion monitoring and control. The flame video images of recent research are mainly derived from experimental burners in the laboratory, and few pay attention to the flame images in industrial boilers. The actual industrial boiler flame images differ significantly from the laboratory flame images. Additionally, certain flame image features cannot be captured in the laboratory owing to the limitations of the camera installations. Therefore, a flame image texture feature extraction algorithm based on an industrial boiler is proposed in this paper. The texture features were enhanced using a Gabor filter for the RGB channels of the flame images, and then, the statistics of the texture features were scalarized by a gray-level co-occurrence matrix (GLCM). The data were filtered and downscaled by a data compressor consisting of Gaussian-weighted mean and principal component analysis (PCA) to obtain eight key variables. The extracted eight variables were verified to be effective in characterizing the O2 and NO x contents of flue gas using the mutual information method. The combustion process regression model was constructed using a gated recurrent unit (GRU) on the 8 h combustion data of the boiler, and the predicted mean absolute percentage error (MAPE) for O2 and NO x content in the test set reached 7.5 and 10.2%, respectively. Compared to the conventional methods of direct PCA on images and GLCM plus PCA on images, the MAPE for O2 content prediction was reduced by 12.3 and 7.3%, and the MAPE for NO x content prediction was reduced by 10.5 and 6.1%, respectively. The advantage of the new flame feature based on Gabor-GLCM is suitable for the subsequent analysis and control of an industrial combustion system.

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Section: Flame Image Texture Feature Extraction Based On Gabor-glcmmentioning

confidence: 99%

“…The Gabor filter is the result of the convolution of a Gaussian function and complex sine function in the Fourier domain. 22 The representation of the 2D Gabor filter in 3D space is shown in Figure 4.…”

Section: Flame Image Texture Feature Extractionmentioning

confidence: 99%

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O₂ Content and NO_x

Yang

et al. 2022

ACS Omega

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“…Em (c) tanto a informação espacial quanto a espectral estão presentes. Diversas aplicações multimídia utilizam a extração de características com wavelet de Gabor, tais como reconhecimento facial, classificação de textura de imagem e indexação de imagens [12].…”

Section: A Extração De Características Com a Transformada Wavelet Deunclassified

Comparação de Classificadores para a Estimação de Posicionamento de Cabeça

Nascimento¹,

Zampolo²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Resumo-Alvo de diversas pesquisas, a estimação de posicionamento de cabeça tem por objetivo determinar a posição de um indivíduo em uma determinada cena. Estratégias de análise de vídeo para estimação de posição de cabeça que funcionem com webcams convencionais têm um papel central em sistemas de rastreamento ocular de baixo-custo. Neste trabalho, os autores comparam três classificadores (baseados em kernel principal component analysis, KPCA; linear discriminant analysis, LDA; e support vector classification, SVC) para estimar a posição de cabeça em uma base de imagens contendo poses discretas. A extração de características de entrada dos classificadores utiliza transformada wavelet de Gabor, cujos aspectos conceituais básicos são apresentados no texto. Testes utilizando uma base de dados disponível publicamente na internet foram realizados. Os resultados mostram que os classificadores possuem acurácia superior a 70% para número total de classes não superior a 15. À medida em que o número de classes aumenta, o desempenho dos classificadores deteriora significativamente: para 93 classes, por exemplo, as acurácias medidas foram de 34%, 33% e 43% para os classificadores baseados em KPCA, LDA, e SVC, respectivamente. Palavras-chave-Estimação de pose de cabeça. Wavelet de Gabor. KPCA. LDA. SVC.

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Shape based local affine invariant texture characteristics for fabric image retrieval

Zhang

Chen

et al. 2018

Multimed Tools Appl

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Performance analysis of Gabor wavelet for extracting most informative and efficient features

Cited by 5 publications

References 35 publications

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O₂ Content and NO_x

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O₂ Content and NO_x

Comparação de Classificadores para a Estimação de Posicionamento de Cabeça

Shape based local affine invariant texture characteristics for fabric image retrieval

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Performance analysis of Gabor wavelet for extracting most informative and efficient features

Cited by 5 publications

References 35 publications

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O2 Content and NOx

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O2 Content and NOx

Comparação de Classificadores para a Estimação de Posicionamento de Cabeça

Shape based local affine invariant texture characteristics for fabric image retrieval

Contact Info

Product

Resources

About

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O₂ Content and NO_x

Gabor-GLCM-Based Texture Feature Extraction Using Flame Image to Predict the O₂ Content and NO_x