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- W82392166 abstract "Because of the large computation and not have multi-resolution for complex application used the Gray-level Co-occurrence Matrix (GLCM).In this paper, we choose the critical parameter by compare analysis for GLCM to bring down the calculated amount; we bring wavelet transforms to analysis texture feature in multi-resolution. First of all, construct GLCM for the image and calculate the parameter; second, decompose the image use wavelet, calculate mean and standard deviation of the wavelet son band coefficient; Third, build up eigenvector with mean standard deviation and the GLCM parameter which we choose; The last, we use the eigenvector to extract texture feature for multi-objective image in complex traffic environment for head identify. The results show that, the texture feature which we extract can bring high recognition rate." @default.
- W82392166 created "2016-06-24" @default.
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- W82392166 date "2012-01-01" @default.
- W82392166 modified "2023-09-26" @default.
- W82392166 title "The Texture Feature Extraction for Multi-objective Image in Complex Traffic Environment" @default.
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- W82392166 doi "https://doi.org/10.1007/978-3-642-25781-0_109" @default.
- W82392166 hasPublicationYear "2012" @default.
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