Texture Unit, Texture Spectrum, And Texture Analysis

By D. He, Li Wang. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Robust Representations by Projecting Superficial Statistics Out (2018, methodology): “In order to search the old computer vision techniques for a method that can extract more textural information and less semantic information, we experimented with three classifcal computer vision techniques: SURF (Bay et al., 2006), LBP (He & Wang, 1990), and GLCM (Haralick et al., 1973) on several d”
DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval (2018, methodology): “Feature Extractor Category #Features BIC Histogram [15] Color 512 Edge Histogram [16] Color 150 Normalized Histogram [17] Color 256 Haralick [18] Texture 24 Rotation Invariant LBP [19] Texture 108 Texture Spectrum [20] Texture 8 Daubechies [21] Shape 16 Haar [21] Shape 16 Zernike [22] Shape 36”
A survey of image classification methods and techniques for improving classification performance (2007, unknown): “Many texture measures have been developed (Haralick et al. 1973, Kashyap et al. 1982, He and Wang 1990, Unser 1995, Emerson et al. 1999) and have been used for image classifications (Gordon and Phillipson 1986, Franklin and Peddle 1989, 840 D. Lu and Q. Weng Table 3.”