Radon transform orientation estimation for rotation invariant texture analysis

By Kourosh Jafari-Khouzani, Hamid Soltanian-Zadeh. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

321 citations recorded by the source corpus.

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A novel Ray-space based view generation algorithm via Radon transform (2013, unknown): “k l M a X y M u y a a a y E − ∑ + − − λ L (26)”
A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features (2010, methodology): “Wavelet-Based Features: Wavelet-based features aim at extracting the information in the frequency domain, and their capability of undergoing texture analysis has been shown [30].”
Dominant Local Binary Patterns for Texture Classification (2009, methodology): “Analogous to the works presented in [10] and [14], each feature extraction technique is evaluated and compared with others by passing the features through the same classifier.”
Learning rotation invariant convolutional filters for texture classification (2016, background): “One approach to achieve invariance to rotation in anisotropic textures is to find the main axis of a query texture patch and rotate it until this axis is aligned with those in the training set [2].”
Rotation invariant texture classification using LBP variance (LBPV) with global matching (2010, methodology): “Similar idea has been exploited by Jafari-Khouzani and Soltanian-Zadeh [22].”
Texture Feature Extraction Methods: A Survey (2019, background): “However, several papers proposed attempts towards rotation invariant texture analysis using wavelet transform (see [156] and a short survey mentioned in [156]).”