Brief review of invariant texture analysis methods

By Jianguo Zhang, Tien-Ping Tan. Pattern Recognition, 2002

521 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/30d1c4fa60ce1fd5b05c79631b1c0e54a26206bf

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In Analytical Chemistry

In Computer Vision and Pattern Recognition

Citations in the corpus

Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features (2009, background): “Rotation invariant texture analysis is a widely studied problem [1], [2], [3].”
Radon transform orientation estimation for rotation invariant texture analysis (2005, background): “Translation, rotation, and scale invariant texture analysis methods have been of particular interest [ 1 ].”
Texture Classification from Random Features (2012, background): “First, the presence of irrelevant and noisy features can mislead the clustering algorithm; second, in high dimensions, data may be very sparse (the so-called curse of dimensionality), making it difficult to represent the structure in the data.”
Texture Feature Extraction Methods: A Survey (2019, methodology): “In 2002, Zhang and Tan [20] proposed a brief review of texture analyses for which the performances are not modified by translation, rotation, affine, and perspective transform.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, result): “Similarly, the articles (Randen and Husoy 1999; Zhang and Tan 2002) only covered approaches before 2000.”
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes (2009, methodology): “These methods and others are surveyed in [41].”
Combined scattering for rotation invariant texture analysis (2012, methodology): “Many methods [1] start by computing informative statistics with a first computational layer and then use a second layer to build rotation invariance.”
The role of quantitative PET in predicting cancer treatment outcomes (2014, unknown): “[61, 62].”
Personal Identification Based on Iris Texture Analysis (2003, methodology): “In the spatial domain, one can extract information of an image at a certain orientation and scale using some specific filters, such as Gabor filters [33], [34], [35], [36], [37].”
Enriching Texture Analysis with Semantic Data (2013, background): “Humans are capable of analysing texture in a way resistant to noise, and invariant to illumination, rotation, and scale [10, 30].”