Rotation-invariant texture classification using feature distributions
By Matti Pietikäinen, Timo Ojala, Zelin Xu. Pattern Recognition, 2000
409 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/26d9214ecf6a738095362738512f4cc9b44f15fe
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Anne Humeau-Heurtier
- Timo Ojala
- Topi Mäenpää
- Matti Pietikäinen
- Baochang Zhang
- Yongsheng Gao
- Jianzhuang Liu
- Sanqiang Zhao
In Computer Vision and Pattern Recognition
- Matti Pietikäinen
- Anne Humeau-Heurtier
- Xiaogang Wang
- Luc Van Gool
- Baochang Zhang
- Dacheng Tao
- Hamid Soltanian-Zadeh
- Yongsheng Gao
Citations in the corpus
- A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification (2001, methodology): “Recently, we introduced a theoretically and computationally simple approach for gray scale and rotation in variant texture analysis based on Local Binary P atterns, which is robust in terms of gray scale v ariations and discriminated rotated te xtures ef ficiently [8,10].”
- Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor (2010, background): “The recently proposed local binary pattern (LBP) features are originally designed for texture description [16], [17], [21].”
- Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (2011, neutral): “However, in [40], it was shown that such a rotation-invariant LBP operator does not necessarily provide discriminative information, since the occurrence frequencies of the individual patterns incorporated in LBP (P, R) vary greatly and the crude quantization of the angular spaces at 45° intervals.”
- Radon transform orientation estimation for rotation invariant texture analysis (2005, background): “Pietikainen, et al. [ 5 ] present a set of features to describe the texture, most of which locally invariant to rotation.”
- Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns (2000, methodology): “However, our practical experience has showed that LBP 8ri36 as such does not provide a very good discrimination, as we also concluded in [11].”
- Texture Feature Extraction Methods: A Survey (2019, background): “Thus, in 2000, a rotation invariant version has been proposed [79].”