Rotation-Invariant Image and Video Description With Local Binary Pattern Features

By Guoying Zhao, Timo Ahonen, Jiri Matas, Matti Pietikäinen. IEEE Transactions on Image Processing, 2012

384 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/74412f25763f9f82c28ec4b7101bb996adc1c31d

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Stéphane Mallat
2. Laurent Sifre
3. Mihai Datcu
4. Shiyong Cui
5. John See
6. Raphael Chung-Wei Phan
7. Yandan Wang
8. Gottfried Schwarz

Citations in the corpus

• Remote Sensing Image Classification: No Features, No Clustering (2015, background): “Many local feature descriptors have been proposed, like SPIN Image, Rotation Invariant Feature Transform (RIFT) proposed in [11], Census Transform Histogram (CENTRIST) [12], and Local Binary Pattern (LBP) [13], [14].”
• Rigid-Motion Scattering for Texture Classification (2014, background): “A scattering operator (Mallat, 2012) computes an invariant image representation relatively to the action of a group by applying wavelet transforms to functions defined on the group.”
• LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition (2014, methodology): “To increase its robustness against view-based variations in texture, rotation-invariant descriptors [16] computed from the LBP-TOP features were proposed, to a good measure of success.”