Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns

By Timo Ojala, Matti Pietikäinen, Topi Mäenpää. European Conference on Computer Vision, 2000

704 citations recorded by the source corpus.

How researchers see this paper

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

Evaluating the Potential of Texture and Color Descriptors for Remote Sensing Image Retrieval and Classification (2010, methodology): “If the quantity of transitions is le s than 2, the LBP value for that window position is equal to the quantity of 1 signals in the neighboro d.”
A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification (2001, methodology): “For brevity we omit the derivation presented in [8], and obtain the follo wing generalized operator for gray scale and rotation in variant texture description:”
A Comprehensive Survey of Deep Learning for Image Captioning (2018, methodology): “ategories: (1) Traditional machine learning based techniques and (2) Deep machine learning based techniques. In traditional machine learning, hand crafted features such as Local Binary Patterns (LBP) [107], Scale-Invariant Feature Transform (SIFT) [87], the Histogram of Oriented Gradients (HOG) [27]”