Unsupervised texture segmentation based on multi-scale local binary patterns and FCMs clustering

2006 ◽  
Author(s):  
L. Ma ◽  
L.P. Lu ◽  
L. Zhu
Keyword(s):  
Local Binary Patterns ◽  
Texture Segmentation ◽  
Multi Scale

Texture Segmentation Using SOM and Multi-scale Bayesian Estimation

2006 ◽  
pp. 661-668
Author(s):  
Tae Hyung Kim ◽  
Il Kyu Eom ◽  
Yoo Shin Kim
Keyword(s):  
Bayesian Estimation ◽  
Texture Segmentation ◽  
Multi Scale

Texture Segmentation Using Neural Networks and Multi-scale Wavelet Features

2005 ◽  
pp. 400-409 ◽  
Author(s):  
Tae Hyung Kim ◽  
Il Kyu Eom ◽  
Yoo Shin Kim
Keyword(s):  
Neural Networks ◽  
Texture Segmentation ◽  
Multi Scale ◽  
Wavelet Features

Statistical multi-scale laws’ texture energy for texture segmentation

2018 ◽  
Author(s):  
Mega Kusuma Wardhani ◽  
Xingru Yu ◽  
Jinping Li
Keyword(s):  
Texture Segmentation ◽  
Multi Scale

Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition

2015 ◽  
Vol 26 (8) ◽  
pp. 2013-2026 ◽  
Author(s):  
Zi Liu ◽  
Xiaoning Song ◽  
Zhenmin Tang
Keyword(s):  
Face Recognition ◽  
Local Binary Patterns ◽  
Multi Scale

Off-line writer identification using multi-scale local binary patterns and SR-KDA

2013 ◽  
Author(s):  
E Khalifa ◽  
S Al-Maadeed ◽  
M A Tahir ◽  
F Khelifi ◽  
A Bouridane
Keyword(s):  
Local Binary Patterns ◽  
Writer Identification ◽  
Multi Scale

scholarly journals Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies

2019 ◽  
Author(s):  
Cefa Karabag ◽  
Jo Verhoeven ◽  
Naomi Rachel Miller ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Deep Learning ◽  
Local Binary Patterns ◽  
Texture Segmentation ◽  
Learning Approach ◽  
Optimisation Algorithms

This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Hus{\o}y were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques.

scholarly journals Automated Macular Pathology Diagnosis in Retinal OCT Images Using Multi-Scale Spatial Pyramid with Local Binary Patterns

2010 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Ying Liu ◽  
Mei Chen ◽  
Hiroshi Ishikawa ◽  
Gadi Wollstein ◽  
Joel S. Schuman ◽  
...  
Keyword(s):  
Local Binary Patterns ◽  
Multi Scale ◽  
Spatial Pyramid
Sign in / Sign up

Export Citation Format

Share Document