TY - JOUR
T1 - Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
AU - Huang, Yuan
AU - De Bortoli, Valentin
AU - Zhou, Fugen
AU - Gilles, Jérôme
N1 - Publisher Copyright:
© 2018, The Institution of Engineering and Technology.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.
AB - Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.
UR - https://www.scopus.com/pages/publications/85051639208
U2 - 10.1049/iet-ipr.2017.1005
DO - 10.1049/iet-ipr.2017.1005
M3 - 文章
AN - SCOPUS:85051639208
SN - 1751-9659
VL - 12
SP - 1626
EP - 1638
JO - IET Image Processing
JF - IET Image Processing
IS - 9
ER -