TY - GEN
T1 - Anisotropic diffusion for smoothing
T2 - International Conference on Computer Vision and Graphics, ICCVG 2016
AU - Bustacara, César
AU - Gómez-Mora, Miller
AU - Flórez-Valencia, Leonardo
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Anisotropic diffusion is a powerful image processing technique, which allows simultaneously to remove noise and to enhance sharp features in two and three dimensional images. Anisotropic diffusion filtering concentrates on preservation of important surface features, such as sharp edges and corners, by applying direction dependent smoothing. This feature is very important in image smoothing, edge detection, image segmentation and image enhancement. For instance, in the image segmentation case, it is necessary to smooth images as accurately as possible in order to use gradient-based segmentation methods. If image edges are seriously polluted by noise, these methods would not be able to detect them, so edge features cannot be retained. The aim of this paper is to present a comparative study of three methods that have been used for smoothing using anisotropic diffusion techniques. These methods have been compared using the root mean square error (RMSE) and the Nash-Sutcliffe error. Numerical results are presented for both artificial data and real data.
AB - Anisotropic diffusion is a powerful image processing technique, which allows simultaneously to remove noise and to enhance sharp features in two and three dimensional images. Anisotropic diffusion filtering concentrates on preservation of important surface features, such as sharp edges and corners, by applying direction dependent smoothing. This feature is very important in image smoothing, edge detection, image segmentation and image enhancement. For instance, in the image segmentation case, it is necessary to smooth images as accurately as possible in order to use gradient-based segmentation methods. If image edges are seriously polluted by noise, these methods would not be able to detect them, so edge features cannot be retained. The aim of this paper is to present a comparative study of three methods that have been used for smoothing using anisotropic diffusion techniques. These methods have been compared using the root mean square error (RMSE) and the Nash-Sutcliffe error. Numerical results are presented for both artificial data and real data.
UR - http://www.scopus.com/inward/record.url?scp=84989820735&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46418-3_10
DO - 10.1007/978-3-319-46418-3_10
M3 - Conference contribution
AN - SCOPUS:84989820735
SN - 9783319464176
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 120
BT - Computer Vision and Graphics - International Conference, ICCVG 2016, Proceedings
A2 - Datta, Amitava
A2 - Wojciechowski, Konrad
A2 - Chmielewski, Leszek J.
A2 - Kozera, Ryszard
PB - Springer Verlag
Y2 - 19 September 2016 through 21 September 2016
ER -