TY - GEN
T1 - Fast Generalized Chan-Vese Model for plant/soil segmentation to estimate percentage of ground cover in agricultural images
AU - Boutiche, Y.
AU - Abdessalem, A.
AU - Ramou, N.
AU - Chetih, N.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Computer vision systems have been widely used in agriculture to perform tasks requiring segmentation and classification. In this paper, we describe a fast multi-channel implicit active contour method for performing plant/soil segmentation in color agriculture images. First, the Chan-Vese model is generalized to segment images in L∗ a∗ b∗ color space, then a level set method, capable of dealing with topology changes, is used to segment the whole image. Finally, a functional is optimized via a 'sweeping' algorithm for fast convergence. Percentage of Ground Cover (PGC) is then easily estimated from the segmented image. Several experiments have been conducted to validate the proposed algorithm.
AB - Computer vision systems have been widely used in agriculture to perform tasks requiring segmentation and classification. In this paper, we describe a fast multi-channel implicit active contour method for performing plant/soil segmentation in color agriculture images. First, the Chan-Vese model is generalized to segment images in L∗ a∗ b∗ color space, then a level set method, capable of dealing with topology changes, is used to segment the whole image. Finally, a functional is optimized via a 'sweeping' algorithm for fast convergence. Percentage of Ground Cover (PGC) is then easily estimated from the segmented image. Several experiments have been conducted to validate the proposed algorithm.
KW - Agricultural images
KW - Percentage of Ground Cover
KW - color spaces
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85081307161&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081307161&partnerID=8YFLogxK
U2 - 10.1109/ISSPIT47144.2019.9001887
DO - 10.1109/ISSPIT47144.2019.9001887
M3 - Conference contribution
AN - SCOPUS:85081307161
T3 - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
BT - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2019
Y2 - 10 December 2019 through 12 December 2019
ER -