TY - GEN
T1 - Segmentation of tomatoes in open field images with shape and temporal constraints
AU - Verma, Ujjwal
AU - Rossant, Florence
AU - Bloch, Isabelle
AU - Orensanz, Julien
AU - Boisgontier, Denis
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - With the aim of estimating the growth of tomatoes during the agricultural season, we propose to segment tomatoes in images acquired in open field, and to derive their size from the segmentation results obtained in pairs of images acquired each day. To cope with difficult conditions such as occlusion, poor contrast and movement of tomatoes and leaves, we propose to base the segmentation of an image on the result obtained on the image of the previous day, guaranteeing temporal consistency, and to incorporate a shape constraint in the segmentation procedure, assuming that the image of a tomato is approximately an ellipse, guaranteeing spatial consistency. This is achieved with a parametric deformable model with shape constraint. Results obtained over three agricultural seasons are very good for images with limited occlusion, with an average relative distance between the automatic and manual segmentations of 6.46% (expressed as percentage of the size of tomato).
AB - With the aim of estimating the growth of tomatoes during the agricultural season, we propose to segment tomatoes in images acquired in open field, and to derive their size from the segmentation results obtained in pairs of images acquired each day. To cope with difficult conditions such as occlusion, poor contrast and movement of tomatoes and leaves, we propose to base the segmentation of an image on the result obtained on the image of the previous day, guaranteeing temporal consistency, and to incorporate a shape constraint in the segmentation procedure, assuming that the image of a tomato is approximately an ellipse, guaranteeing spatial consistency. This is achieved with a parametric deformable model with shape constraint. Results obtained over three agricultural seasons are very good for images with limited occlusion, with an average relative distance between the automatic and manual segmentations of 6.46% (expressed as percentage of the size of tomato).
UR - https://www.scopus.com/pages/publications/84951811300
UR - https://www.scopus.com/pages/publications/84951811300#tab=citedBy
U2 - 10.1007/978-3-319-25530-9_11
DO - 10.1007/978-3-319-25530-9_11
M3 - Conference contribution
AN - SCOPUS:84951811300
SN - 9783319255293
VL - 9443
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 162
EP - 178
BT - Pattern Recognition Applications and Methods - 3rs International Conference, ICPRAM 2014, Revised Selected Papers
A2 - de Marsico, Maria
A2 - Fred, Ana
A2 - Tabbone, Antoine
PB - Springer Verlag
T2 - 3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014
Y2 - 6 March 2014 through 8 March 2014
ER -