Authors:
Ujjwal Verma
1
;
Florence Rossant
2
;
Isabelle Bloch
3
;
Julien Orensanz
4
and
Denis Boisgontier
4
Affiliations:
1
ISEP, Télécom ParisTech and CNRS LTCI, France
;
2
ISEP, France
;
3
Télécom ParisTech and CNRS LTCI, France
;
4
Cap2020, France
Keyword(s):
Image Segmentation, Parametric Active Contours, Shape Constraint, Precision Farming, Elliptic Approximation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image Understanding
;
Image-Based Modeling
;
Object Recognition
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
Abstract:
In this paper, we present a segmentation procedure based on a parametric active contour with shape constraint,
in order to follow the growth of the tomatoes from the images acquired in the field. This is a challenging task
because of the poor contrast in the images and the occlusions by the vegetation. In our sequential approach,
considering one image per day, we assume that a segmentation of the tomatoes is available for the image
acquired the previous day. An initial curve for the active contour model is computed by combining gradient
information and region information. Then, an active contour with shape constraint is applied to provide an
elliptic approximation of the tomato boundary. We performed a quantitative evaluation of our approach by
comparing the results with the manual segmentation. Given the varying degree of occlusion in the images, the
image data set was divided into three categories, based on the occlusion degree of the tomato in the processed
image. For the cases wi
th low occlusion, good results were obtained, with an average relative distance between
the manual segmentation and the automatic segmentation of 2.73% (expressed as percentage of the size of
tomato). For the images with significant amount of occlusion, a good segmentation was obtained on 44% of
the images, where the average error was less than 10%.
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