Leaf Segmentation, Its 3D Position Estimation and Leaf Classification from a Few Images with Very Close Viewpoints

Authors:
Chin-Hung Teng;Yi-Ting Kuo;Yung-Sheng Chen
Affiliations:
Department of Information Communication, Yuan Ze University,;Department of Electrical Engineering, Yuan Ze University,;Department of Electrical Engineering, Yuan Ze University,
Venue:
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Year:
2009

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Abstract

In this paper, we present a complete system to extract leaves, recover their 3D positions and finally classify them based on leaf shape. We use only a few images with slightly different viewpoints to achieve the task. The images are captured by a general hand-held digital camera and no camera pre-calibration is required. Because only a few images with close viewpoints are sufficient to segment the leaves and recover their 3D positions, our system is flexible and easy to use in image acquisition. For leaf classification, we use the normalized centroid-contour distance as our classification feature and employ a circular-shift comparing scheme to measure the similarity, thus our system has the advantages of being invariant to leaf translation, rotation and scaling. We have conducted several experiments and the results are encouraging. The leaves are nearly perfectly extracted and the classification results are also acceptable.