Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods
- 4 February 2015
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 24 (5), 1549-1560
- https://doi.org/10.1109/tip.2015.2400214
Abstract
In this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation-Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, using preprocessing tools, such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally, we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones.Keywords
Funding Information
- French National Agency for Research through the Project entitled Reconnaissance de Vegetaux Pour des Interfaces Smartphones (ANR-10-CORD-005)
This publication has 33 references indexed in Scilit:
- The minimum barrier distanceComputer Vision and Image Understanding, 2013
- Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodologyPattern Recognition, 2012
- Adaptive pyramid mean shift for global real-time visual trackingImage and Vision Computing, 2010
- Individual leaf extractions from young canopy images using Gustafson–Kessel clustering and a genetic algorithmComputers and Electronics in Agriculture, 2006
- Mean shift: a robust approach toward feature space analysisIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
- Active contours without edgesIEEE Transactions on Image Processing, 2001
- B-spline snakes: a flexible tool for parametric contour detectionIEEE Transactions on Image Processing, 2000
- Normalized cuts and image segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- Mean shift, mode seeking, and clusteringIEEE Transactions on Pattern Analysis and Machine Intelligence, 1995
- Automatic multithreshold selectionComputer Vision, Graphics, and Image Processing, 1984