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Introduction
Figure 1:An example of execution of the proposed framework. (a) Original image. (b) RGBVI image (c) Binary image (d) Tree Detection.
We present an unsupervised method for tree detection from high resolution UAV imagery based on a modified version of the Decremental Ellipse Fitting Algorithm DEFA. The proposed Decremental Circle Fitting Algorithm (DCFA) works similarly to DEFA with the main difference that DCFA uses circles instead of ellipses. According to DCFA, the skeleton of the 2D shape is calculated first, followed by the initialization of the circle hypotheses and the application of the Gaussian Mixture Model Expectation Maximization algorithm.
Methodology of DCFA
Figure 2. The schema of the main steps of the DCFA.
The DCFA works similarly to DEFA (Panagiotakis and Argyros, 2016) with the main difference that DCFA uses circles instead of ellipses. The main steps of the DCFA are depicted in Fig. 2.
Experiments - Downloads of DCFA
You can download the matlab code of the DCFA method proposed in [1]
You can download the datasets proposed in [3] from https://github.com/tongpinmo/PSSNet
Experimental results of DCFA on Acacia-6 dataset
See the corresponding readme.txt files for more details.
Related Publications
[1] S. Markaki, C. Panagiotakis, Unsupervised Tree Detection and Counting via Region-based Circle Fitting, International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2023.
[2] C. Panagiotakis and A. Argyros, Parameter-free Modelling of 2D Shapes with Ellipses, Pattern Recognition, vol. 53, pp. 259-275, 2016.
[3] Tong, P., Han, P., Li, S., Li, N., Bu, S., Li, Q., and Li, K. (2021). Counting trees with point-wise supervised segmentation network. Engineering Applications of
Artificial Intelligence, 100:104172
[3] C. Panagiotakis and A. Argyros, Cell Segmentation via Region-based Ellipse Fitting, IEEE International Conference on Image Processing, 2018.
[4] C. Panagiotakis and A.A. Argyros, Region-based Fitting of Overlapping Ellipses and its Application to Cells Segmentation, Image and Vision Computing, Elsevier, vol. 93, pp. 103810, 2020.
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