Comparison of feature points detection algorithms in different color spaces in order to create 3D map of trees

Document Type : Research Paper


PHD Student, Department of Biosystems Engineering, Ferdowsi University of Mashhad


One method of creating a 3D model is stereo vision. The most important problem in this method is the corresponding points. In this study, 6 algorithms included Harris-Stephens, Minimum eigenvalue, MSER, FAST, SURF and BRISK were compared in RGB, G, HSV, H, YCbCr, Y, NTSC, Lab and a spaces. The results showed that SURF algorithm had the best performance. Detected feature points by this algorithm were fix in most spaces, so this algorithm is stable in different spaces. After SURF algorithm, MSER algorithm had the best performance. This algorithm detected tree crops as feature points. Although the number of these points is low, but if cannot be matched corner points in two images together, these points can be used to match as common points (keypoints). Algorithms had the best performance in the HSV, H, YCbCr and NTSC spaces and they were stable in RGB and Y spaces in terms of the number of detected feature points.


Main Subjects

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