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Journals / International Journal of Engineering and Geosciences / 2021 / Volume: 6 Issue: 3

A new color distance measure formulated from the cooperation of the Euclidean and the vector angular differences for lidar point cloud segmentation

Ali Sağlam(KONYA TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-2980-9666
Nurdan Akhan Baykan(KONYA TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-4289-8889
Pages
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Publication Date
2021-10-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Two important features of the points in the LiDAR point clouds are the spatial and the color features.
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Key Findings & Data:The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic.
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Conclusion & Impact:According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Point Cloud Segmentation · Weighting · Color Distance

Abstract

Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.

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Cite This Article

Sağlam, A., Baykan, N. A. (2021). A new color distance measure formulated from the cooperation of the Euclidean and the vector angular differences for lidar point cloud segmentation. International Journal of Engineering and Geosciences, Volume: 6 Issue: 3. https://doi.org/10.26833/ijeg.709212

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