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Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting

The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
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Sensors 2020, 20(13), 3806; https://doi.org/10.3390/s20133806
Received: 22 April 2020 / Revised: 22 June 2020 / Accepted: 23 June 2020 / Published: 7 July 2020
(This article belongs to the Section Remote Sensors)
In this paper, quantized residual preference is proposed to represent the hypotheses and the points for model selection and inlier segmentation in multi-structure geometric model fitting. First, a quantized residual preference is proposed to represent the hypotheses. Through a weighted similarity measurement and linkage clustering, similar hypotheses are put into one cluster, and hypotheses with good quality are selected from the clusters as the model selection results. After this, the quantized residual preference is also used to present the data points, and through the linkage clustering, the inliers belonging to the same model can be separated from the outliers. To exclude outliers as many as possible, an iterative sampling and clustering process is performed within the clustering process until the clusters are stable. The experiments undertake indicate that the proposed method performs even better on real data than the some state-of-the-art methods. View Full-Text
Keywords: geometric model fitting; quantized residual; linkage clustering; sampling and clustering geometric model fitting; quantized residual; linkage clustering; sampling and clustering
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MDPI and ACS Style

Zhao, Q.; Zhang, Y.; Qin, Q.; Luo, B. Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting. Sensors 2020, 20, 3806. https://doi.org/10.3390/s20133806

AMA Style

Zhao Q, Zhang Y, Qin Q, Luo B. Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting. Sensors. 2020; 20(13):3806. https://doi.org/10.3390/s20133806

Chicago/Turabian Style

Zhao, Qing, Yun Zhang, Qianqing Qin, and Bin Luo. 2020. "Quantized Residual Preference Based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting" Sensors 20, no. 13: 3806. https://doi.org/10.3390/s20133806

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