Sensors, Vol. 20, Pages 3806: Quantized Residual Preference based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting

Sensors, Vol. 20, Pages 3806: Quantized Residual Preference based Linkage Clustering for Model Selection and Inlier Segmentation in Geometric Multi-Model Fitting
Sensors doi: 10.3390/s20133806
Authors:
Qing Zhao
Yun Zhang
Qianqing Qin
Bin Luo

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.

Source: Sensors - July 6, 2020 Category: Biotechnology Authors: Qing Zhao Yun Zhang Qianqing Qin Bin Luo Tags: Article Source Type: research