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. 2020 May 27;20(11):3037.
doi: 10.3390/s20113037.

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Xi Zhao  1 , Yun Zhang  1 , Shoulie Xie  2 , Qianqing Qin  1 , Shiqian Wu  3 , Bin Luo  1
Affiliations

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Xi Zhao et al. Sensors (Basel). .

Abstract

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

Keywords: alternative sampling and clustering; geometric multi-model fitting; outlier detection; residual histogram.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The X-axis represents the residual value, and the Y-axis represents the number of model hypotheses votes. (a) The residual histogram of an inlier. Peak appear in bands with small residual value, which is corresponding to the true model. (b) The residual histogram of an outlier. Most residual values are large and there is no obvious peak in the whole band.
Figure 2
Figure 2
Least squares line fitting results under different degree of outlier contamination.
Figure 3
Figure 3
The distribution of matched points for homography estimation in different preference spaces.
Figure 4
Figure 4
The flowchart of outlier detection.
Figure 5
Figure 5
MDS (Multiple Dimensional Scaling) plots for “johnsonb” without outliers by using different preferences.
Figure 5
Figure 5
MDS (Multiple Dimensional Scaling) plots for “johnsonb” without outliers by using different preferences.
Figure 6
Figure 6
The flowchart of inlier segmentation.
Figure 7
Figure 7
Inlier segmentation results for the multi-homography estimation.
Figure 7
Figure 7
Inlier segmentation results for the multi-homography estimation.
Figure 8
Figure 8
Inlier segmentation results for the multi-fundamental matrix estimation.
Figure 9
Figure 9
Outlier detection results on individual sequence.
Figure 10
Figure 10
The impact of the quantization level and quantization length on the outlier detection.
Figure 10
Figure 10
The impact of the quantization level and quantization length on the outlier detection.
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