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

Area of Science:

• Computer Vision
• Geometric Modeling
• Data Analysis

Background:

• Outliers significantly impact geometric model fitting accuracy.
• Existing methods like inlier thresholds and scale estimators have limitations with multiple models and noise distributions.
• Outliers exhibit a consensus in residuals across true models, a key observation for detection.

Purpose of the Study:

• To develop a robust outlier detection and inlier segmentation method for geometric multi-model fitting.
• To address the limitations of traditional methods in handling complex data with multiple models and outliers.
• To improve the accuracy and stability of geometric model fitting in computer vision applications.

Main Methods:

• Proposed a preference analysis method based on residual histograms to identify outlier consensus.
• Utilized linkage clustering with permutation preference for inlier segmentation after outlier removal.
• Implemented an alternative sampling and clustering framework for enhanced stability and robustness in both detection and segmentation.

Main Results:

• The residual histogram preference method effectively detects outliers by exploiting their consensus.
• Outliers are successfully separated from inliers in a designed preference space.
• The proposed method demonstrates superior fitting results compared to state-of-the-art techniques in geometric multi-model fitting.

Conclusions:

• Preference analysis based on residual histograms offers a powerful tool for outlier detection in geometric model fitting.
• The developed linkage clustering and sampling framework ensures robust inlier segmentation.
• This approach significantly enhances the performance and reliability of geometric multi-model fitting algorithms.