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Accelerated Guided Sampling for Multistructure Model Fitting
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AI
DOI：10.1109/TCYB.2018.2889908.png)
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Abstract
En 中文
The performance of many robust model fitting techniques is largely dependent on the quality of the generated hypotheses. In this paper, we propose a novel guided sampling method, called accelerated guided sampling (AGS), to efficiently generate the accurate hypotheses for multistructure model fitting. Based on the observations that residual sorting can effectively reveal the data relationship (i.e., determine whether two data points belong to the same structure), and keypoint matching scores can be used to distinguish inliers from gross outliers, AGS effectively combines the benefits of residual sorting and keypoint matching scores to efficiently generate accurate hypotheses via information theoretic principles. Moreover, we reduce the computational cost of residual sorting in AGS by designing a new residual sorting strategy, which only sorts the top-ranked residuals of input data, rather than all input data. Experimental results demonstrate the effectiveness of the proposed method in computer vision tasks, such as homography matrix and fundamental matrix estimation.
Keywords:
Correlation
Sorting
Data models
Computational modeling
Sampling methods
Acceleration
Estimation
Hypothesis generation
keypoint matching scores
multiple structures
residual sorting
robust model fitting
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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Cited Papers
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