Scale Adaptive Clustering of Multiple Structures
This work addresses the challenge of robust structure segmentation in noisy data for computer vision and pattern recognition applications, representing an incremental improvement in adaptive clustering methods.
The paper tackles the problem of segmenting noisy datasets into multiple inlier structures by proposing a new robust estimator (MISRE) that adaptively estimates and refines scales without manual tuning, resulting in efficient and robust segmentation as demonstrated with synthetic and real examples.
We propose the segmentation of noisy datasets into Multiple Inlier Structures with a new Robust Estimator (MISRE). The scale of each individual structure is estimated adaptively from the input data and refined by mean shift, without tuning any parameter in the process, or manually specifying thresholds for different estimation problems. Once all the data points were classified into separate structures, these structures are sorted by their densities with the strongest inlier structures coming out first. Several 2D and 3D synthetic and real examples are presented to illustrate the efficiency, robustness and the limitations of the MISRE algorithm.