Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
This work addresses a domain-specific problem in computer vision for researchers and practitioners dealing with geometric model fitting, but it appears incremental as it builds on existing deterministic fitting frameworks with enhancements.
The paper tackles the problem of geometric model fitting for multiple-structure data by proposing Superpixel-based Deterministic Fitting (SDF), which uses superpixel segmentation to reduce computational complexity and includes novel sampling and model selection algorithms, resulting in superior speed and accuracy over state-of-the-art methods in experiments on real images.
This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. Specifically, the proposed sampling algorithm leverages the grouping cues of superpixels to generate reliable and consistent hypotheses. The proposed model selection algorithm further makes use of desirable properties of the generated hypotheses, to improve the conventional fit-and-remove framework for more efficient and effective performance. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multi-structure data. Experimental results demonstrate that the proposed SDF shows superiority over several state-of-the-art fitting methods for real images with single-structure and multiple-structure data.