Sub-pixel accuracy edge fitting by means of B-spline

TL;DR

This paper introduces a B-spline based deformable model for sub-pixel edge fitting in computer vision, which effectively filters noise and improves accuracy over classical methods, especially under Gaussian and Salt & Pepper noise conditions.

Contribution

It proposes a global B-spline model for sub-pixel edge estimation that incorporates orientation and position data within a maximum likelihood framework, enhancing noise robustness.

Findings

Outperforms classical spline interpolation in noisy conditions

Effectively filters out the noisiest data points

Provides more accurate sub-pixel edge detection

Abstract

Local perturbations around contours strongly disturb the final result of computer vision tasks. It is common to introduce a priori information in the estimation process. Improvement can be achieved via a deformable model such as the snake model. In recent works, the deformable contour is modeled by means of B-spline snakes which allows local control, concise representation, and the use of fewer parameters. The estimation of the sub-pixel edges using a global B-spline model relies on the contour global determination according to a maximum likelihood framework and using the observed data likelihood. This procedure guarantees that the noisiest data will be filtered out. The data likelihood is computed as a consequence of the observation model which includes both orientation and position information. Comparative experiments of this algorithm and the classical spline interpolation have shown that the proposed algorithm outperforms the classical approach for Gaussian and Salt & Pepper noise.