Matching-Constrained Active Contours

TL;DR

This paper introduces a novel active contour model that incorporates feature point matching as a constraint, enabling automatic object segmentation without user intervention and improving over existing methods.

Contribution

It extends active contour models by integrating feature matching constraints, providing an automated segmentation framework with a new mathematical formulation and solution approach.

Findings

Achieves automatic object segmentation without user input.

Outperforms related segmentation methods in experiments.

Ensures contour alignment through feature point matching constraints.

Abstract

In object segmentation by active contours, the initial contour is often required. Conventionally, the initial contour is provided by the user. This paper extends the conventional active contour model by incorporating feature matching in the formulation, which gives rise to a novel matching-constrained active contour. The numerical solution to the new optimization model provides an automated framework of object segmentation without user intervention. The main idea is to incorporate feature point matching as a constraint in active contour models. To this effect, we obtain a mathematical model of interior points to boundary contour such that matching of interior feature points gives contour alignment, and we formulate the matching score as a constraint to active contour model such that the feature matching of maximum score that gives the contour alignment provides the initial feasible solution to the constrained optimization model of segmentation. The constraint also ensures that the optimal contour does not deviate too much from the initial contour. Projected-gradient descent equations are derived to solve the constrained optimization. In the experiments, we show that our method is capable of achieving the automatic object segmentation, and it outperforms the related methods.