Free Form based active contours for image segmentation and free space perception
This work addresses image segmentation and free space perception for robots in unknown environments, presenting an incremental improvement over existing active contour methods.
The paper tackles the problem of representing and evolving deformable active contours for image segmentation and free space perception by combining piecewise regular Bézier models with local Free Form Deformation, resulting in fast and real-time robot navigation with almost linear complexity.
In this paper we present a novel approach for representing and evolving deformable active contours. The method combines piecewise regular B{é}zier models and curve evolution defined by local Free Form Deformation. The contour deformation is locally constrained which allows contour convergence with almost linear complexity while adapting to various shape settings and handling topology changes of the active contour. We demonstrate the effectiveness of the new active contour scheme for visual free space perception and segmentation using omnidirectional images acquired by a robot exploring unknown indoor and outdoor environments. Several experiments validate the approach with comparison to state-of-the art parametric and geometric active contours and provide fast and real-time robot free space segmentation and navigation.