This work is part of a global framework that aims to build a complete set of algorithms for autonomous robot exploration, navigation and map building using a sole omnidirectional camera. It focuses on topology extraction of navigable free spaces to yield autonomous robot exploration. The paper proposes a new algorithm, the Omnidirectional Delta Medial Axis (ODMA), with linear-time implementation to extract robust topology of the robot's local free space. It first introduces an adapted metric to cope with the deformations involved by the catadioptric sensor, and that is conformal with the ground 2D-metric. Then, it produces a pruned skeleton of the free space, inspired by the delta medial axis. Experimental results validate the approach for both skeleton precision and stability with respect to the robot position and free space topology.

Medial axes are known to be very sensitive to shape irregularities. In this paper, we develop a solution to compute a stable medial axis of noisy discrete shapes. It introduces a parameter up to which a deformation (noise) of the shape is considered irrelevant, and thus ignored in the discrete Euclidian Medial Axis computation. We show the linearity property of the proposed algorithm and compare it with two recent state of the art methods: The Gamma Integer Medial Axis and the Discrete Linear Lambda Medial Axis using a single pruning parameter. Based on Kimia's database (216 binary images), we present comparative experimental results with respect to skeletonization quality, noise sensitivity and computation time.

Detection of moving objects is known to be a critical first step in many vision-based applications such as video surveillance. Background Subtraction Algorithms (BSA) offer a solution to detect all foreground pixels in a frame by comparing them with a background model. However, this is still a very challenging task especially when dynamic scenes are involved (camera jitter, noise, etc). In this paper, we present a new method for dynamic background subtraction operation based on invariant moments (Hu Set). Each pixel is modeled as a set of moments calculated from its neighborhood and stored using codebook construction. Experimental results on a set of outdoor scenes show that our method outperforms traditional BSA.