Feature detection using Hidden Markov Models for 3D-visual recognition

Abstract

In this work, we present a novel implementation for visual recognition using probabilistic models. Given a scene view, we first propose a 3D feature extraction from a point cloud as a series of observations for a Hidden Markov Model; then, we evaluate the Profile HMM in the place recognition task using a publicly available dataset. Furthermore, we evaluated a classical HMM in the object recognition task in the context of anthropomorphic service robots. Results show that our approach performs well in the aforementioned tasks with high recognition rates.