Advanced pattern recognition from complex environments: a classification-based approach

This paper describes an algorithm for building 3D maps of objects detected in the visual scene acquired in an indoor environment. One feature of the described algorithm is that it works with a standard webcam equipped with a simple devices which automatically estimates the camera orientation and its distance from the floor. Another feature is that the algorithm has a low computational complexity. The proposed algorithm first extracts from the acquired images the regions of interest (ROI) which may contain an object. The ROI's 3D position is then estimated and a map of the environment is generated. ROI extraction is realized with an Haar-like approach. ROIs are represented with edge-based features. The edge representation is filtered with a novel fuzzy-based technique which removes edges introduced by noise. Object classification is performed with a pseudo2D-HMM algorithm. We prove the reliability of our method by discussing some critical applications in the context of human-robot interaction and robot-robot interaction. Finally, we complete our contributions via describing a case study in the robotic field and providing comprehensive experimental results showing the benefits deriving from our approach.