A Novel 2-D Robust LPP-Based Approach Using Density-Based Neighborhood Granulation for Challenging Visual Cue Detection

In recent times, the rapid growth of human-robot interaction (HRI)-based utilities can be observed in industrial and domestic applications. To ensure seamless operation in daily life HRI spaces, several aspects of environmental hazards need to be taken care of. Vision-based systems, in particular, are prone to challenges such as degradation in ambient illumination and noise in the sensor accessories. For high-dimensional vision sensor data, manifold learning-based dimensionality reduction (DR) techniques like locality preserving projection (LPP) have shown promising results. To handle the sensitivity of LPP toward noise and outliers arising from such adversities, methods like 2D-LPP and robust 2D-LPP (2DRLPP) have been introduced further. However, in these methods also, the projection maps remain susceptible to the spatial perturbations in the data. To address these limitations, this article proposes a granular feature-aided 2DRLPP scheme to enhance robustness against noise, intensity variations, and spatial outliers. To obtain the robust feature information, a new granular computing (GrC)-based technique is introduced. A novel density-based neighborhood granulation (dNG) algorithm is proposed for extracting granular information from real-world vision sensor data. Additionally, an RGB-channel fuzzy decoding scheme is developed to decode the granular information and utilize it for constructing a robust projection kernel. The proposed technique is validated in a real-world assistive robotic environment, where flagstick visual cues are used by human participants to supervise an assistive mobile robot in performing necessary tasks.