A robust RGBD saliency method with improved probabilistic contrast and the global reference surface

Abstract

The human attention mechanism inspires salient object detection. Most of the saliency methods work on 2D perception mechanisms, while human attention systems work on 3D perception mechanisms. This proposed method makes use of depth information from RGBD to robustly and correctly detect the salient object in a complex and clutter background. The saliency of regions related to object border increases in Poisson probabilistic contrast space while distinguishing the conspicuous object in a complex and clutter background. This process produces a global concave reference surface. This global reference plane integrated with intra-regional spatial, structural, color, and depth information detects the salient object correctly. Background estimation and central saliency integration thoroughly remove the background. This algorithm generates a robust conspicuous object. The experimental result presented here shows that the proposed method performs better in comparison to the recent, highly referenced and closely related fourteen state-of-the-art methods, and the three publicly available complex RGBD datasets and six evaluation parameters. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.