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.

人为注意机制激发了显着的物体检测。大多数显着性方法适用于2D感知机制，而人类注意力系统则适用于3D感知机制。该方法利用了来自RGBD的深度信息，能够在复杂而杂乱的背景下稳健并正确地检测到显着物体。泊松概率对比空间中与对象边界相关的区域的显着性增加，同时在复杂而混乱的背景下区分了显眼的对象。此过程将产生一个整体的凹参考面。此全局参考平面与区域内的空间，结构，颜色和深度信息集成在一起，可以正确检测到显着物体。背景估计和中央显着性集成彻底消除了背景。该算法会生成一个健壮的明显对象。此处提供的实验结果表明，与最近的，高度引用的和密切相关的十四个最新方法，三个公开可用的复杂RGBD数据集和六个评估参数相比，该方法的性能更好。