Optimizing feature selection strategy for adaptive object identification in noisy environment

Abstract

We present the development of a multi-stage automatic target recognition (MS-ATR) system for computer vision in robotics. This paper discusses our work in optimizing the feature selection strategies of the MS-ATR system. Past implementations have utilized Optimum Trade-off Maximum Average Correlation Height (OT‐MACH) filtering as an initial feature selection method, and principal component analysis (PCA) as a feature extraction strategy before the classification stage. Recent work has been done in the implementation of a modified saliency algorithm as a feature selection method. Saliency is typically implemented as a “bottom-up” search process using visual sensory information such as color, intensity, and orientation to detect salient points in the imagery. It is a general saliency mapping algorithm that receives no input from the user on what is considered salient. We discuss here a modified saliency algorithm that accepts the guidance of target features in locating regions of interest (ROI). By introducing target related input parameters, saliency becomes more focused and task oriented. It is used as an initial stage for the fast ROI detection method. The ROIs are passed to the later stages for feature extraction and target identification process.