No abstract

Object recognition is a challenging task in computer vision with numerous applications. The challenge is in selecting appropriate robust features with tolerable computing costs. Feature learning attempts to solve the feature extraction problem through a learning process using various samples of the objects. This research proposes a two-stage optimization framework to identify the structure of a first-order linear non-homogeneous difference equation which is a linear time-varying system of overlay layers (LtvoL) that construct an image. The first stage consists of the determination of a finite set of impulses, called overlay layers, by the application of a genetic algorithm. The second stage defines the coefficients of the corresponding difference equation derived from L 2 regularization. Classification of the test images is possible by a novel process exclusively designed for this model. Experiments on the Washington RGB-D dataset and ETH-80 show promising results which are comparable to those of state-of-the-art methods for RGB-D-based object recognition.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.