3D object recognition with a linear time‐varying system of overlay layers

• Main Authors: Mohammad Sohrabi Nasrabadi, Reza Safabakhsh
• Format: Article
• Language: English
• Published: Wiley 2021-08-01
• Series: IET Computer Vision
• Online Access: https://doi.org/10.1049/cvi2.12029

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 L2 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.