3D object recognition with a linear time‐varying system of overlay layers. Issue 5 (9th April 2021)

• Record Type: Journal Article
• Authors: Sohrabi Nasrabadi, Mohammad; Safabakhsh, Reza
• Is Part Of: IET computer vision. Volume 15:Issue 5(2021)
• Page Start: 380
• Page End: 391
• Publication Date: 2021-04-09
• Subjects: Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/cvi2.12029
• Languages: English
• ISSNs: 1751-9632

Abstract

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.