Open Access3D object recognition with a linear time‐varying system of overlay layers
Author(s) -
Sohrabi Nasrabadi Mohammad,
Safabakhsh Reza
Publication year - 2021
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/cvi2.12029
Subject(s) - computer science , cognitive neuroscience of visual object recognition , overlay , artificial intelligence , computer vision , object (grammar) , pattern recognition (psychology) , operating system
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.