Paper Info
Title
Rotational Invariant Biologically Inspired Object Recognition
Abstract
The biologically inspired hierarchical model and X (HMAX) has been one of the superior techniques for object recognition purposes. HMAX is a robust method in the presence of some image variations including illumination, different scales, and location changes. However, the performance of HMAX extremely deteriorates if the orientation of the applied images in training phase is different than the orientation of testing images. In this study, the authors propose rotational invariant HMAX (RIMAX) to overcome the existing issues in object recognition imposed by rotations in the images. To this end, they embed two new layers into the structure of the standard HMAX. In the first added layer, non-accidental properties (e.g. corners and edges) are extracted as features that lead to obtaining a repeatable object recognition process. The second added layer provides robustness of RIMAX to image rotations by normalising the dominant orientation of the extracted features. Furthermore, they considerably reduce the imposed computational load by modifying template matching strategy as well as removing multiple scales of the Gabor filter. Simulation results employ Caltech101, TUD, Caltech5, and GRAZ-02 databases that validate RIMAX outperforms the standard HMAX and the other mathematical approaches in terms of robustness, accuracy, repeatability, and speed.
Year
DOI
Venue
2020
10.1049/iet-ipr.2019.1621
IET IMAGE PROCESSING
Keywords
DocType
Volume
feature extraction, visual databases, object detection, image classification, Gabor filters, image matching, object recognition, object recognition purposes, robust method, image variations, location changes, applied images, training phase, testing images, rotational invariant HMAX, RIMAX, standard HMAX, repeatable object recognition process, added layer, image rotations, dominant orientation, rotational invariant biologically inspired object recognition, biologically inspired hierarchical model, superior techniques
Journal
14
Issue
ISSN
Citations 
15
1751-9659
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Hiwa Sufi Karimi100.34
Karim Mohammadi200.34