NDLI: Rotational invariant biologically inspired object recognition

Content Provider	IET Digital Library
Author	karimi, Hiwa Sufi Mohammadi, Karim
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.
Starting Page	3762
Ending Page	3773
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1621
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Added Layer Applied Image Biologically Inspired Hierarchical Model Computer Vision And Image Processing Technique Dominant Orientation Feature Extraction Filtering Method in Signal Processing Gabor Filter Image Classification Image Matching Image Recognition Image Rotations Image Variations Location Changes Object Detection Object Recognition Object Recognition Purposes Repeatable Object Recognition Process RIMAX Robust Method Rotational Invariant Biologically Inspired Object Recognition Rotational Invariant HMAX Standard HMAX Superior Technique Testing Image Training Phase Visual Database
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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