NDLI: A biologically-inspired computational model for transformation invariant target recognition

Content Provider	IEEE Xplore Digital Library
Author	Iftekharuddin, K.M. Yaqin Li
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Memphis, Memphis, TN (Iftekharuddin, K.M.; Yaqin Li)
Abstract	Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion. In this work, we investigate a biologically-inspired computational modeling approach that exploits reinforcement learning (RL) for transformation-invariant image recognition. The RL is implemented in an adaptive critic design (ACD) framework to approximate the neuro-dynamic programming. Two ACD algorithms such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) are investigated and compared for transformation invariant recognition. The two learning algorithms are evaluated statistically using simulated transformations in 2-D images as well as with a large-scale UMIST 2-D face database with pose variations. Our simulations show promising results for both HDP and DHP for transformation-invariant image recognition as well as face authentication. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. On the other hand, HDP is more robust than DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less.
Starting Page	1049
Ending Page	1056
File Size	323447
Page Count	8
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4633928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Visualization Biology Biological system modeling Image recognition Dynamic programming Image resolution
Content Type	Text
Resource Type	Article

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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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