A biological model for distortion-invariant target recognition

Iftekharuddin, Khan M.; Power, Gregory J.

doi:10.1109/ijcnn.2001.939083

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…Physiological evidence shows that a biological vision system employs RL as one of the key functions in robust target recognition in a complex and stochastic environment. 11 The use of RL in target recognition problem is first reported in Ref. 12 for finding the optimal path in an edge extracted image.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning approach in rotated image recognition and its convergence analysis

Iftekharuddin

2006

SPIE Proceedings

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One of the major problems in automatic target recognition (ATR) involves the recognition of images in different orientations. In a classical training-testing setup for an ATR for rotated targets using neural networks, the recognition is inherently static, and the performance is largely dependent on the range of orientation angles in the training process. To alleviate this problem, we propose a reinforcement learning (RL) approach for the ATR of rotated images. The RL is implemented in an adaptive critic design (ACD) framework wherein the ACD is mainly composed of the neuro-dynamic programming of an action network and a critic network. The proposed RL provides an adaptive learning and object recognition ability without a priori training. Numerical simulations demonstrate that the proposed ACD-based ATR system can effectively recognize rotated target with the whole range of 180 • rotation. Analytic characterization of the learning algorithm provides a sufficient condition for its asymptotic convergence. Finally, we obtain an upper bound for the estimation error of the cost-to-go function under the asymptotic convergence condition.

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Section: Introductionmentioning

confidence: 99%

A reinforcement learning approach in rotated image recognition and its convergence analysis

Iftekharuddin

2006

SPIE Proceedings

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“…In a previous work [1], we have investigated a biologically inspired distortion invariant information processing model for automatic target recognition (ATR). The attention mechanism, which is an integral part of biological information processing systems, may provide a reliable tool for robust ATR.…”

Section: Introductionmentioning

confidence: 99%

<title>Techniques in biologically inspired computational vision</title>

Iftekharuddin

2005

SPIE Proceedings

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The challenge of effectively focusing a large array of vision system in time-constrained and uncertain environments is a daunting challenge. The biology provides us some clues as higher-level organisms routinely accomplish complex tasks through iterative learning and invariance processing. In this talk, we discuss the present state-of-the-art in biological vision and recent computing models for recognition. We investigate invariance processing and reinforcement learning as some of the key ingredients for such computing. We present few implementations of these novel computing concepts.

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A Biologically Inspired Dynamic Model for Object Recognition

Iftekharuddin¹,

Li²,

Siddiqui³

Understanding Complex Systems

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A biological model for distortion-invariant target recognition

Cited by 11 publications

References 37 publications

A reinforcement learning approach in rotated image recognition and its convergence analysis

A reinforcement learning approach in rotated image recognition and its convergence analysis

<title>Techniques in biologically inspired computational vision</title>

A Biologically Inspired Dynamic Model for Object Recognition

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