Transformation Invariant On-Line Target Recognition

Abstract

Transformation invariant automatic target recognition (ATR) has been an active research area due to its widespread applications in defense, robotics, medical imaging and geographic scene analysis. The primary goal for this paper is to obtain an on-line ATR system for targets in presence of image transformations, such as rotation, translation, scale and occlusion as well as resolution changes. We investigate biologically inspired adaptive critic design (ACD) neural network (NN) models for on-line learning of such transformations. We further exploit reinforcement learning (RL) in ACD framework to obtain transformation invariant ATR. We exploit two ACD designs, such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) to obtain transformation invariant ATR. We obtain extensive statistical evaluations of proposed on-line ATR networks using both simulated image transformations and real benchmark facial image database, UMIST, with pose variations. Our simulations show promising results for learning transformations in simulated images and authenticating out-of plane rotated face images. Comparing the two on-line ATR designs, HDP outperforms DHP in learning capability and robustness and is more tolerant to noise. The computational time involved in HDP is also less than that of DHP. On the other hand, DHP achieves a 100% success rate more frequently than HDP for individual targets, and the residual critic error in DHP is generally smaller than that of HDP. Mathematical analyses of both our RL-based on-line ATR designs are also obtained to provide a sufficient condition for asymptotic convergence in a statistical average sense.

Discrete Globalised Dual Heuristic Dynamic Programming in Control of the Two-Wheeled Mobile Robot

“…There are implementations of RL algorithms in mobile robot path planning [24], urban traffic signal control [25], or power system control [26], but these are mostly implementations of the Q-learning algorithm [10]. There are not many recent articles concerning ADP algorithms; the example is the application of ADHDP algorithm for a static compensator connected to a power system [27] or HDP and DHP algorithms in target recognition [28]. Application of the ADP algorithms in the control of the wheeled mobile robot is presented in [4] and in the trajectory generating process in [29].…”

The ripple pond: enabling spiking networks to see

“…Since many of the approaches above are based on mammalian vision and achieving the accuracy and resolution of mammalian vision, they are very complex and can only be truly implemented on computers (Sonka, 2007), (Gong, 2012), (Norouzi 2009), (Nakamura, 2002), (Iftekharuddin, 2011), (Meng 2011), (Serre, 2005), (Jhuang, 2007), sometimes with very slow computation times. Other implementations that have been demonstrated on hardware (Serrano-Gotarredona, 2009), (Rasche, 2007), (Folowosele, 2011) have been successful in proving that vision can be achieved for small, low-power robots, UAVs, and remote sensing applications, however; the functionality of such neuromorphic systems has been limited.…”

“…Generally most biologically based object recognition solutions have been based on vertebrate vision, in particular mammalian vision, and have used either statistical methods (Sountsov et al, 2011 ; Gong et al, 2012 ), signal processing techniques (such as log-polar filters) (Cavanagh, 1978 ; Reitboeck and Altmann, 1984 ), artificial neural networks (i.e., non-spiking neural networks) (Nakamura et al, 2002 ; Norouzi et al, 2009 ; Iftekharuddin, 2011 ), and more recently, spiking neural networks (Serre et al, 2005 ; Rasche, 2007 ; Serrano-Gotarredona et al, 2009 ; Meng et al, 2011 ).…”

Generative-Adversarial-Networks-based Ghost Recognition

“…However, the poor trade-off of image quality and imaging time limits the application of GI. Nowadays, target recognition technique is an important approach, whether for economy or military [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]. Conventional recognition technique is Y.…”