Automated tracking and classification of infrared images

The problem of automatic target recognition (ATR) and image classification has been active research fields in image processing and neural networks. In this paper, we explore novel ATR techniques such as object pre-processing, tracking and classification for sequence of infrared (IR) targets. We attempt to track the IR objects automatically without the prior knowledge of the object. We enhance our algorithm to track multiple objects in image frames based on the knowledge of the histogram of the targets. We also propose algorithms for for classification of IR targets. We perform image segmentation and extract intensity and edge features of the object. Finally, we exploit these different types of features such as statistical (based on intensity) and shape (based on edge information) of the object for classification using a self-organizing map (SOM) classifier.

[1]  Jim Schroeder Automatic Target Detection and Recognition Using Synthetic Aperture Radar Imagery , 2002 .

[2]  Khan M. Iftekharuddin,et al.  Constraints in distortion-invariant target recognition system simulation , 2000, SPIE Optics + Photonics.

[3]  M. Karim,et al.  Rotation‐invariant target recognition using an amplitude‐coupled minimum‐average correlation‐energy filter , 1996 .

[4]  M. M. Bayoumi,et al.  Affine invariant object recognition using dyadic wavelet transform , 2000, 2000 Canadian Conference on Electrical and Computer Engineering. Conference Proceedings. Navigating to a New Era (Cat. No.00TH8492).

[5]  Reginald L. Lagendijk,et al.  Block-adaptive image identification and restoration , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[6]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[7]  Ayanna M. Howard A novel information fusion methodology for intelligent terrain analysis , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[8]  Bir Bhanu,et al.  Predicting object recognition performance under data uncertainty, occlusion and clutter , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[9]  Paul D. Gader,et al.  Automatic target detection using entropy optimized shared-weight neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[10]  Khan M. Iftekharuddin,et al.  Multiobject detection of targets with fine details, scale and translation variations , 1998 .

[11]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[12]  Jun Liu,et al.  Feature-based target recognition with a Bayesian network , 1996 .

[13]  James Ting-Ho Lo,et al.  An adaptive method of training multilayer perceptrons , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[14]  Mahmoud I. Khalil,et al.  Affine invariants for object recognition using the wavelet transform , 2002, Pattern Recognit. Lett..