Target Detection using Adaptive Progressive Thresholding Based Shifted Phase-Encoded Fringe-Adjusted Joint Transform Correlator
A new target detection technique is presented in this
paper for the identification of small boats in coastal surveillance. The
proposed technique employs an adaptive progressive thresholding (APT) scheme to first process the given input scene to separate any
objects present in the scene from the background. The preprocessing
step results in an image having only the foreground objects, such as
boats, trees and other cluttered regions, and hence reduces the search
region for the correlation step significantly. The processed image is then fed to the shifted phase-encoded fringe-adjusted joint transform
correlator (SPFJTC) technique which produces single and delta-like
correlation peak for a potential target present in the input scene. A
post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. Simulation results are presented to show that the
proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in the given input scene.
[1] C. S. Weaver and J. W. Goodman, "Technique for optically convolving
two functions," Applied Optics, vol. 5, pp. 1248 - 1249, 1966.
[2] B. Javidi and C. Kuo, "Joint transform image correlation using a binary spatial light modulator at the Fourier plane," Applied Optics, vol. 27, pp. 663 - 665, 1988.
[3] A. K. Cherri and M. S. Alam, "Reference phase-encoded fringe-adjusted
joint transform correlation," Applied Optics, vol. 40, pp. 1216 - 1225, 2001.
[4] M. S. Alam and M. A. Karim, "Fringe-adjusted joint transform correlation," Applied Optics, vol. 32, pp. 4344 - 4350, 1993.
[5] M. R. Haider, M. N. Islam, M. S. Alam and J. F. Khan, "Shifted phaseencoded
fringe-adjusted joint transform correlation for multiple target
detection," Optics Communications, vol. 248, pp. 69 - 88, 2005.
[6] X. W. Chen, M. A. Karim, and M. S. Alam, "Distortion-invariant fractional power fringe-adjusted joint transform correlation," Optical
Engineering, vol. 37, no. 1, pp. 138-143, 1998.
[7] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms",
IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, pp. 62-66,1979.
[8] K.V. Asari, T. Srikanthan, S. Kumar, and D. Radhakrishnan, "A
pipelined architecture for image segmentation by adaptive progressive
thresholding," Journal of Microprocessors and Microsystems , vol. 23,
no. 8-9, pp. 493-499, December 1999.
[9] S. Kumar, K.V. Asari, and D. Radhakrishnan, "Real-time automatic extraction of lumen region and boundary from endoscopic images," IEE
Journal of Medical & Biological Engineering & Computing, vol. 37, no.
5, pp. 600-604, September 1999.
[10] M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula and A. Sharma, "Metrics for evaluating the performance of jointtransfor-correlation-based target recognition and tracking algorithms,"
Optical Engineering, vol. 44, no. 6, pp. 067005-1 - 067005-12, 2005.
[1] C. S. Weaver and J. W. Goodman, "Technique for optically convolving
two functions," Applied Optics, vol. 5, pp. 1248 - 1249, 1966.
[2] B. Javidi and C. Kuo, "Joint transform image correlation using a binary spatial light modulator at the Fourier plane," Applied Optics, vol. 27, pp. 663 - 665, 1988.
[3] A. K. Cherri and M. S. Alam, "Reference phase-encoded fringe-adjusted
joint transform correlation," Applied Optics, vol. 40, pp. 1216 - 1225, 2001.
[4] M. S. Alam and M. A. Karim, "Fringe-adjusted joint transform correlation," Applied Optics, vol. 32, pp. 4344 - 4350, 1993.
[5] M. R. Haider, M. N. Islam, M. S. Alam and J. F. Khan, "Shifted phaseencoded
fringe-adjusted joint transform correlation for multiple target
detection," Optics Communications, vol. 248, pp. 69 - 88, 2005.
[6] X. W. Chen, M. A. Karim, and M. S. Alam, "Distortion-invariant fractional power fringe-adjusted joint transform correlation," Optical
Engineering, vol. 37, no. 1, pp. 138-143, 1998.
[7] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms",
IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, pp. 62-66,1979.
[8] K.V. Asari, T. Srikanthan, S. Kumar, and D. Radhakrishnan, "A
pipelined architecture for image segmentation by adaptive progressive
thresholding," Journal of Microprocessors and Microsystems , vol. 23,
no. 8-9, pp. 493-499, December 1999.
[9] S. Kumar, K.V. Asari, and D. Radhakrishnan, "Real-time automatic extraction of lumen region and boundary from endoscopic images," IEE
Journal of Medical & Biological Engineering & Computing, vol. 37, no.
5, pp. 600-604, September 1999.
[10] M. S. Alam, A. Bal, E. H. Horache, S. F. Goh, C. H. Loo, S. P. Regula and A. Sharma, "Metrics for evaluating the performance of jointtransfor-correlation-based target recognition and tracking algorithms,"
Optical Engineering, vol. 44, no. 6, pp. 067005-1 - 067005-12, 2005.
@article{"International Journal of Information, Control and Computer Sciences:59234", author = "Inder K. Purohit and M. Nazrul Islam and K. Vijayan Asari and Mohammad A. Karim", title = "Target Detection using Adaptive Progressive Thresholding Based Shifted Phase-Encoded Fringe-Adjusted Joint Transform Correlator", abstract = "A new target detection technique is presented in this
paper for the identification of small boats in coastal surveillance. The
proposed technique employs an adaptive progressive thresholding (APT) scheme to first process the given input scene to separate any
objects present in the scene from the background. The preprocessing
step results in an image having only the foreground objects, such as
boats, trees and other cluttered regions, and hence reduces the search
region for the correlation step significantly. The processed image is then fed to the shifted phase-encoded fringe-adjusted joint transform
correlator (SPFJTC) technique which produces single and delta-like
correlation peak for a potential target present in the input scene. A
post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. Simulation results are presented to show that the
proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in the given input scene.", keywords = "Adaptive progressive thresholding, fringe adjusted filters, image segmentation, joint transform correlation, synthetic discriminant function", volume = "2", number = "5", pages = "1639-6", }