Intelligent multi-spectral IR image segmentation

Thomas Lu; Andrew Luong; Stephen Heim; Maharshi Patel; Kang Chen; Tien-Hsin Chao; Edward Chow; Gilbert Torres

doi:10.1117/12.2262730

1 May 2017 Intelligent multi-spectral IR image segmentation

Thomas Lu, Andrew Luong, Stephen Heim, Maharshi Patel, Kang Chen, Tien-Hsin Chao, Edward Chow, Gilbert Torres

Proceedings Volume 10203, Pattern Recognition and Tracking XXVIII; 1020303 (2017) https://doi.org/10.1117/12.2262730
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

This article presents a neural network based multi-spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. This article compares the neural network based segmentation method to the Wavelet-threshold and Grab-Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-spectral IR images.

Conference Presentation

Citation Download Citation

Thomas Lu, Andrew Luong, Stephen Heim, Maharshi Patel, Kang Chen, Tien-Hsin Chao, Edward Chow, and Gilbert Torres "Intelligent multi-spectral IR image segmentation", Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 1020303 (1 May 2017); https://doi.org/10.1117/12.2262730

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