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.
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 (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262730.
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