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Proceedings Paper

Deep neural network for precision multi-band infrared image segmentation
Author(s): Thomas Lu; Alexander Huyen; Kevin Payumo; Luis Figueroa; Edward Chow; Gilbert Torres
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Paper Abstract

Image segmentation is one of the fundamental steps in computer vision. Separating targets from background clutter with high precision is a challenging operation for both humans and computers. Currently, segmenting objects from IR images is done by tedious manual work. The implementation of a Deep Neural Network (DNN) to perform precision segmentation of multi-band IR video images is presented. A customized pix2pix DNN with multiple layers of generative encoder/decoder and discriminator architecture is used in the IR image segmentation process. Real and synthetic images and ground truths are employed to train the DNN. Iterative training is performed to achieve optimum accuracy of segmentation using a minimal number of training data. Special training images are created to enhance the missing features and to increase the segmentation accuracy of the objects. Retraining strategies are developed to minimize the DNN training time. Single pixel accuracy has been achieved in IR target boundary segmentation using DNNs. The segmentation accuracy between the customized pix2pix DNN and simple thresholding, GraphCut, simple neural network and ResNet models are compared.

Paper Details

Date Published: 27 April 2018
PDF: 12 pages
Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064904 (27 April 2018); doi: 10.1117/12.2305134
Show Author Affiliations
Thomas Lu, NASA/Jet Propulsion Lab./Caltech (United States)
Alexander Huyen, NASA/Jet Propulsion Lab./Caltech (United States)
Kevin Payumo, Univ. of California Irvine (United States)
Luis Figueroa, Occidental College (United States)
Edward Chow, NASA/Jet Propulsion Lab./Caltech (United States)
Gilbert Torres, Naval Air Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 10649:
Pattern Recognition and Tracking XXIX
Mohammad S. Alam, Editor(s)

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