Supporting artificial intelligence with artificial images

Aurdal, Lars
Brattli, Alvin Andreas
Glimsdal, Eirik
Klausen, Runhild Aae
Løkken, Kristin Hammarstrøm
Palm, Hans Christian
Kunstig intelligens
Infrarød avbildning
Aurdal, Lars; Brattli, Alvin Andreas; Glimsdal, Eirik; Klausen, Runhild Aae; Løkken, Kristin Hammarstrøm; Palm, Hans Christian. Supporting artificial intelligence with artificial images. Proceedings of SPIE, the International Society for Optical Engineering 2018
Size: 2M
Infrared (IR) imagery is frequently used in security/surveillance and military image processing applications. In this article we will consider the problem of outlining military naval vessels in such images. Obtaining these outlines is important for a number of applications, for instance in vessel classification. Detecting this outline is basically a very complex image segmentation task. We will use a special neural network for this purpose. Neural networks have recently shown great promise in a wide range of image processing applications, image segmentation is no exception in this regard. The main drawback when using neural networks for this purpose is the need for substantial amounts of data in order to train the networks. This problem is of particular concern for our application due to the difficulty in obtaining IR images of military vessels. In order to alleviate this problem we have experimented with using alternatives to true IR images for the training of the neural networks. Although such data in no way can capture the exact nature of real IR images, they do capture the nature of IR images to a degree where they contribute substantially to the training and final performance of the neural network.
View Meta Data