<title>Object tracking: an ART based phase filtering approach</title>

Abstract

In a satellite image the background is always regarded as the noise with high frequency. If the background can be removed, the phase only match filter (POMF), which is applied to registration of the template, can get an obvious pulse at the correct position where the template object locates. This article proposes an algorithm consisting of an artificial neural network and POMF method to tracking an object in a series of satellite images with variant intensity level. The notable adaptive resonance theory (ART) neural network is used as a high frequency noise filter for the POMF. In the proposed method, a template image is divided into several sub-images, which are used as the primitive texture blocks of the template. Since the ART has self-categorizing capability, it can learn the primitive patterns contained in the template. Once the source image is presented to the ART for registration, the network can recognize the texture blocks not contained in the template image. If these blocks are masked in the source image, the refined image contains only the blocks similar to the template image. Furthermore, an image that contains the texture blocks of the template image is obtained using the template as a mask to restore the blocks previously moved. The phase match filter is then applied to detecting the exact location of the template object on the source image. The proposed algorithm is examined by means of several satellite images. From the experimental results, the proposed method demonstrates that the performance of POMF can be successfully improved by reducing the high frequent noise of the background in the satellite images.