Clustering algorithms for a PC based hardware implementation of the unsupervised classifier for the Shuttle ice detection system

The author introduces a near-real-time method of image processing in a PC-based environment. A segmentation technique based on unsupervised classification is implemented. A prototype for the detection of ice formation on the external tank (ET) of the Space Shuttle is being developed. The objective is to be able to do an online classification of the ET images into distinct regions denoting ice, frost, wet or dry areas. The images are acquired with an infrared camera and digitized before being processed by a computer to yield a false color-coded pattern, with each color representing a region. A two-monitor PC based setup is used for image processing. Various techniques for classification both supervised and unsupervised, are being investigated for developing a methodology. The implementation of two adaptive algorithms for image segmentation is discussed. The K -means algorithm is compared to another algorithm based on adaptive estimation of region boundaries