Low-level computer vision techniques for processing of extensive air shower track images

An extensive air shower can be observed as a bright spot moving through the field of view of an orbital fluorescence detector. A challenging part of the air shower recognition is segmentation of its track. The issues arise from a low signal to noise ratio. This paper provides a short review of selected low-level computer vision techniques such as filtering and thresholding methods, which are for a demonstration applied to a composite simulated air shower image. The article should provide a shortlist of algorithms that can be applied as a part of more complex event classification or reconstruction procedure.