Değişik arka planlardan ve farklı uzaklıklardan elde edilen kızılötesi görüntülerde otomatik küçük hedef tespiti.

Automatic detection of small targets in various backgrounds from far distances is a very challenging problem. Background clutter and small target size are the main difficulties which should be solved while reaching a very high detection performance as well as a very low computational load. In this thesis, various methods such as Top-Hat and Wavelet Transform, edge, filtering, saliency and feature based algorithms are investigated. All of the methods are compared using some realistic test scenarios, which are created synthetically. Precision, recall, processing time and number of user dependent parameters are used to evaluate the approaches. The comparative results indicate that no algorithm can detect the target with having high precision and recall at the same time in none of the scenarios. Besides, we have realized that the methods that are used in pre-processing, detection, thresholding and post-processing stages of the algorithms are very effective on the final results. Thus, the methods used in these stages are evaluated separately and the best approach for each stage is verified. Finally, an algorithm is constructed which constitutes of the best approach for each stage. However, although we end up with a very high precision rate such as 100%, the recall values are low. In this context, a postprocessing method is proposed which increases the recall value while keeping the precision at 100% in prepared test scenarios. It is analyzed and indicated that the proposed post-processing method increases the recall value averagely 130% in all prepared test scenarios.M.S. - Master of Scienc