چكيده به لاتين
Abstract:
Small target detection in infrared images taken by Electro-optical systems is a challenge in surveillance and early warning systems.in this infrared systems, targets are seen as tiny dots generally. In the infrared spectrum, Thermal radiation in the Background of target and the noise of sensor can produce strong noise that is really annoyed, so the target would be lost in the energy derived from background of clutter and noise. Since mass production of false alerts it cannot be detected with usual methods. In this thesis, strategies are introduced and developed for more accurate detection of targets by reducing background clutter in infrared images.
The small Target Detection algorithms are considered in two categories:
The first algorithm is Detection before tracking algorithm (DBT) that is including detection algorithm based on information of a frame and the second one is tracking before Detection algorithm (TBD) where detection in this, is dependent on previous information frames.
Because of The first one has a lower computational load and better performance in target detection, generally, the common species transformation of morphology used in this category especially the transformation of Top-Hat, Hit-Miss and Toggle. The transformation of morphology, is one of the best methods of Reduce the effect of background and increase the SNR and thus it increases the efficiency of target detection in image processing.
In this thesis, we used the simulation and analysis of the transformation of morphology in Point Target Detection in Infrared Images Based on Top-Hat, Hit-Miss and Toggle transformation. This transformation model using genetic algorithms try to achieve optimum structural element to increase their efficiency.
The results show that in infrared images with different and complex backgrounds, the proposed method compared to the conventional methods, has improved the SNR measure by about 30 % and BSF measure has improved to 20 %.
