A key point for good camouflage results in the thermal infrared domain lies in the ability of the camouflage system to adapt to the thermal emission behavior of the surrounding background. In order to obtain reliable assessments of the camouflage effectiveness, evaluation has to take place under various environment conditions. The combination of the different results leads to a assessment measure with the demanded reliability. The objective quantization of the individual camouflage effectiveness and the following combination is very difficult to achieve by human operators. Therefore an Infrared Camouflage Effectiveness Assessment Tool (ICEAT) has been developed, which needs only minor human interaction and supports the automated combination of the results of various test scenes. In a first step hot spots of the object and the background are detected. In a second phase various features are calculated which are combined to a single assessment measure in the third phase by using fuzzy logic. The fuzzy logic approach has the advantage that the customization of the ICEAT can be achieved by simply modifying the used membership functions.
An accurate method to detect and classify military vehicles based on the recognition of shapes is presented in this work. FFT-Descriptors are used to generate a scale, translation and rotation invariant characterization of the shape of such an object. By interpreting the boundary pixels of an object as complex numbers it is possible to calculate an FFT-Descriptor based on the spectrum of a Fast Fourier Transform of these numbers. It is shown that by using this characterization it is possible to match such representations with models in a database of known vehicles and thereby gaining a highly robust and fault tolerant object classification. By selecting a specific number of components of a FFT-Descriptor the classification process can by tailored to different needs of recognition accuracy, allowed shape deviation and classification speed.
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