Evaluation of a target recognition algorithm using the sensor model

In this work, the performance of a target recognition algorithm is evaluated on images distorted by blur and noise. We simulate such image distortions using our Sensor Model Software. The software includes a blur model based on the modulation transfer function and a 3-D noise model. The main objective of the work is to show the importance of the sensor model for evaluating the performance of target recognition algorithms. An example algorithm, which is based on the Scale Invariant Feature Transform has been selected for this evaluation. Under conditions for which atmospheric factors (turbulence and aerosol) are dominant, image degradations have been simulated and their effect on the algorithm has been evaluated using relevant measurement criteria. This work is believed to shed light to the efforts for increasing the efficiency of target detection, tracking and recognition algorithms in the infrared band by employing synthetic image generators.