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Active image registration and recognition

An active feature matching technique was developed, which incorporated both local and global information in the matching process, to achieve a global optimal goodness-of-match. First, an optimal snake was developed to reduce the 2D object of interest to a 1D feature string. This snake has the capability to extract accurate information about an object's corners that contains critical important discriminant information. High performance was achieved by dividing the energy optimization process into multiple stages that optimized both performance and speed of the snake. After the objects to be matched were reduced to two feature vector strings, dynamic feature matching (DFM) was used to match these strings. DFM matched the two feature strings in a global optimally way by using the Bellman optimality principle. An active image registration system was then developed using active feature matching to obtain a partial disparity map from which a full disparity map was estimated using regularization. This system was tested on a sequence of MR functional brain images. Results showed that the brain activation map obtained from registered images was significantly improved when compared to nonregistered images. Finally, an active image recognition system was implemented based on active feature matching. This system was applied to aircraft images and results showed that the active recognition system had superior distortion tolerance over the correlation based system and maintained good performance over a wide range of distortion. This tolerance to distortion was due to its 'active' nature. In other words, it, to some extent, mimicked human vision by dynamically adjusting the matching path so that the differences due to perspective distortion were minimized.