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Real-time image stabilization based on a multiscale top hat filter

This paper presents a new Feature Matching approach for Real Time Image Stabilization based on features provided by a Multi-Scale Top Hat Filter. The idea is to calculate bright and dark regions of different sizes in two images, calculate 'image' and 'match history' specific characteristics for each region, match the regions from the two images based on their characteristics and then to estimate the x and y translations and rotation between the two images based on the region matches. Given the real time requirement and the performance characteristics of today's generation of Digital Signal Processors we implemented the Multi-Scale Top Hat Filter with a lower resolution and compensated for the precision lost by using the Multi-Window Correlation algorithm. The successfully matched regions from the Multi- Scale Top Hat Filter provide exactly the kind of data to the Multi-Window Correlation algorithm which tend to be critical for its application. Automatic parameter adaptation and the Top Hat Filter specific nature of the detected regions provide a robust and adaptive algorithm dealing well with different scenes and environmental changes. We present the details of this approach including its real time implementation and its role in our Automatic Target Detection and Tracking system.