17 June 1996 Class of methods for representation and recognition of multiscale objects
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When an airborne imagery sensor moves from far to near, for a non-zoom imaging system, the image sequence from a scene will vary with different scales dramatically, state-of-the-art methods based on a single invariant feature or a simple feature, such as moments invariant, shape specific points, topological features and Fourier descriptor, etc., are rendered useless for representing and recognizing a multiscale object in this specific image sequence. Even the image gray-pyramid technique, which has great potential for pattern recognition by template matching with different resolutions, can not provide satisfactory performance due to not knowing exactly the resolution of real images, so there is an increasing need for improvement in multiscale object rendering and recognition. In this paper, we develop a class of algorithms for representation and recognition of a multiscale object in the specific image sequence taken from a sensor moving from far to near, which is called hierarchy features model (HFM) and sequential object recognition algorithm (SORA) based on this hierarchy features model, respectively, and intended to represent a size-changing object and recognize it. Experimental results with many real visual and infrared images and simulated images have shown that when a non-zoom imagery sensor moves from far to near, the HFM is suitable to represent a multiscale object, and the SORA available to recognize it.
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Guoyou Wang, Wei Li, Tianxu Zhang, and Jiaxiong Peng "Class of methods for representation and recognition of multiscale objects", Proc. SPIE 2742, Targets and Backgrounds: Characterization and Representation II, (17 June 1996); doi: 10.1117/12.243027; https://doi.org/10.1117/12.243027

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