New nonuniform sampling image representation method and its application in knowledge-based active pattern recognition

The research of Image representation method based on nonuniform sampling and the development of the foveated sensors are active research fields in recent years. We propose in this paper a nonuniform sampling image representation method based on an improved log-polar transform and apply it into the knowledge-based active pattern recognition. The novelty of our method lies in three aspects. First, compared with other nonuniform representation methods, our method provides a flexible structure between the pure nonunifrom sampling and the classical uniform sampling representation and imitates the focus characteristic ofhuman vision. The size ofthe areas ofinterest which is uniformly sampled with the highest resolution can be adjusted arbitrarily according to the knowledge of vision task and objects. Second, we propose a knowledge-based method to decide "where to look next" based on the fovea-periphery structure. By introducing the concept of knowledge grain, knowledge ofobjects is organized hierarchically, from coarse to fine. We use fine grain knowledge to do the accurate pattern recognition in fovea area and use coarse grain knowledge to locate the fixation point candidates in periphery. Third, we give a general paradigm for knowledge-based active pattern recognition. Nonuniform sampling transform is imposed on the input image to obtain the fovea-periphery stmcture first. Then different grain ofknowledge is used to solve the problems of"what it is " and "where it is "in fovea and periphery. The above procedure is repeated until no more fixation points can be found or the goal ofvision task has already been reached. Experimental result in this paper is demonstrates our idea to be a valid one.