A5 problem solving paradigm: a unified perspective and new results on RHT computing, mixture based learning, and evidence combination

摘要

In this paper, the roles of grid, granular, modular structures in density learning and Hough transform (HT) like object detection, as well as the corresponding typical approaches have been systematically reviewed. Featured by five essential mechanisms (namely, acquisition, assumption, accumulation, adaptation, and assessment), a general problem solving paradigm, shortly A5 paradigm, is elaborated to provide not only a unified perspective but also new results on Hough transform (HT) like object detection, mixture based learning (RPCL learning and multi-set modelling), and evidence combination.