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A general framework is presented to realize 3D object recognition, invariant to object scaling, deformation, rotation, occlusion, and viewpoint change. This framework utilizes densely sampled grids, with different resolutions, to represent the local information of the input image. A Markov random field (MRF) model is then created to model the geometric distribution of the object key nodes. Flexible matching, which is aimed at finding the accurate correspondence map between the key points of two images, is performed by combining the local similarities and the geometric relations together using the highest confidence first (HCF) method. Afterwards, a global similarity is calculated for object recognition. Experimental results on the Coil-100 object database are presented. The excellent recognition rates achieved in all the experiments indicate that our approach is well-suited for appearance-based recognition.