Recognition of 3-D objects based on Markov random field models

Abstract

The recognition of 3-D objects is quite a difficult task for computer vision systems. This paper presents a new object framework, which utilizes densely sampled grids with different resolutions to represent the local information of the input image. A Markov random field model is then created to model the geometric distribution of the object key nodes. Flexible matching, which aims to find 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 method. Afterwards, a global similarity is calculated for object recognition. Experimental results on Coil-100 object database, which consists of 7 200 images of 100 objects, are presented. When the numbers of templates vary from 4, 8, 18 to 36 for each object, and the remaining images compose the test sets, the object recognition rates are 95.75 %, 99.30 %, 100.0 % and 100.0 %, respectively. The excellent recognition performance is much better than those of the other cited references, which indicates that our approach is well-suited for appearance-based object recognition.

Keywords

Pattern recognition, 3-D object recognition, Markov random field, Highest confidence first