Recognition of multiple 3-D objects based on Markov random field models
Shengjin Wang · Journal of Tsinghua University(Science and Technology) · 2005
Computer vision systems can not easily identify 3-D objects. This paper presents an 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 used to model the geometric distribution of the key object nodes. Flexible matching, which seeks to find an accurate correspondence map between the key points of two images, combines the local similarities and the geometric relations using the highest confidence first method. Then, a global similarity value is calculated for the object recognition. The algorithm was evaluated using the Coil-100 object database, which consists of 7 200 images of 100 objects. When the numbers of templates for each object were varied from 4, 8, 18 to 36, the object recognition rates were 95.75%, 99.30%, 100.0% and 100.0%, which are much higher than those of previous algorithms. This excellent recognition performance indicates that the approach is well-suited for appearance-based object recognition.