Recognition of 3-D objects based on Markov random field models |
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Authors: | Huang Ying Ding Xiao-qing and Wang Sheng-jin |
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Affiliation: | (1) Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China |
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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 7200 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.
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Translated from Journal of Tsinghua University (Science and Technology), 2005, 45(1):28–32 (in Chinese) |
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Keywords: | Pattern recognition 3-D object recognition Markov random field Highest confidence first |
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