Recognition of partially occluded objects using probabilistic ARG (attributed relational graph)-based matching

摘要

In this paper, we propose a novel 2-D partial matching algorithm using the model-based probabilistic analysis of feature correspondences in the relation vector space, which is quite robust to shape variations due to noise and occlusions and invariant to 2-D geometric transformations as well. We represent an object using the attributed relational graph (ARG) model with nodes (features) of a set of the binary relation vectors. By defining relation vector space which can describe the structural information of an object centered at a specific feature, and modeling distortions due to partial occlusion or the input noise statistically in this space, lost features can be easily identified, so that the partial matching is performed efficiently. The proposed partial matching algorithm consists of two-phases. First, a finite number of candidate subgraphs are selected in an image, by using the logical constraint embedding local and structural consistency as well as the correspondence measure between model and image features. Second, the feature loss detection is done iteratively by the error detection and voting scheme through the error analysis in the relation vector space. Experimental results on real images demonstrate that the proposed algorithm has superior performance to those of the conventional relaxation algorithms, by localizing target objects robustly and correctly even in severely noisy and occluded scenes.