Feature-Based Partially Occluded Object Recognition

We propose a framework to combine geometry, color and texture information among pairwise feature points into a graph and find the correct assignments from all candidates using graph matching techniques. Because of our informative similarity matrix, objects can be still recognized under severe occlusion and the matching errors can be greatly reduced when images are taken from very different view angles and partial occluded.