We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Images of an object undergoing ego- or camera- motion
often appear to be scaled, rotated, and deformed versions
of each other. To detect and match such distorted patterns
to a s...
We present a method for figure-ground segregation of moving objects from monocular video sequences. The approach is based on tracking extracted contour fragments, in contrast to ...
Graph matching is an important component in many object recognition algorithms. Although most graph matching algorithms seek a one-to-one correspondence between nodes, it is often...
Yakov Keselman, Ali Shokoufandeh, M. Fatih Demirci...
In this paper, we propose a novel image similarity learning approach based on Probabilistic Feature Matching (PFM). We consider the matching process as the bipartite graph matchin...