Updated May 2015
S.B. Kumar and P. Milanfar,
"One Shot Detection with Laplacian Object and Fast
Matrix Cosine Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
, To appear, 2015
One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among the local descriptors, embedding the query descriptors into a low dimensional but discriminatory subspace.
A. Kheradmand and P. Milanfar,
"A General Framework for Regularized, Similarity-based Image Restoration
IEEE Transactions on Image Processing
, vol. 23, no. 12, pp. 5136-5151, Dec. 2014
We've developed an iterative graph-based framework for image restoration based on a new deﬁnition of the normalized graph Laplacian. We propose a cost function which consists of a new data ﬁdelity term
and a regularization term derived from the speciﬁc deﬁnition of the normalized graph Laplacian. The speciﬁc form of the cost function allows us to
render the spectral analysis for the algorithm. The approach is general in the sense that we have shown its effectiveness for different restoration problems including deblurring, denoising, and sharpening.