Abstract We develop a distance metric for clustering and classification algorithms which is invariant to affine transformations and includes priors on the transformation parameters...
Most visualization systems fail to convey uncertainty within data. To provide a way to show uncertainty in similar hierarchies, we interpreted the differences between two tree stru...
Bongshin Lee, George G. Robertson, Mary Czerwinski...
This paper introduces a supervised discriminant Hausdorff distance that fits into the framework for automatic face analysis and recognition proposed in [1]. Our proposal relies so...
We study Euclidean embeddings of Euclidean metrics and present the following four results: (1) an O(log3 n √ log log n) approximation for minimum bandwidth in conjunction with a ...
The Structural SIMilarity Measure (SSIM) combined with the sequential Monte Carlo approach has been shown [1] to achieve more reliable video object tracking performance, compared ...
Artur Loza, Fanglin Wang, Jie Yang, Lyudmila Mihay...