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A common problem in image analysis is the transformation-invariant estimation of the similarity between a query image and a set of reference images representing different classes. This typically requires the comparison of the distance between the query image and the transformation manifolds of the reference images. The tangent distance algorithm is a popular method that estimates the manifold distance...
Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pair-wise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are...
This paper addresses the reconstruction of high-resolution omnidirectional images from multiple low-resolution images with inexact registration. When omnidirectional images from low-resolution vision sensors can be uniquely mapped on the 2-sphere, such a reconstruction can be described as a transform-domain super-resolution problem in a spherical imaging framework. We describe how several spherical...
The characterization of signals and images in manifolds often lead to efficient dimensionality reduction algorithms based on manifold distance computation for analysis or classification tasks. We propose in this paper a method for the discretization of signal manifolds given in a parametric form. We present an iterative algorithm for the selection of samples on the manifold that permits to minimize...
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