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Updated: Aug 6, 2026

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
Published on: August 2, 2018
Using the low-resolution properties of correlated images to improve the computational efficiency of eigenspace
Kishor Saitwal1, Anthony A Maciejewski, Rodney G Roberts
1Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA. kishor.saitwal@colostate.edu
Abstract:
Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high-resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs well on arbitrary video sequences.
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