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Matching DSIFT Descriptors Extracted from CSLM Images
Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania Light Microscopy and Screening Center, Swiss Federal Institute of Technology, Zurich, Switzerland
Electric Engineering Department, Valahia University of Targovi?te, Targovi?te, Romania
Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania
Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania
- 1 Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania Light Microscopy and Screening Center, Swiss Federal Institute of Technology, Zurich, Switzerland
- 2 Electric Engineering Department, Valahia University of Targovi?te, Targovi?te, Romania
- 3 Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania
- 4 Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania
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Abstract
The matching of local descriptors represents at this moment a key tool in computer vision, with a wide variety of methods designed for tasks such as image classification, object recognition and tracking, image stitching, or data mining relying on it. Local feature description techniques are usually developed so as to provide invariance to photometric variations specific to the acquisition of natural images, but are nonetheless used in association with biomedical imaging as well. It has been previously shown that the matching of gradient based descriptors is affected by image modifications specific to Confocal Scanning Laser Microscopy (CSLM). In this paper we extend our previous work in this direction and show how specific acquisition or post-processing methods alleviate or accentuate this problem.
KeywordsLocal FeaturesLocal DescriptorsFeature MatchingSIFTCSLM
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