Matching DSIFT Descriptors Extracted from CSLM Images


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.

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S. G. Stanciu, D. Coltuc, D. E. Tranca and G. A. Stanciu, "Matching DSIFT Descriptors Extracted from CSLM Images," Engineering, Vol. 5 No. 10B, 2013, pp. 199-202. doi: 10.4236/eng.2013.510B042.

Conflicts of Interest

The authors declare no conflicts of interest.


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