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This plugin combines the idea of using fiduciary markers, local descriptors and geometric hashing and applies global optimization. It can register an arbitrary number of partially overlapping point clouds. It is robust with respect to the amount of incorporated beads, bead distribution, amount of overlap, and can reliably detect non-affine disturbances (e.g. abrupt agarose movement) that might occur during imaging.

This plugin parses arbitrary mathematical expressions and compute results using images as variables.

DEPRECATED (as of Dec. 2018)

This Node implements the so-called anisotropic diffusion scheme of Perona and Malik, 1990. For details on the anisotropic diffusion principles, see: http://en.wikipedia.org/wiki/Anisotropic_diffusion, and the original paper: Perona and Malik. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence (1990) vol. 12 pp. 629-639

The Options allows to use different Functions to be used for filtering.

Transposes an image by swapping its spatial dimensions.

Exports connected components data in XLS. Exports All, by T or by ROI

The method to use for thresholding. Currently only OTSU is available.

Colocalizer block.

 Fast edge and ridge detection, irrespective of their orientation.