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Ranger is a MatLab™ code for the identification of atoms and atomic-columns in atomic resolution dark-filed STEM images. It is designed to be highly automated and self-optimising to minimise the need for user interaction. The code supports crystal images, grain / twin boundaries, crysta edges and nano-particles. To achieve this a robust peak-finding search algorithm is used that requires no prior knowledge of image magnification or orientation and that identifies genuine features based on:

The distance between the features (column-column separation),
Intensity of local signal at every region in the image, and
Widths of fitted Gaussians for all candidate features.

To identify the most appropriate feature-feature spacing values are trialled iteratively. A user display shows the features that are identified at each step during this initial optimisation phase as well as a plot of the total number of features. This graph updates with each iteration and gradually a profile is created that is used to identify the optimum feature-separation.

After the optimum feature separation is found this value is used to generate a course estimate of the peak-positions with integer pixel-coordinates. The user can select to perform an optional 2D Gaussian refinement of these estimates to yield sub-pixel precision estimates of the feature coordinates.

In these cases the majority of features will 'move' by less than one pixel in X or Y, with larger shifts only being necessary when image-noise degraded the initial integer-pixel search results.

Supported File Types

Gatan Digital Micrograph - .dm3 .
Tital TIA Image Files - .ser .
Graphics Interchange Format - .gif .
Joint Photographic Experts Group - .jpg * .
MatLab Variable File - .mat ^† .
Text File - .txt .

* Note, using a lossey compressed format such as JPEGs are not reccomended as processing of compressed images will severly limit performance.
^† If using a MatLab .mat file, the file must contain only a single variable with the image as a m x n matrix.