Provides functionality to perform a likelihood-free method for estimating the parameters of complex models
    that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins
    with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution
    of model parameters. Accepted points result in model predictions that are within the initially specified tolerance
    intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of
    multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the
    acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the
    adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <arXiv:1804.02090>.

Improve optical character recognition by binarizing images. The package focuses primarily on local adaptive thresholding algorithms. 
    In English, this means that it has the ability to turn a color or gray scale image into a black and white image. This is particularly useful
    as a preprocessing step for optical character recognition or handwritten text recognition.

An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.

An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). 
    The algorithm is explained at <doi:10.5201/ipol.2016.175>.

An implementation of the "FAST-9" corner detection algorithm explained in the paper 'FASTER and better: A machine learning approach to corner detection' by Rosten E., Porter R. and Drummond T. (2008), available at <doi:10.48550/arXiv.0810.2434>.
    The package allows to detect corners in digital images.