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AG Computational Data Analysis


Equiangular Lines by Pillar Decomposition
Symmetric Molecule-Based Feature Detector (SymFD, previously CoShREM)
Haar Wavelet-Based Perceptual Similarity Index (HaarPSI)

Equiangular Lines by Pillar Decomposition

By combining the pillar decomposition of Lemmens and Seidel 1973 with the semidefinite programming approach of Bachoc and Vallentin 2008 and Barg and Yu 2014 and further arguments from combinatorics and linear algebra, one is able to prove lower upper bounds on the size of sets of equiangular lines in various dimensions which are in some cases significantly better than the previously known bounds.

For more information, see the paper "New Upper Bounds for Equiangular Lines by Pillar Decomposition" by Emily J. King and Xiaoxian Tang, PDF. The code and supplementary materials may be found here.

Symmetric Molecule-Based Feature Detector (SymFD)

SymFD is a MATLAB toolbox providing methods for the detection and analysis of edges, ridges, and blobs in 2D images by utilizing the special symmetry properties of directionally sensitive 2D analyzing functions. Its predecessor, the Complex Shearlet-Based Edge and Ridge Measure (CoShREM) Toolbox, can also be downloaded here.

The image of a monarch butterfly used in the examples below is part of the LIVE Image Quality Assessment Database. The shown results can be reproduced by calling the respective scripts in the 'Examples' folder in the SymFD toolbox.

Edge Detection:

Monarch Edges OverlayMonarch Edges Tangent Directions OverlayMonarch Edges Curvature Overlay
SymFD Edge MeasureTangent DirectionsCurvature

Ridge Detection:

Monarch Ridges OverlayMonarch Ridges Tangent Directions OverlayMonarch Ridges Widths Overlay
SymFD Ridge MeasureTangent DirectionsRidge Widths

Blob Detection:

Monarch Blobs Overlay
Detected Blobs



  1. R. Reisenhofer, J. Kiefer and E. J. King
    Shearlet-Based Detection of Flame Fronts (PDF)
    Experiments in Fluids, vol. 57(3), 41:1-41:14, 2016.
  2. E. J. King, R. Reisenhofer, J. Kiefer, W.-Q Lim, Z. Li and G. Heygster
    Shearlet-Based Edge Detection: Flame Fronts and Tidal Flats (PDF)
    Applications of Digital Image Processing XXXVIII (Andrew G. Tescher, ed.), SPIE Conference Series, vol. 9599, 2015. doi:10.1117/12.218865
  3. R. Reisenhofer
    The Complex Shearlet Transform and Applications to Image Quality Assessment (PDF)
    Technische Universitšt Berlin, Master's Thesis, 2014.

Haar Wavelet-Based Perceptual Similarity Index (HaarPSI)

The Haar wavelet-based perceptual similarity index (HaarPSI) is a similarity measure for images that aims to correctly assess the perceptual similarity between two images with respect to a human viewer.

In most practical situations, images and videos can neither be compressed nor transmitted without introducing distortions that will eventually be perceived by a human observer. Vice versa, most applications of image and video restoration techniques, such as inpainting or denoising, aim to enhance the quality of experience of human viewers. Correctly predicting the similarity of an image with an undistorted reference image, as subjectively experienced by a human viewer, can thus lead to significant improvements in any transmission, compression, or restoration system.

For more information and downloads please visit www.haarpsi.org. The HaarPSI is also available on github.


A regular simplex is a collection of s+1 equiangular vectors which form a tight frame for their span, which is s-dimensional. The binder of an equiangular tight frame (ETF) consists of all of the subsets of the index set which correspond to regular simplices. Such simplices, if they exist, must have the same size as the spark of the frame and thus are also the smallest circuits in the matroid associated to the frame. Directly computing the binder is very computationally expensive. A few different mathematical tricks are employed in the code of BinderFinder in order to make computing the binder of some larger ETFs tractable. The code is a short Matlab program (which is also Octave compatible).

Download the code here: BinderFinder.m

For more information, see "Equiangular tight frames that contain regular simplices" by Matthew Fickus, John Jasper, Emily J. King, and Dustin G. Mixon. (PDF)


A Python program to blindly separate the contributions of different instruments in an audio recording via dictionary learning.

The algorithm uses a shift-invariant time-frequency representation that enables it to identify patterns of peaks in the spectrum corresponding to sounds of musical instruments regardless of pitch. Further refinement via non-linear optimization makes it independent of the tuning of the instruments and incorporates inharmonicity. Reasonability of the representation is ensured by a sparsity condition.

For the training of the dictionary, we use a modified version of ADAM, which is a state-of-the-art stochastic gradient descent algorithm.

These are the separation results for the 8th piece from the 12 Basset Horn Duos by Wolfgang A. Mozart (KV 487) in an arrangement by Alberto Gomez Gomez (license: CC BY-NC-SA 3.0) for two recorders, played on acoustic soprano recorder and violin:

A preprint describing the algorithm is available on arXiv, and the code is on GitHub (also refer to the API documentation). In order to reproduce the results, it must be run on the test data (ZIP).