Geometrons - Ghost Symbols - Buried Information

Geometron classification problems:

Problem 1: recognizing the elementary shapes called "geometrons (binary)" (143 classes). Problem 1 is a classification issue that can be decomposed in several sub-problems since 3 sub-categories of binary geometrons are provided: mathematical alphabet (76 classes), standard shapes (49 classes) and irregular polygonal shapes (18 classes).

Problem 2: encrypted geometrons, super-visual attention. Same as Problem 1, but the geometrons are now "ghost information in a world of textures". Human eyes can sometimes see the ghosts, but at the cost of a high level of visual attention for the recognition of some features that allow for the identification of the class associated with a ghost geometron, in such a complex environment.

Problem 3: encrypted geometrons, ultra-visual attention. Same as Problem 2, but only "machines" can (?) now see the ghost or buried geometrons.

Geometron decoding problems (Image to Image)

Problem 4: Consider an encrypted geometron in the "super-visual attention" folder and find a model that can decode the content of any pixel. In this problem, the encrypted geometron is considered as an input, and its corresponding interference-free geometron located in the “Geometrons” folder is the desirable output of machine/deep learning model.

Problem 5: Same as Problem 4, but by using the "ultra-visual attention" folder for inputs and "Geometrons" folder for outputs.

Remarks:

1/ if we consider the singles, pairs and triplets of sub-folder fusion possibilities and by mixing super and ultra attention, studying attention per shape, etc., then we have hundreds of AI problems associated with the dataset.

2/ if your favourite machine is unable to learn and you wish to challenge it with better resolved images, ...., you can send a REQUEST to IEEE dataport to identify the possibilities of making high-resolution data available via this platform (several TB, 2048x2048 images). Textures are composites of elliptical multi-fractional anisotropic Brownian fields, defined in paper "Toward Generalized Artificial Intelligence by Assessment Aggregation With Applications to Standard and Extreme Classifications", https://doi.org/10.1109/TNNLS.2023.3297079