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Developer's Corner / Re: Dice analyzer machine project
« Last post by mouser on February 17, 2016, 10:15 PM »Just a screenshot showing how well auto clustering (sometimes) works. Given 159 images, it properly identified front face label, and then autonomously grouped them into equivalent clusters with no mistakes:
No guidance was provided by the user other than to say this was a D20 die; there was no pre-training of labels (i.e. this was a pure unsupervised clustering task).
Note that the class numbers listed have no meaning at all.
Notice that it separated the 6s from the 9s, which differ only in the occurrence of a very small dot at the baseline of the 6s. These classes can be hard for the algorithm to separate sometimes depending on how clear the dot is on a given die (for example if the camera isn't in good focus).
The 159 source camera images being processed look like this (after they've been cropped and focused to hide extraneous background):
No guidance was provided by the user other than to say this was a D20 die; there was no pre-training of labels (i.e. this was a pure unsupervised clustering task).
Note that the class numbers listed have no meaning at all.
Notice that it separated the 6s from the 9s, which differ only in the occurrence of a very small dot at the baseline of the 6s. These classes can be hard for the algorithm to separate sometimes depending on how clear the dot is on a given die (for example if the camera isn't in good focus).
The 159 source camera images being processed look like this (after they've been cropped and focused to hide extraneous background):