Press escape to exit fullscreen

{{sketch.instructions}}

CC {{sketch.licenseObject.short}}

Archived Sketch

This sketch is created with an older version of Processing,
and doesn't work on browsers anymore.

View Source Code

Capture Screenshot

PCA - Principal Component Analysis

{{$t('general.by')}}
Thomas
Diewald
PCA - Principal Component Analysis the eigenvectors (sorted by their eigenvalue) of the covarianz-matrix, which is built of the data-vectors(R,G,B), make the tranformation matrix. in the sketch, the thick gizmo shows the "PCA-Matrix" '1' ... source image '2' ... destination image '3' ... original RGB values '4' ... transformed RGB values (eigenvector matrix) '5' ... back-transformed RGB values (eigenvector matrix) 'q' | 'w' ... change scale of 1st component 'a' | 's' ... change scale of 2nd component 'y' | 'x' ... change scale of 3rd component
We recovered an unsaved version of this sketch. Please review your changes below.

As a Plus+ Member feature, this source code is hidden by the owner.

  • {{co.title}}
    •
    {{$t('sketch.mode-pjs')}} {{$t('general.learnMore')}}
    Select mode or a template
    {{liveCodeLink}}
    • {{l.url.substr(l.url.lastIndexOf('/') + 1)}}
    {{$t('sketch.layoutDescription')}}

    {{k.category}}
    {{k.label}}
    {{k.description}}

    {{$t('sketch.seeMoreShortcuts')}}

    Versions are only kept for 7 days.
    Join Plus+ to keep versions indefinitely!

    {{$t('general.joinPlus')}}

    {{$t('sketch.versionsSummarized')}}

    {{ $t('general.joinPlus') }}