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Theme
'Objects are not exhausted'
object-oriented ontology (OOO)
Objects solely just exist, not particularily for us to use. It just exists.
Context
Reflecting on this enquiry of object just exists, I was breaking every layer on what an object does behind their visual appearance, peeling every skin like an onion layer, till the deepest of part till of atoms? but beyond that it exists as well, it's black, its nothing. Not exactly nothing but just space. Also reflecting from own experiences.
Method also inspired by this video by covet: https://www.youtube.com/watch?v=RXGwVJCdV6A
and and : A simile from the Pali scriptures (SN 22.95) compares form and feelings with foam and bubbles. < https://en.wikipedia.org/wiki/Śūnyatā?fbclid=IwAR07y7ZMiUewgPkdGl1uarpxhmkTCKm6smRphfivZ7hOuDHgyEh9DS6kznQ >
Method
I'll try to change/remove visual feedback from external world to that of space. I initially wanted to just put starspace representing that space of nothingness and just being, in place of different objects. In a lazy response to rotoing the video's, I was looking at semantic segmentation , ie- a trained neural net ai to detect and categorise objects. Ideally i thought of this experience as an app, or a projection in later versions where it just converts any human body visual to starspace.After some research on the same, I realised it's a bit too complicated to be done during a CPS module. Time management was important here as the idea quickly kept building in my head which reached a good massive project scale.
Response
A portray of objects just being. Starting with human's and trees, while i've focused on these in contrast to harder object's like buildings etc. - Rocks can be viewed in a similar state in the very atom of their being. All just pure stardust , chilling in a space of being.
The photo was taken from the previous guestlecture hall.
ftureRefOryours:
https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef
https://arxiv.org/pdf/1411.4038.pdf
https://www.mathworks.com/videos/generate-cuda-code-for-a-semantic-segmentation-algorithm-1522147796162.html
https://heartbeat.fritz.ai/building-an-image-segmentation-app-in-ios-3377eb4a3e7c
https://au.mathworks.com/help/vision/examples/semantic-segmentation-using-deep-learning.html
https://arxiv.org/pdf/1804.04603.pdf
https://medium.com/tensorflow/introducing-bodypix-real-time-person-segmentation-in-the-browser-with-tensorflow-js-f1948126c2a0