Re: [scikit-image] Determine different grass percentage
On Nov 13, 2017, 14:57 -0700, Stefan van der Walt <stefanv@berkeley.edu>, wrote:
On Sun, Nov 12, 2017, at 23:18, Dzung Nguyen wrote:
How about labeling manually a dataset for training, then let machine learning do the rest (of course local binary pattern could be used as feature)?
You will definitely need some ground truth data; both for training, and for evaluating any system you come up with.
Alternatively, if you are sure you/the human visual cortex can correctly identify it, then you could create your training data set (i.e. the labels) using a Citizen Science project. zooniverse.org has a relatively easy process for setting up a new project. Michael
Stéfan
Is this similar to Mechanical Turk (crowdsourcing), but for scientific purpose? On Mon, Nov 13, 2017 at 4:06 PM, K.-Michael Aye <kmichael.aye@gmail.com> wrote:
On Nov 13, 2017, 14:57 -0700, Stefan van der Walt <stefanv@berkeley.edu>, wrote:
On Sun, Nov 12, 2017, at 23:18, Dzung Nguyen wrote:
How about labeling manually a dataset for training, then let machine learning do the rest (of course local binary pattern could be used as feature)?
You will definitely need some ground truth data; both for training, and for evaluating any system you come up with.
Alternatively, if you are sure you/the human visual cortex can correctly identify it, then you could create your training data set (i.e. the labels) using a Citizen Science project. zooniverse.org has a relatively easy process for setting up a new project.
Michael
Stéfan
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-- *Dzung Nguyen* PhD Student Electrical Engineering and Computer Science, Northwestern University, IL, USA http://users.eecs.northwestern.edu/~dtn419/
participants (2)
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Dzung Nguyen -
K.-Michael Aye