The HotSpotDetection software written in C++ enables to detect fluorescence accumulation over time in video-microscopy. The cumulated detection maps enable to extract more reliably the regions of interest. In practice, this method only requires the setting of the false alarm probability.
We adopt a MRF framework to capture image regularity. In contrast to the usual pixel-wise MRF models, a recent line of work consists in modeling non-local interactions from image patches. The redundancy property and patch-based representation is here exploited to detect unusual spatial patterns seen in the scene. This property holds true in fluorescence imaging and we propose a patch-based Gibbs/MRF modeling to represent the more regular image components. Furthermore, we detect the locations where redundancy is low, that is protein concentrations against a nearly uniform background ideally.
patch size : -m (default = 3) neighborhoud size : -n (default = 5) p-value : -pv (default = 0.2)
In order to run a job you need to be identified or register a new account.
The following curl command will create a job (note: job[webapp] is required, all other parameters are optional):
curl -H 'Authorization: Token token=<your private_token>' \
-X POST https://allgo18.inria.fr/api/v1/jobs \
-F 'job[webapp]=hotspotdetection' \
-F 'job[version]=1.1' \
-F 'job[param]=' \
-F 'job[queue]=standard' \
-F 'files[0]=@test.txt' \
-F 'files[1]=@test2.csv'Monitor its progress:
curl -H 'Authorization: Token token=<your private_token>' \
https://allgo18.inria.fr/api/v1/jobs/JOB_ID/eventsGet the result:
curl -H 'Authorization: Token token=<your private_token>' \
https://allgo18.inria.fr/api/v1/jobs/JOB_IDAbort the job:
curl -H 'Authorization: Token token=<your private_token>' \
-X POST https://allgo18.inria.fr/api/v1/jobs/JOB_ID/abortDelete the job:
curl -H 'Authorization: Token token=<your private_token>' \
-X DELETE https://allgo18.inria.fr/api/v1/jobs/JOB_ID