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 | 1 | initial version |
Hi thanks for your reply.
I will check the window sizes again to make sure I'm using the same window size to both train and detect.
SVMlight outputs a "model" with the various support vectors, a bunch of different threshold parameters and a "threshold" value. I always assumed this is what was expected in the "hit_threshold" parameter of HOGDescriptor::detectMultiScale. If you don't mind me asking, how should one look to finding the correct threshold value? Should this be optimised through trial and error, or using a cost function?
| | 2 | No.2 Revision |
Hi thanks for your reply.reply.
I will check the window sizes again to make sure I'm using the same window size to both train and detect.detect.
SVMlight outputs a "model" with the various support vectors, a bunch of different threshold kernel parameters and a "threshold" value. I always assumed this is what was expected in the "hit_threshold" parameter of HOGDescriptor::detectMultiScale.
If you don't mind me asking, how should one look to finding the correct threshold value? Should this be optimised through trial and error, or using a cost function?
