I’ve been trying to accurately detect a very simple object like a watch first before moving on to our positive samples. Today I wrote scripts to download large sets of watch samples from ImageNet an image database. If the success rate increases for detecting watches within this new environment. I will apply the same techniques to the images I cropped of the narrow helix.
Wednesday, April 19, 2017
In my previous post I mentioned that we had a 32% accuracy rate of identifying a narrow helix. To provide an alternative environment to train our cascade classifier I set up a 2gb ubuntu server provided from digital ocean to provide an alternative environment to train our cascade classifier. Using a computer with higher specs may help the performance when training, so I dowloaded the required libraries and python bindings for OpenCV on the server. In addition in my last post I also discussed how we wanted to also change our technique so I manually cropped all of our positive sample to include solely the narrow helix instead of the entire ear.