I'm wondering what is the best mechanism to take images from the smartphone camera in order to match theses images with my data-set ,in addition I'm planing to use SURF algorithm for features extraction & descriptor .when i should take the training set how i should take pictures (i.e in height or width manner or both ).also how many pictures in average may i need for each interest building ? I will be very thankful if someone could guide me .
what is the best strategy to capture training images from a smartphone for buildings recognition
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The short answer is "enough". Buildings vary in size, shape, detail and whatnot. You should implement some sort of algorithm which detects and registers features and should feed it new data until you don't detect any new features and only old and already detected features, you may have a "learned" factor in percent, starting from 0% and all the way to 100% when N new images no longer detect any new feature. Then you may tune that to say 70% or whatever, depending on how much time and resources you want to dedicate to creating a feature profile for a given building.
Then for some building it may take 5 images, for another building it may take 500 images. But you will have an algorithm which knows when it is enough.