A huge number of high quality photographs are being taken in the smartphone era. For many companies there is value locked within these images but the vast number of such images makes them tedious to sort through. Identifying features and objects in these images automatically allows companies to gather information in new ways and while expending fewer resources. This can be used both with publicly available images and with user supplied images, allowing companies to get trained eye on something without actually paying a trained employee to look at it.
One of our clients who need help in this area was a construction company who wanted to use amateur photos of houses to learn information about the layout and dimension of houses without having to send crews to the site.
SFL-Scientific is able to combine advanced machine learning algorithms to suit our clients’ needs. Machine vision is a cutting edge field and there are many different algorithms each with strengths and weaknesses in different tasks. In this case we combined the types of algorithms used to create panorama images with convolutional neural nets for object recognition. This allowed us to create an accurate 3d model of a house using only photos of the outside.
The ability to have a computer instead of a person extract relevant information from an image can save companies a great deal of money, especially if the necessary identification requires a great deal of domain knowledge. Trained humans are expensive but once you train them computers are cheap. In this case our algorithm allowed the construction company to forgo sending employees out to properties to collect basic information.