Development of an Automated Image Processing Framework for Segmenting Floor Plans for Optimal Placement of Internet Access Points

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The objective of this proposal is to develop a software tool consisting of image analysis techniques to automatically convert pixel based digitalized Floor Plans (FPs) into a structured data. Our proposed software will automatically extract the information present into the FPs and represent it in structures like walls, windows, and doors. This information can be used to create a digital model of the building (DMB) represented by the FP. With the grow of the Internet of Things the number of wireless devices tends to increase. A reliable Internet connection can be achieved by correctly placing the wireless access points (APs). The optimal APs distribution can be obtained by computer simulations of the radio waves propagation within a building. To generate the model necessary to the simulation, the FP of the building can be used. Ericsson is currently developing a software to plan the APs distribution within a building, but the FP conversion into a DMB is performing poorly, generating a model with missing features andmisidentifications. In this project, we will determine the feasibility of incorporating state-of-the-art image segmentation methods based on machine learning to address some key technical challenges in converting FPs into DMBs. In conjunction with our industrial partner, Ericsson Canada, we intend to increase the accuracy and speed of this model creation by developing new image segmentation methods. These methods will ensure improved accuracy and speed in conversion of FPs into DMBs that would enable Ericson to be in the edge in this highly competitive field.