Implementation of the ‘engine’ of Multi-touch table

The goal in the project is to repair and improve the processing of an image displayed upon a touch screen, in order to gain the ability of shape recognition. The aspect of image processing dealt with in the project, is the combing and stitching of three images derived from three different cameras into one single image that describes the surface of the touch screen.

The touch screen is based on an existing system. It consists of a projective surface and capturing cameras that were built in a previous project, and from an open source code - CCV – Computer Core Vision. Other system that based on the CCV code does not usually use multiple cameras or does not require precise image recognition, and therefore there was no need for a better image stitching than the existing one.

In this document two different methods will be shown, which were used in order to get an image that reflects in a good way the objects and events occurring on the system surface. We will present the main idea behind each method, the implementation and the guidelines that were used in order to choose the suitable one, in light of the results received from the different implementations.

Implementation of the ‘engine' of Multi-touch table

 

Image Compression Through Multi-Scale Learned Dictionaries
Image Compression Through Multi-Scale Learned Dictionaries