Planar Object Recognition Using Local Descriptor Based On Histogram Of Intensity Patches.
Marek Jakab · 2013
The purpose of our research is to develop an application of augmented reality on mobile device that will be educa-tive and entertaining for their users- children. User will be asked for an input to take a picture from the book and the application will draw a supplementary information in the form of a 3D object on the screen. The key task of our application is the problem of image recognition on mobile platform using local descriptors. Currently available de-scriptors included in OpenCV library are well designed, some of them are scale and rotation invariant, but most of them are time and memory consuming and hence not suit-able for mobile platform. Therefore we decided to develop a fast binary descriptor based on the Histogram of Inten-sity PatcheS (HIPs) originally proposed by Simon Taylor et al. To train the descriptor, we need a set of images de-rived from a reference picture taken under varying viewing conditions and geometry and therefore we have to take into account different scales, rotations and perform perspective transformations. Our descriptor is based on a histogram of intensity of the selected pixels around the key-point. We use this descriptor in the combination with the FAST key-point detector, where the most occurring key-points are used with the aim to reduce the computation time.