NDLI: Planar object recognition using local descriptor based on histogram of intensity patches (2013)

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Planar object recognition using local descriptor based on histogram of intensity patches (2013)

Content Provider	CiteSeerX
Author	Jakab, Marek
Description	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.
File Format	PDF
Language	English
Publisher Date	2013-01-01
Publisher Institution	In Proceedings of the 17th Central European Seminar on Computer Graphics
Access Restriction	Open
Subject Keyword	Reference Picture Augmented Reality Image Recognition Simon Taylor Computation Time Opencv Library Key Task Fast Key-point Detector Local Descriptor Occurring Key-points Planar Object Recognition Available De-scriptors Rotation Invariant Intensity Patch Inten-sity Patch Account Different Scale Memory Consuming Perspective Transformation Supplementary Information Mobile Platform Mobile Device Fast Binary Descriptor User Child
Content Type	Text
Resource Type	Article

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