Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unified Context-Aware Augmented Reality Application Framework for User-Driven Tour Guides
ISUVR '10 Proceedings of the 2010 International Symposium on Ubiquitous Virtual Reality
Mobile museum guide based on fast SIFT recognition
AMR'08 Proceedings of the 6th international conference on Adaptive Multimedia Retrieval: identifying, Summarizing, and Recommending Image and Music
Concept and applications of In-Situ AR mashup content
Proceedings of 1st international symposium on From digital footprints to social and community intelligence
IMAF: in situ indoor modeling and annotation framework on mobile phones
Personal and Ubiquitous Computing
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In the case of illumination and view direction changes, the ability to accurately detect the Regions of Interest (ROI) is important for robust recognition. In this paper, we propose a stroke-based semi-automatic ROI detection algorithm using adaptive thresholding and a Hough-transform method for in-situ painting recognition. The proposed algorithm handles both simple and complicated texture painting cases by adaptively finding the threshold. It provides dominant edges by using the determined threshold, thereby enabling the Hough-transform method to succeed. Next, the proposed algorithm is easy to learn, as it only requires minimal participation from the user to draw a diagonal line from one end of the ROI to the other. Even though it requires a stroke to specify two vertex searching regions, it detects unspecified vertices by estimating probable vertex positions calculated by selecting appropriate lines comprising the predetected vertices. In this way, it accurately (1.16 error pixels) detects the painting region, even though a user sees the painting from the flank and gives inaccurate (4.53 error pixels) input points. Finally, the proposed algorithm provides for a fast processing time on mobile devices by adopting the Local Binary Pattern (LBP) method and normalizing the size of the detected ROI; the ROI image becomes smaller in terms of general code format for recognition, while preserving a high recognition accuracy (99.51%). As such, it is expected that this work can be used for a mobile gallery viewing system.