GSCT UVR LAB.
UVR Lab. was formed in Feb. 2001 at GIST to study and develop “Virtual Reality in Smart computing environments” that process multimodal input, perceive user’s intention and emotion, and respond to user’s request through Augmented Reality. Since 2012, UVR Lab moved to KAIST GSCT and restarted with a theme of “FUN in Ubiquitous VR.”
 
작성일 : 12-04-09
Stroke-based ROI Detection Algorithm for In-situ Painting Recognition
 글쓴이 : UVR
조회 : 5,301  


Uploaded by GIST CTI on Aug. 4, 2011
 
This work was presented through the conference HCI International 2011.

Paper Info.: Youngkyoon Jang, Woontack Woo, "A Stroke-based Semi-automatic ROI Detection Algorithm for In-Situ Painting Recognition", HCII2011 (Virtual and Mixed Reality, Part II), LNCS 6774, pp. 167-176, Orlando, Florida, USA, July 9-14, 2011

Abstract: 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.