Browse > Article
http://dx.doi.org/10.7472/jksii.2017.18.1.43

Inside Wall Frame Detection Method Based on Single Image  

Jeong, Do-Wook (School of Media, Soongsil Univ.)
Jung, Sung-Gi (School of Media, Soongsil Univ.)
Choi, Hyung-Il (School of Media, Soongsil Univ.)
Publication Information
Journal of Internet Computing and Services / v.18, no.1, 2017 , pp. 43-50 More about this Journal
Abstract
In this paper, we are proposing improved vanishing points detection and segments labeling methods for inside wall frame detection from indoor image of a piece of having a colour RGB. A lot of research related to recognizing the frame of artificial structures from the image is being performed due to increase in demand for AR technology. But detect the inside wall frame in indoor images have many objects that caused the occlusion is still a difficult issue. Inner wall frame detection methods are usually segment labeling methods and detect vanishing point methods are used together. In order to improve the vanishing point detection method we proposed using inner wall orthogonality which forms the cube. Also we proposed labeling method using tree based learning and superpixel based segmentation method for labelingthe segments in indoor images. Finally, in experiments have shown improved results about inside wall frame detection according to our methods.
Keywords
inside wall frame detection; vanishing points detection; tree based learning; superpixel segmentation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chen, Y., Pan, D., Pan, Y., Liu, S., Gu, A., & Wang, M., "Indoor scene understanding via monocular RGB-D images." Information Sciences, 320, pp. 361-371 , Nov. 2015.   DOI
2 D. Hoiem, A. A. Efros, M. Hebert. "Recovering surface layout from an image." International Journal of Computer Vision, 75.1: 151-172, 2007.   DOI
3 Hedau, Varsha, Derek Hoiem, David Forsyth. "Recovering the spatial layout of cluttered rooms." 2009 IEEE 12th international conference on computer vision. IEEE 2009, pp. 1849-1856, 2009.
4 D. C. Lee, M. Hebert, T. Kanade. "Geometric reasoning for single image structure recovery." Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2009. pp. 2136-2143, 2009.
5 A. Gupta, M. Hebert, T. Kanade, D. M. Blei. "Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces." In NIPS, pp. 1288-1296, 2010.
6 L. Del Pero, J. Bowdish, D. Fried, B. Kermgard, E. Hartley, K. Barnard. "Bayesian geometric modeling of indoor scenes." In CVPR, 2012, pp. 2719-2726, 2012.
7 Canny, John. "A computational approach to edge detection." IEEE Transactions on pattern analysis and machine intelligence, 6, pp. 679-698, 1986.
8 Kosecka, Jana, Wei Zhang. "Video compass." European conference on computer vision. Springer Berlin Heidelberg, 2002.
9 Felzenszwalb, Pedro F., and Daniel P. Huttenlocher. "Efficient graph-based image segmentation." International Journal of Computer Vision 59.2: 167-181, 2004.   DOI
10 Shi, Jianbo, and Jitendra Malik. "Normalized cuts and image segmentation." IEEE Transactions on pattern analysis and machine intelligence 22.8: 888-905, 2000.   DOI
11 Achanta, Radhakrishna, et al. Slic superpixels. No. EPFL-REPORT-149300. 2010.
12 A. G. Schwing, R. Urtasun. "Efficient exact inference for 3d indoor scene understanding." In ECCV, pp. 299-313, 2012.
13 Y. Zhao and S.-C. Zhu. "Scene parsing by integrating function, geometry and appearance models." In CVPR. IEEE, 2013, pp. 3119-3126, 2013.