DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition
Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection.
KeywordsProjector-camera systems Feature descriptors Object recognition
This project has been partially funded by MINECO (TIN2014-61068-R).
- 6.Drouin, M.-A., Jodoin, P.-M., Premont, J.: Camera-projector matching using an unstructured video stream. In: 2010 IEEE Computer Society Conference on CVPR Workshops (CVPRW), vol. 33, p. 40 (2010)Google Scholar
- 7.Fujii, K., Grossberg, M.D., Nayar, S.K.: A projector-camera system with real-time photometric adaptation for dynamic environments. In: 2005 IEEE Computer Society Conference on (CVPR 2005), San Diego, CA, USA, 20–26 June 2005, pp. 814–821 (2005)Google Scholar
- 9.Kumar, V., Namboodiri, A.M., Jawahar, C.V.: Face recognition in videos by label propagation. In: 22nd ICPR 2014, Stockholm, Sweden, 24–28 August 2014, pp. 303–308 (2014)Google Scholar
- 11.Ng, T.-T., Pahwa, R.S., Bai, J., Quek, T.Q.S., Tan, K.-H.: Radiometric compensation using stratified inverses. In: IEEE 12th ICCV 2009, Kyoto, Japan, 27 September – 4 October 2009, pp. 1889–1894 (2009)Google Scholar
- 13.Ortiz, E.G., Wright, A., Shah, M.: Face recognition in movie trailers via mean sequence sparse representation-based classification. In: 2013 IEEE Conference on CVPR, Portland, OR, USA, 23–28 June 2013, pp. 3531–3538 (2013)Google Scholar
- 14.Park, H., Lee, M.-H., Kim, S.-J., Park, J.-I.: Contrast enhancement in direct-projected augmented reality. In: Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, ICME 2006, Toronto, Ontario, Canada, 9–12 July 2006, pp. 1313–1316 (2006)Google Scholar
- 16.Setkov, A., Gouiffès, M., Jacquemin, C.: Color invariant feature matching for image geometric correction. In: 6th International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, MIRAGE 2013, Berlin, Germany, 06–07 June 2013, pp. 7:1–7:8 (2013)Google Scholar
- 18.Yamanaka, T., Sakaue, F., Sato, J.: Adaptive image projection onto non-planar screen using projector-camera systems. In: 20th ICPR 2010, Istanbul, Turkey, 23–26 August 2010, pp. 307–310 (2010)Google Scholar
- 19.Zollmann, S., Langlotz, T., Bimber, O.: Passive-active geometric calibration for view-dependent projections onto arbitrary surfaces. JVRB J. Virtual Reality Broadcast. 4(6), 10 (2007)Google Scholar