Object Contour Completion by Combining Object Recognition and Local Edge Cues

Authors

  • Kar Seng Loke School of Information Technology Swinburne University of Technology Sarawak Campus, Malaysia

DOI:

https://doi.org/10.32890/jict2017.16.2.2

Keywords:

Computer vision, object segmentation, object detection, contour extraction, scene interpretation, image understanding

Abstract

We developed a top-down and bottom-up segmentation ofobjects using shape contours through a two-stage procedure. First, the object was identified using an edge-based contour feature and then the object contour was obtained using a constraint optimization procedure based on the results from the earlier identified contours. The initial object detection provides object category specific information for the contour completion to be effected. We argue that top-down bottom-up interaction architecture has plausible neurological correlates. This method has an advantage in that it does not require learning boundaries with large datasets.

 

References

Arandjelovic, R., & Zisserman, A. (2011). Smooth object retrieval using a bag of boundaries. Proceedings of the 2011 International Conference on Computer Vision, 375-382.

Bai, X., Li, Q., Latecki, L.J., Liu W., & Tu, Z. (2009). Shape band: A deformable object detection approach. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2009.5206543

Bar, M., & Kassma, K. S. et al. (2006). Top-down facilitation of visual recognition. Proc. Natl. Acad. Sci USA, 103(2), 449-454. doi: 10.1073/ pnas.0507062103

Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions of Pattern Analysis and Machine Intelligence, 24, 509-522. doi: 10.1109/34.993558.

Ben-Yosef, G., Assif, L., Harari, D., & Ullman, S. (2015). A model for full local image interpretation. In Noelle, D. C., Dale, R., Warlaumont, A. S., Yoshimi, J., Matlock, T., Jennings, C. D., & Maglio, P. P. (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 220-225.

Braje, W. L., Kersten, D. et al. (1998). Illumination effects in face recognition. Psychobiology, 26(4), 371-380. doi:10.3758/BF03330623 Journal of ICT, 16, No. 2 (Dec) 2017, pp: 224–

Canny, J. (1986). A computational approach to edge detection. IEEE Transactions of Pattern Analysis and Machine Intelligence, 8(6), 679698.

Caselles, V., Kimmel, R. et al. (1997). Geodesic active contours. International Journal in Computer Vision, 22(1), 61-79.

Chaji, N., & Ghassemian, H. (2006). Texture-gradient-based contour detection. The European Association for Signal Processing (EURASIP). Journal on Applied Signal Processing 21709, 1-8. doi:10.1155/ASP/2006/21709

Dollar, P., & Zitnick, C.L. (2013). Structured forests for fast edge detection. Proceedings IEEE International Conference on Computer Vision, 1841-1848. doi: 10.1109/ICCV.2013.

Dorigo, M., & Stützle, T. (2004). Ant colony optimization. MIT Press.

Felzenszwalb, P., & McAllester, D. (2006). A min-cover approach for finding salient curves. IEEE Computer Society Workshop on Perceptual Organization in Computer Vision, 185-185. doi: 10.1109/

CVPRW.2006.18

Fenske, M. J., Aminoff, E. et al. (2006). Top-down facilitation of visual object recognition: Object-based and context-based contributions. Progress in Brain Research, 155, 3-21. doi: 10.1016/S0079-6123(06)55001-0

Ferrari, V., Jurie, F. et al. (2007). Accurate object detection with deformable shape models learnt from images. Proceedings IEEE International Conference on Computer Vision and Pattern Recognition. doi: 10.1109/

CVPR.2007.383043

Ferrari, V., Jurie, F. et al. (2010). From images to shape models for object detection. International Journal in Computer Vision, 87, 284. doi:10.1007/s11263-009-0270-9

Grill-Spector, K., & Sayres, R. (2008). Object recognition - Insights from advances in fMRI methods. Current Directions in Psychological Science, 17(2), 73-79. https://doi.org/10.1111/j.1467-8721.2008.00552.x Journal of ICT, 16, No. 2 (Dec) 2017, pp: 224–

Grill-Spector, K., Kushnir, T. et al. (1998). A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Human Brain Mapping, 6, 316-328.

Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., & Malik, J. (2011). Semantic contours from inverse detectors. Proceedings IEEE International Conference on Computer Vision, 991-998. doi:10.1109/

ICCV.2011.6126343

Kim, J. G., Biederman, I. et al. (2009). Adaptation to objects in the lateral occipital complex (LOC): Shape or semantics. Vision Research, 49, 2297-2305.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Loke K.S., & Egerton, S. (2010). Scene understanding: A framework for image segmentation via object recognition. Proceedings of the Sixth International Conference on Intelligent Environments, 328-331. doi: 10.1109/IE.2010.67

Loke, K. (2013). Contour-based shape recognition using perceptual turning points. Proceedings of the International Conference on Computer Vision

Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 487-491. doi: 10.5220/0004304804870491

Marr, D., & E. C. Hildreth (1980). Theory of edge detection. Proceedings of the Royal Society of London. Series B. Biological Sciences, 207(1167), 187-217.

Nguyen A, Yosinski J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR ’15), 427-436. Olga Russakovsky, Jia Deng, Hao Su, Jonathan, K. Sanjeev S. Sean Ma, Zhiheng Huang, Andrej, K. Aditya, K. Michael, B. Alexander, C. B. &

Li Fei-Fei. (2005). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252. Journal of ICT, 16, No. 2 (Dec) 2017, pp: 224–

Opelt, A., Pinz, A., & Zisserman, A. (2006). A boundary fragment model for object detection. In Leonardis, Aleš, Bischof, Horst, Pinz, Axel (Eds.), Computer Vision -- ECCV 2006, 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006, Proceedings, Part II, 575-588. doi: 10.1007/11744047_44

Papari, G., Campisi, P., Petkov, N., & Neri, A. (2007). A biologically motivated multiresolution approach to contour detection. EURASIP Journal on Advances in Signal Processing, 1-28. doi: 10.1155/2007/71828

Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions of Pattern Analysis and Machine Intelligence, 12(7), 629-639.

Peterson, M. A. (1994). Object recognition processes can and do operate before figure-ground organization. Current Directions in Psychological Science, 3, 105-111.

Ren, X., Fowlkes, C., & Malik, J. (2005). Scale-invariant contour completion using conditional random fields. Tenth IEEE International Conference on Computer Vision (ICCV’05), 2, 1214-1221. doi: 10.1109/

ICCV.2005.213

Shashua, A., & Ullman, S. (1988). Structural saliency: The detection of globally salient structures using a locally connected network. Second International Conference on Computer Vision, 321-327. doi: 10.1109/

CCV.1988.590008

Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions of Pattern Analysis and Machine Intelligence, 22(8), 888-905. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow,

I., & Fergus, R. (2013). Intriguing properties of neural networks. Retrieved from: http://arXiv preprint arXiv:1312.6199.

Thayananthan, A., Stenger, B., Torr, P.H.S., & R. Cipolla, R. (2003). Shape context and chamfer matching in cluttered scenes. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, 1, 127-133. doi: 10.1109/CVPR.2003.1211346 Journal of ICT, 16, No. 2 (Dec) 2017, pp: 224–

Vecera, S. P., & Farah, M. J. (1997). Is visual image segmentation a bottomup or an interactive process? Perception & Psychophysics, 59(8), 1280-1296.

Winter, J. D., & Wagemans, J. (2008). Perceptual saliency of points along the contour of everyday objects: A large-scale study. Perception & Psychophysics, 1(70), 50-64.

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Published

06-11-2017

Issue

Vol. 16 No. 2 (2017): Journal of Information and Communication Technology (JICT) Vol.16, No.2, December 2017

Section

Articles

How to Cite

Loke, K. S. (2017). Object Contour Completion by Combining Object Recognition and Local Edge Cues. Journal of Information and Communication Technology, 16(2), 224-242. https://doi.org/10.32890/jict2017.16.2.2

Research impact

Harvested 2026-09-06
2 citations, from OpenAlex — the highest of the sources checked

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Identifiers DOI 10.32890/jict2017.16.2.2 OpenAlex W4245085463