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Research Article | Open Access

TransHist: Occlusion-robust shape detection in cluttered images

The Chinese University of Hong Kong, Hong Kong, China.
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Abstract

Shape matching plays an important role in various computer vision and graphics applications such as shape retrieval, object detection, image editing, image retrieval, etc. However, detecting shapes in cluttered images is still quite challenging due to the incomplete edges and changing perspective. In this paper, we propose a novel approach that can efficiently identify a queried shape in a cluttered image. The core idea is to acquire the transformation from the queried shape to the cluttered image by summarising all point-to-point transformations between the queried shape and the image. To do so, we adopt a point-based shape descriptor, the pyramid of arc-length descriptor (PAD), to identify point pairs between the queried shape and the image having similar local shapes. We further calculate the transformations between the identified point pairs based on PAD. Finally, we summarise all transformations in a 4D transformation histogram and search for the main cluster. Our method can handle both closed shapes and open curves, and is resistant to partial occlusions. Experiments show that our method can robustly detect shapes in images in the presence of partial occlusions, fragile edges, and cluttered backgrounds.

Keywords

shape matchingshape detectiontransformation histogram

References

[1]
Jacobs, C. E.; Finkelstein, A.; Salesin, D. H. Fast multiresolution image querying. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, 277-286, 1995.
Crossref
[2]
Granlund, G. H. Fourier preprocessing for hand print character recognition. IEEE Transactions on Computers Vol. C-21, No. 2, 195-201, 1972.
Crossref Google Scholar
[3]
Persoon, E.; Fu, K. S. Shape discrimination using Fourier descriptors. IEEE Transactions on Systems, Man, and Cybernetics Vol. 7, No. 3, 170-179, 1977.
Crossref Google Scholar
[4]
Zhang, D.; Lu, G. Enhanced generic Fourier descriptors for object-based image retrieval. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IV-3668–IV-3671, 2002.
Crossref
[5]
Mokhtarian, F.; Mackworth, A. K. A theory of multiscale, curvature-based shape representation for planar curves. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 14, No. 8, 789-805, 1992.
Crossref Google Scholar
[6]
Mokhtarian, F.; Abbasi, S.; Kittler, J. Efficient and robust retrieval by shape content through curvature scale space. Image Databases and Multi-Media Search Vol. 8, 51-58, 1998.
Crossref Google Scholar
[7]
Alajlan, N.; El Rube, I.; Kamel, M. S.; Freeman, G. Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recognition Vol. 40, No. 7, 1911-1920, 2007.
Crossref Google Scholar
[8]
Manay, S.; Cremers, D.; Hong, B.-W.; Yezzi, A. J.; Soatto, S. Integral invariants for shape matching. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 28, No. 10, 1602-1618, 2006.
Crossref Google Scholar
[9]
Hong, B. W.; Soatto, S. Shape matching using multiscale integral invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 1, 151-160, 2015.
Crossref Google Scholar
[10]
Belongie, S.; Malik, J.; Puzicha, J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 4, 509-522, 2002.
Crossref Google Scholar
[11]
Ling, H.; Jacobs, D. W. Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 29, No. 2, 286-299, 2007.
Crossref Google Scholar
[12]
Kwan, K. C.; Sinn, L. T.; Han, C.; Wong, T.-T.; Fu, C.-W. Pyramid of arclength descriptor for generating collage of shapes. ACM Transactions on Graphics Vol. 35, No. 6, Article No. 229, 2016.
Crossref Google Scholar
[13]
Chuang, G. C. H.; Kuo, C. C. J. Wavelet descriptor of planar curves: Theory and applications. IEEE Transactions on Image Processing Vol. 5, No. 1, 56-70, 1996.
Crossref Google Scholar
[14]
Tabbone, S.; Wendling, L.; Salmon, J.-P. A new shape descriptor defined on the radon transform. Computer Vision and Image Understanding Vol. 102, No. 1, 42-51, 2006.
Crossref Google Scholar
[15]
Lee, S.-M.; Abbott, A. L.; Clark, N. A.; Araman, P. A. A shape representation for planar curves by shape signature harmonic embedding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1940-1947, 2006.
[16]
Hu, M.-K. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory Vol. 8, No. 2, 179-187, 1962.
Crossref Google Scholar
[17]
Khotanzad, A.; Hong, Y. H. Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 12, No. 5, 489-497, 1990.
Crossref Google Scholar
[18]
Belkasim, S. O.; Shridhar, M.; Ahmadi, M. Pattern recognition with moment invariants: A comparative study and new results. Pattern Recognition Vol. 24, No. 12, 1117-1138, 1991.
Crossref Google Scholar
[19]
Sheng, Y.; Shen, L. Orthogonal Fourier – Mellin moments for invariant pattern recognition. Journal of the Optical Society of America A Vol. 11, No. 6, 1748-1757, 1994.
Crossref Google Scholar
[20]
Bernier, T.; Landry, J.-A. A new method for representing and matching shapes of natural objects. Pattern Recognition Vol. 36, No. 8, 1711-1723, 2003.
Crossref Google Scholar
[21]
Mori, G.; Belongie, S.; Malik, J. Shape contexts enable efficient retrieval of similar shapes. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, I-723–I-730, 2001.
[22]
Mori, G.; Belongie, S.; Malik, J. Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 27, No. 11, 1832-1837, 2005.
Crossref Google Scholar
[23]
Mori, G.; Malik, J. Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, I-134–I-141, 2003.
[24]
Tanase, M.; Veltkamp, R. C. Part-based shape retrieval. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, 543-546, 2005
Crossref
[25]
Tănase, M.; Veltkamp, R. C.; Haverkort, H. Multiple polyline to polygon matching. In: Algorithms and Computation. Lecture Notes in Computer Science, Vol. 3827. Deng, X.; Du, D. Z. Eds. Springer, Berlin, Heidelberg, 60-70, 2005.
Crossref
[26]
Pickup, D.; Sun, X.; Rosin, P. L.; Martin, R. R. Skeleton-based canonical forms for non-rigid 3D shape retrieval. Computational Visual Media Vol. 2, No. 3, 231-243, 2016.
Crossref Google Scholar
[27]
Xu, K.; Chen, K.; Fu, H.; Sun, W.-L.; Hu, S.-M. Sketch2Scene: Sketch-based co-retrieval and co-placement of 3D models. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 123, 2013.
Crossref Google Scholar
[28]
Lian, W.; Zhang, L.; Zhang, D. Rotation-invariant nonrigid point set matching in cluttered scenes. IEEE Transactions on Image Processing Vol. 21, No. 5, 2786-2797, 2012.
Crossref Google Scholar
[29]
Thayananthan, A.; Stenger, B.; Torr, P. H. S.; Cipolla, R. Shape context and chamfer matching in cluttered scenes. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, I-127–I-133, 2003.
[30]
Riemenschneider, H.; Donoser, M.; Bischof, H. Using partial edge contour matches for efficient object category localization. In: Computer Vision – ECCV 2010. Lecture Notes in Computer Science, Vol. 6315. Daniilidis, K.; Maragos, P.; Paragios, N. Eds. Springer, Berlin, Heidelberg, 29-42, 2010.
Crossref
[31]
Bai, X.; Li, Q.; Latecki, L. J.; Liu, W.; Tu, Z. Shape band: A deformable object detection approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1335-1342, 2009.
Crossref
[32]
Cheng, M.-M.; Zhang, F.-L.; Mitra, N. J.; Huang, X.; Hu, S.-M. RepFinder: Finding approximately repeated scene elements for image editing. ACM Transactions on Graphics Vol. 29, No. 4, Article No. 83, 2010.
Crossref Google Scholar
[33]
Toshev, A.; Taskar, B.; Daniilidis, K. Shape-based object detection via boundary structure segmentation. International Journal of Computer Vision Vol. 99, No. 2, 123-146, 2012.
Crossref Google Scholar
[34]
Chi, Y.; Leung, M. K. H. Part-based object retrieval in cluttered environment. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 29, No. 5, 890-895, 2007.
Crossref Google Scholar
[35]
Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39, No. 6, 1137-1149, 2017.
Crossref Google Scholar
[36]
Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779-788, 2016.
Crossref
[37]
Gidaris, S.; Komodakis, N. Object detection via a multi-region and semantic segmentation-aware CNN model. In: Proceedings of the IEEE International Conference on Computer Vision, 1134-1142, 2015.
Crossref
[38]
Canny, J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. PAMI-8, No. 6, 679-698, 1986.
Crossref Google Scholar
[39]
Cui, M.; Femiani, J.; Hu, J.; Wonka, P.; Razdan, A. Curve matching for open 2D curves. Pattern Recognition Letters Vol. 30, No. 1, 1-10, 2009.
Crossref Google Scholar
[40]
Ferrari, V.; Fevrier, L.; Jurie, F.; Schmid, C. Groups of adjacent contour segments for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 30, No. 1, 36-51, 2008.
Crossref Google Scholar
[41]
Jeannin, S.; Bober, M. Description of core experiments for MPEG-7 motion/shape. MPEG-7, ISO/IEC/JTC1/SC29/WG11/MPEG99 N, 2690, 1999.
Computational Visual Media
Volume 4 Issue 2,
June 2018
Pages 161-172
DOI: 10.1007/s41095-018-0104-1
Cite this article:
Han C, Liu X, Sinn LT, et al. TransHist: Occlusion-robust shape detection in cluttered images. Computational Visual Media, 2018, 4(2): 161-172. https://doi.org/10.1007/s41095-018-0104-1

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Revised: 29 December 2017
Accepted: 31 December 2017
Published: 12 March 2018
© The Author(s) 2018

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