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Object detection by global contour shape. (English) Zbl 1162.68629
Summary: We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.
MSC:
68T10 Pattern recognition, speech recognition
Software:
Tabu search
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[1] M.C. Burl, M. Weber, P. Perona, A probabilistic approach to object recognition using local photometry and global geometry, in: Proceedings of the 5th European Conference on Computer Vision, Freiburg, Germany, 1998.
[2] E. Sali, S. Ullman, Combining class-specific fragments for object classification, in: Proceedings of the 10th British Machine Vision Conference, Nottingham, UK, 1999.
[3] D. Lowe, Object recognition from local scale-invariant features, in: Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, 1999.
[4] J. Sivic, A. Zisserman, Video Google: a text retrieval approach to object matching in videos, in: Proceedings of the 9th International Conference on Computer Vision, Nice, France, 2003.
[5] Belongie, S.; Malik, J.; Puzicha, J., Shape matching and object recognition using shape contexts, IEEE trans. pattern anal. Mach. intell., 24, 4, 509-522, (2002)
[6] H.G. Barrow, J.M. Tenenbaum, R.C. Boles, H.C. Wolf, Parametric correspondence and chamfer matching: two new techniques for image matching, in: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, 1977.
[7] Borgefors, G., Hierarchical chamfer matching: a parametric edge matching algorithm, IEEE trans. pattern anal. Mach. intell., 10, 6, 849-865, (1988)
[8] D. Gavrila, V. Philomin, Real-time object detection for smart vehicles, in: Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, 1999.
[9] Olson, C.; Huttenlocher, D., Automatic target recognition by matching oriented edge pixels, IEEE trans. image process., 6, 1, 103-113, (1997)
[10] B. Leibe, E. Seemann, B. Schiele, Pedestrian detection in crowded scenes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
[11] Del Bimbo, A.; Pala, P., Visual image retrieval by elastic matching of user sketches, IEEE trans. pattern anal. Mach. intell., 19, 2, 121-132, (1997)
[12] Cremers, D.; Schörr, C.; Weickert, J., Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework, ()
[13] Cootes, T.; Taylor, C.J.; Cooper, D.H.; Graham, J., Active shape models—their training and application, Comput. vision image understanding, 61, 1, (1995)
[14] Coughlan, J.; Yuille, A.; English, C.; Snow, D., Efficient deformable template detection and localisation without user initialisation, Comput. vision image understanding, 78, 3, 303-319, (2000)
[15] Felzenszwalb, P.F., Representation and detection of deformable shapes, IEEE trans. pattern anal. Mach. intell., 27, 2, 208-220, (2005)
[16] A.C. Berg, T.L. Berg, J. Malik. Shape matching and object recognition using low distortion correspondence, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
[17] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
[18] A. Opelt, A. Pinz, A. Zisserman, A boundary-fragment-model for object detection, in: Proceedings of the 9th European Conference on Computer Vision, Graz, Austria, 2006.
[19] J. Shotton, A. Blake, R. Cipolla, Contour-based learning for object detection, in: Proceedings of the 10th International Conference on Computer Vision, Beijing, China, 2005.
[20] V. Ferrari, T. Tuytelaars, L. Van Gool, Object detection by contour segment networks, in: Proceedings of the 9th European Conference on Computer Vision, Graz, Austria, 2006. · Zbl 1098.68761
[21] Martin, D.; Fowlkes, C.; Malik, J., Learning to detect natural image boundaries using local brightness, colour and texture cues, IEEE trans. pattern anal. Mach. intell., 26, 5, 530-549, (2004)
[22] Borenstein, E.; Sharon, E.; Ullman, S., Combining top-down and bottom-up segmentation, () · Zbl 1039.68601
[23] D. Hoiem, A.A. Efros, M. Hebert, Geometric context from a single image, in: Proceedings of the 10th International Conference on Computer Vision, Beijing, China, 2005. · Zbl 1235.68268
[24] B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman, A. Zisserman, Using multiple segmentations to discover objects and their extent in image collections, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, 2006.
[25] J. Wang, E. Gu, M. Betke, MosaicShape: stochastic region grouping with shape prior, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
[26] J. Shi, J. Malik, Motion segmentation and tracking using normalised cuts, in: Proceedings of the 6th International Conference on Computer Vision, Bombay, India, 1998.
[27] Nock, R.; Nielsen, F., Statistical region merging, IEEE trans. pattern anal. Mach. intell., 26, 11, 1452-1458, (2004)
[28] Arkin, E.M.; Chew, L.P.; Huttenlocher, D.P.; Kedem, K.; Mitchell, J.S.B., An efficiently computable metric for comparing polygonal shapes, IEEE trans. pattern anal. Mach. intell., 13, 3, 209-216, (1991)
[29] Basri, R.; Costa, L.; Geiger, D.; Jacobs, D., Determining the similarity of deformable shapes, Vision res., 38, 2365-2385, (1998)
[30] Sebastian, T.B.; Klein, P.N.; Kimia, B.B., On aligning curves, IEEE trans. pattern anal. Mach. intell., 25, 1, 116-124, (2003)
[31] Jalba, A.C.; Wilkinson, H.F.; Roerdink, J.B.T.M., Shape representation and recognition through morphological curvature scale spaces, IEEE trans. image process., 15, 2, 331-341, (2006)
[32] Cortelazzo, G.; Mian, G.A.; Vezzi, G.; Zamperoni, P., Trademark shapes description by string-matching techniques, Pattern recognition, 27, 8, 1005-1018, (1994)
[33] R.C. Veltkamp, Shape matching: similarity measures and algorithms, Technical Report UU-CS-2001-03, Institute of Information and Computing Sciences, Utrecht University, 2001.
[34] Fagin, R.; Stockmeyer, L., Relaxing the triangle inequality in pattern matching, Int. J. comput. vision, 30, 3, 219-231, (1998)
[35] Manay, S.; Cremers, D.; Hong, B.-W.; Yezzi, A.J.; Soatto, S., Integral invariants for shape matching, IEEE trans. pattern anal. Mach. intell., 28, 10, 1602-1618, (2006)
[36] Latecki, L.J.; Lakämper, R., Shape similarity measure based on correspondence of visual parts, IEEE trans. pattern anal. Mach. intell., 22, 10, 1185-1190, (2000)
[37] Niblack, W.; Barber, W.; Equitz, M.; Flickner, M.; Glasman, E.; Petkovic, D.; Yanker, P., The QBIC project: querying images by content using colour, texture and shape, ()
[38] Ohta, Y.; Kanade, T., Stereo by intra- and inter-scanline search, IEEE trans. pattern anal. Mach. intell., 7, 2, 139-154, (1985)
[39] Sakoe, H.; Chiba, S., Dynamic programming algorithm optimisation for spoken word recognition, IEEE trans. acoust. speech signal process., 26, 1, 43-49, (1978) · Zbl 0371.68035
[40] Glover, F.; Laguna, M., Tabu search, () · Zbl 0930.90083
[41] Fei-Fei, L.; Fergus, R.; Perona, P., Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories, ()
[42] Mardia, K.V., Statistics of directional data, (1972), Academic Press New York · Zbl 0244.62005
[43] A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, B. Schiele, L. Van Gool, Towards multi-view object class detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, 2006.
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