Abstract
Perceptual grouping plays a critical role in both human and computer vision. However, with the object categorization community’s preoccupation with object detection, interest in perceptual grouping has waned. The reason for this is clear: the object-independent, mid-level shape priors that form the basis of perceptual grouping are subsumed by the object-dependent, high-level shape priors defined by a target object. As the recognition community moves from object detection back to object recognition, a linear search through a large database of target models is intractable, and perceptual grouping will be essential for sublinear scaling. We review three approaches to perceptual grouping based on grouping superpixels. In the first, we use symmetry to group superpixels into symmetric parts, and then group the parts to form structured objects. In the second, we use contour closure to group superpixels, yielding a figure-ground segmentation. In the third, we use a vocabulary of simple parts to both group superpixels into parts and recover the abstract shapes of the parts.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Binford TO (1971) Visual perception by computer. In: Proceedings, IEEE conference on systems and control, Miami, FL
Blum H (1967) A transformation for extracting new descriptors of shape. In: Wathen-Dunn W (ed) Models for the perception of speech and visual form. MIT Press, Cambridge, pp 362–380
Buchin K, Knauer C, Kriegel K, Schulz A, Seidel R (2007) On the number of cycles in planar graphs. In: In proceedings, COCOON, LNCS, vol 4598. Springer, Berlin, pp 97–107
Carreira J, Sminchisescu C (2012) Cpmc: automatic object segmentation using constrained parametric min-cuts. IEEE Trans Pattern Anal Mach Intell 34(7):1312–1328
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models–their training and application. Comput Vis Image Underst 61(1):38–59
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, pp 886–893
Dickinson S, Pentland A, Rosenfeld A (1990) A representation for qualitative 3-d object recognition integrating object-centered and viewer-centered models. In: Leibovic K (ed) Vision: a convergence of disciplines. Springer, New York
Dickinson S, Pentland A, Rosenfeld A (1992) From volumes to views: an approach to 3-d object recognition. CVGIP, Image Underst 55(2):130–154
Dickinson S, Pentland A, Rosenfeld A (1992) 3-d shape recovery using distributed aspect matching. IEEE Trans Pattern Anal Mach Intell 14(2):174–198
Felzenszwalb P, Huttenlocher D (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181
Huttenlocher D, Ullman S (1990) Recognizing solid objects by alignment with an image. Int J Comput Vis 5(2):195–212
Kolmogorov V, Boykov YY, Rother C (2007) Applications of parametric maxflow in computer vision. In: IEEE international conference on computer vision, pp 1–8
Levinshtein A, Dickinson S, Sminchisescu C (2009) Multiscale symmetric part detection and grouping. In: IEEE international conference on computer vision, September 2009
Levinshtein A, Sminchisescu C, Dickinson S (2010) Optimal contour closure by superpixel grouping. In: ECCV, pp 480–493
Levinshtein A, Sminchisescu C, Dickinson S (2012) Optimal image and video closure by superpixel grouping. Int J Comput Vis 100(1):99–119
Levinshtein A, Sminchisescu C, Dickinson SJ (2010) Spatiotemporal closure. In: ACCV, pp 369–382
Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, Siddiqi K (2009) Turbopixels: fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 31(12):2290–2297
Li F, Carreira J, Sminchisescu C (2010) Object recognition as ranking holistic figure-ground hypotheses. In: CVPR, June 2010
Lindeberg T, Bretzner L (2003) Real-time scale selection in hybrid multi-scale representations. In: Scale-space. LNCS, vol 2695. Springer, Berlin, pp 148–163
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Lowe DG (1985) Perceptual organization and visual recognition. Kluwer Academic, Norwell
Pritch Y, Rav-Acha A, Peleg S (2008) Nonchronological video synopsis and indexing. IEEE Trans Pattern Anal Mach Intell 30:1971–1984
Roberts L (1965) Machine perception of three-dimensional solids. In: Tippett J et al. (eds) Optical and electro-optical information processing. MIT Press, Cambridge, pp 159–197
Sala P, Dickinson S (2010) Contour grouping and abstraction using simple part models. In: Proceedings, European conference on computer vision (ECCV), Crete, Greece, September 2010
Sala P, Macrini D, Dickinson S (2010) Spatiotemporal contour grouping using abstract part models. In: Proceedings, Asian conference on computer vision (ACCV), Queenstown, New Zealand, November 2010
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
Siddiqi K, Shokoufandeh A, Dickinson S, Zucker S (1999) Shock graphs and shape matching. Int J Comput Vis 35:13–32
Stahl JS, Wang S (2007) Edge grouping combining boundary and region information. IEEE Trans Image Process 16(10):2590–2606
Street R (1931) A gestalt completion test: a study of a cross section of intellect. Teachers College Press, Columbia University, New York
Tiernan J (1970) An efficient search algorithm to find the elementary circuits of a graph. Commun ACM 13(12):722–726
Acknowledgements
The authors gratefully acknowledge the support of NSERC, Mitacs, and DARPA. Sven Dickinson would like to thank Keith Price for providing the data in Fig. 1.1(b).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Dickinson, S.J., Levinshtein, A., Sala, P., Sminchisescu, C. (2013). The Role of Mid-Level Shape Priors in Perceptual Grouping and Image Abstraction. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-5195-1_1
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5194-4
Online ISBN: 978-1-4471-5195-1
eBook Packages: Computer ScienceComputer Science (R0)