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.
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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).
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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
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