Skip to main content
Article thumbnail
Location of Repository

Part-based Probabilistic Point Matching using Equivalence Constraints

By Graham McNeill and Sethu Vijayakumar


Correspondence algorithms typically struggle with shapes that display part-based\ud variation. We present a probabilistic approach that matches shapes using independent\ud part transformations, where the parts themselves are learnt during matching.\ud Ideas from semi-supervised learning are used to bias the algorithm towards finding\ud ‘perceptually valid’ part structures. Shapes are represented by unlabeled point\ud sets of arbitrary size and a background component is used to handle occlusion,\ud local dissimilarity and clutter. Thus, unlike many shape matching techniques, our\ud approach can be applied to shapes extracted from real images. Model parameters\ud are estimated using an EM algorithm that alternates between finding a soft\ud correspondence and computing the optimal part transformations using Procrustes\ud analysis

Year: 2010
OAI identifier:

Suggested articles


  1. (2003). A new point matching algorithm for non-rigid registration. doi
  2. (2006). A probabilistic approach to robust shape matching. doi
  3. (2003). A unified framework for alignment and correspondence. Computer Vision and Image Understanding, doi
  4. (2004). Computing Gaussian mixture models with EM using equivalence constraints. In NIPS. doi
  5. (1995). Parts of visual form: doi
  6. (2002). Shape matching and object recognition using shape contexts. doi
  7. (2004). Shape matching and recognition using generative models and informative features. doi
  8. (2005). Unsupervised Learning of Multiple Objects in Images. doi
  9. (2005). Using the inner-distance for classification of ariculated shapes. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.