PDM-ENLOR: Learning ensemble of local PDM-based regressions

Yen H. Le, Uday Kurkure, Ioannis A. Kakadiaris

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Statistical shape models, such as Active Shape Models (ASMs), suffer from their inability to represent a large range of variations of a complex shape and to account for the large errors in detection of model points. We propose a novel method (dubbed PDM-ENLOR) that overcomes these limitations by locating each shape model point individually using an ensemble of local regression models and appearance cues from selected model points. Our method first detects a set of reference points which were selected based on their saliency during training. For each model point, an ensemble of regressors is built. From the locations of the detected reference points, each regressor infers a candidate location for that model point using local geometric constraints, encoded by a point distribution model (PDM). The final location of that point is determined as a weighted linear combination, whose coefficients are learnt from the training data, of candidates proposed from its ensemble's component regressors. We use different subsets of reference points as explanatory variables for the component regressors to provide varying degrees of locality for the models in each ensemble. This helps our ensemble model to capture a larger range of shape variations as compared to a single PDM. We demonstrate the advantages of our method on the challenging problem of segmenting gene expression images of mouse brain.

Original languageEnglish (US)
Article number6619089
Pages (from-to)1878-1885
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013


  • ASM
  • ensemble
  • PDM

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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