1 / 10

Proportion Priors for Image Sequence Segmentation

Proportion Priors for Image Sequence Segmentation. Claudia Nieuwenhuis , etc. ICCV 2013 Oral. Motivation. Current algorithms about Image Sequence Segmentation Shape similarity Assumption : Rigid body transformation from similar viewpoint

minnie
Download Presentation

Proportion Priors for Image Sequence Segmentation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Proportion Priors for Image Sequence Segmentation Claudia Nieuwenhuis, etc. ICCV 2013 Oral

  2. Motivation • Current algorithms about Image Sequence Segmentation • Shape similarity • Assumption: Rigid body transformation from similar viewpoint • Reality: viewpoint changes, articulations or non-rigid deformation • Color similarity • Assumption: Similarity of color or feature distributions • Reality: Similar or overlapping color distributions between objects and background, Illumination changes • Other methods with relaxed assumptions? • Object subspaces or region correspondences, etc. • Problem: Optimization problems are complex and hard to solve.

  3. Contribution • What property of objects could be preserved among various images in a sequence? • Contributions: • Propose framework of proportional preserving priors, add ratio constraint to the classification model. • Construct a convex scheme to approximate it and calculate it efficiently. Invariant and robust to non-rigid deformation, articulation, illumination changes, color overlap Proportional information: Relative size of object parts, eg., size ratio of head to entire body

  4. Problem Definition • Bayesian inference for segmentation • : input image of a sequence on the domain • Task of segmentation: Partition the image plane into n pairwise disjoint regions • Compute a labeling Key point Observation likelihood: Color model learned from images

  5. Framework of Proportion Preserving Priors • Conditional independence assumption • Ratio constraint of one part to whole object: Background, Constant ratio constraint Short boundary length constraint

  6. Proportion Preserving Priors • Uniform Distribution Prior • Laplace Distribution Prior • Penalize deviations of the ratios from their median Advantage: simple and convex Weak point: not robust to outliers How to convert it into convex problem? See paper to get detail. Advantage: perform better and robust to outlier Weak point: not convex

  7. Results

  8. Results

  9. Results

More Related