Deformable probability maps: Probabilistic shape and appearance-based object segmentation

Authors:
Gavriil Tsechpenakis;Sotirios P. Chatzis
Affiliations:
Computer & Information Science Dept., Indiana University-Purdue University Indianapolis, United States;Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom
Venue:
Computer Vision and Image Understanding
Year:
2011

Citing 24
Cited 0

The theory and practice of Bayesian image labeling

International Journal of Computer Vision
Boundary Finding with Parametrically Deformable Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based strategies for active contour models

International Journal of Computer Vision
Shape Modeling with Front Propagation: A Level Set Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bayesian image restoration and segmentation by constrained optimization

CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Variational methods for the Dirichlet process

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Combining Top-Down and Bottom-Up Segmentation

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Information-Theoretic Active Polygons for Unsupervised Texture Segmentation

International Journal of Computer Vision
OBJ CUT

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Level Set Based Shape Prior Segmentation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Metamorphs: Deformable Shape and Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale conditional random fields for image labeling

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A graphical model framework for coupling MRFs and deformable models

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning to combine bottom-up and top-down segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Globally optimal active contours, sequential monte carlo and on-line learning for vessel segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Deformable-Model based textured object segmentation

EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing
Active contours without edges

IEEE Transactions on Image Processing
Integrated active contours for texture segmentation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present the Deformable Probability Maps (DPMs) for object segmentation, which are graphical learning models incorporating properties of deformable models into discriminative classification. The DPM configuration is described by probabilistic energy functionals, which incorporate shape and appearance, and determine boundary smoothness, image features consistency, and topology with respect to the image salient edges. Similarly to deformable models, DPMs are dynamic, and their evolution is solved as a MAP inference problem. DPMs offer two major advantages: (i) they extend the Markovian property in the image domain to incorporate local shape constraints, similar to the known internal energy of deformable models, and therefore provide increased robustness in capturing objects with fuzzy boundaries; (ii) during their evolution, DPMs update the region statistics, and therefore they are robust to image feature variations. In our experiments we evaluate the DPMs' performance in a variety of images, while we compare them with existing deformable models and classification approaches on standard benchmark datasets.