Parametric active membrane for segmentation of multiple objects in an image

Authors:
Sitansu Kumar Das;Dipti Prasad Mukherjee
Affiliations:
Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India;Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India
Venue:
Pattern Recognition
Year:
2011

Citing 10
Cited 1

Shape Modeling with Front Propagation: A Level Set Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model

International Journal of Computer Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic 3D Shape Reconstruction of Bones Using Active Nets Based Segmentation

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Level Set Evolution without Re-Initialization: A New Variational Formulation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Maxdiff kd-trees for data condensation

Pattern Recognition Letters
Facial Component Extraction by Cooperative Active Nets with Global Constraints

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Short communication: An evaluation metric for image segmentation of multiple objects

Image and Vision Computing
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing
Active contours without edges

IEEE Transactions on Image Processing

Segmentation of multiple objects evolving conditional random field based topology adaptive active membrane

Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose a topology adaptive active membrane that can segment images of multiple objects present in a scene. The parametric active membrane evolves in image space and splits into multiple membranes. The shape of the membrane can be constrained according to the shape of the objects present in a scene. We have shown that this active membrane model is also suitable for segmenting images of touching objects. The proposed segmentation technique unifies the membrane evolution and membrane splitting process. The methodology is tested for a number of real images from biomedical and machine vision domains that demonstrate the efficacy of the proposed scheme.