Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

• Main Authors: Tarabalka, Yuliya, Charpiat, Guillaume, Brucker, Ludovic, Menze, Bjoern
• Other Authors: Models of spatio-temporal structure for high-resolution image processing (AYIN), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Spatio-Temporal Activity Recognition Systems (STARS), NASA Goddard Space Flight Center (GSFC), Analysis and Simulation of Biomedical Images (ASCLEPIOS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2014
• Subjects: [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; Sea ice
• Online Access: https://hal.inria.fr/hal-01052543; https://hal.inria.fr/hal-01052543/document; https://hal.inria.fr/hal-01052543/file/2014_IEEETIP_Tarabalka.pdf

International audience We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally-optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional inter-sequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.