Abstract book (pdf) - ICPR 2010

17:00-17:20, Paper WeCT3.5
Length Increasing Active Contour for the Segmentation of Small Blood Vessels
Rivest-Hénault, David, École de Tech. Supérieure
Deschênes, Sylvain, Sainte-Justine Hospital
Lapierre, Chantale, Hospital Sainte-Justine
Cheriet, Mohammed, École de Tech. Supérieure
A new level-set based active contour method for the segmentation of small blood vessels and other elongated structures
is presented. Its main particularity is the presence of a length increasing force in the contour driving equation. The effect
of this force is to push the active contour in the direction of thin elongated shapes. Although the proposed force is not
stable in general, our experiments show that with few precautions it can successfully be integrated in a practical segmentation
scheme and that it helps to segment a longer part of the structures of interest. For the segmentation of blood vessels,
this may reduce the amount of user interactivity needed: only a small region inside the structure of interest need to be
specified.
WeCT4 Anadolu Auditorium
Graphical Models and Bayesian Methods Regular Session
Session chair: Murino, Vittorio (Univ. of Verona)
15:40-16:00, Paper WeCT4.1
Using Sequential Context for Image Analysis
Paiva, Antonio, Univ. of Utah
Jurrus, Elizabeth, Univ. of Utah
Tasdizen, Tolga, Univ. of Utah
This paper proposes the sequential context inference (SCI) algorithm for Markov random field (MRF) image analysis.
This algorithm is designed primarily for fast inference on an MRF model, but its application requires also a specific modeling
architecture. The architecture is composed of a sequence of stages, each modeling the conditional probability of the
labels, conditioned on a neighborhood of the input image and output of the previous stage. By learning the model at each
stage sequentially with regards to the true output labels, the stages learn different models which can cope with errors in
the previous stage.
16:00-16:20, Paper WeCT4.2
Recovery Video Stabilization using MRF-MAP Optimization
Kim, Soo Wan, Seoul National Univ.
Yi, Kwang Moo, Automation and System Res. Inst. Univ.
Oh, Songhwai, Seoul National Univ.
Choi, Jin Young, Seoul National University
In this paper, we propose a novel approach for video stabilization using Markov random field (MRF) modeling and maximum
a posteriori (MAP) optimization. We build an MRF model describing a sequence of unstable images and find joint
pixel matchings over all image sequences with MAP optimization via Gibbs sampling. The resulting displacements of
matched pixels in consecutive frames indicate the camera motion between frames and can be used to remove the camera
motion to stabilize image sequences. The proposed method shows robust performance even when a scene has moving
foreground objects and brings more accurate stabilization results. The performance of our algorithm is evaluated on outdoor
scenes.
16:20-16:40, Paper WeCT4.3
Annealed SMC Samplers for Dirichlet Process Mixture Models
Ülker, Yener, Istanbul Tech. Univ.
Gunsel, Bilge, Istanbul Tech. Univ.
Cemgil, Ali Taylan, Bogazici Univ.
In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model
posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an
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