Abstract book (pdf) - ICPR 2010

Lovell, Brian Carrington, The Univ. of Queensland
We propose a region-based foreground object segmentation method capable of dealing with image sequences containing
noise, illumination variations and dynamic backgrounds (as often present in outdoor environments). The method utilises
contextual spatial information through analysing each frame on an overlapping block by-block basis and obtaining a lowdimensional
texture descriptor for each block. Each descriptor is passed through an adaptive multi-stage classifier, comprised
of a likelihood evaluation, an illumination invariant measure, and a temporal correlation check. The overlapping of
blocks not only ensures smooth contours of the foreground objects but also effectively minimises the number of false positives
in the generated foreground masks. The parameter settings are robust against wide variety of sequences and postprocessing
of foreground masks is not required. Experiments on the challenging I2R dataset show that the proposed method
obtains considerably better results (both qualitatively and quantitatively) than methods based on Gaussian mixture models
(GMMs), feature histograms, and normalised vector distances. On average, the proposed method achieves 36% more accurate
foreground masks than the GMM based method.
13:50-14:10, Paper ThBT4.2
Flooding and MRF-Based Algorithms for Interactive Segmentation
Grinias, Ilias, Univ. of Crete
Komodakis, Nikos, Univ. of Crete
Tziritas, G., Univ. of Crete
We propose a method for interactive colour image segmentation. The goal is to detect an object from the background,
when some markers on object(s) and the background are given. As features only probability distributions of the data are
used. At first, all the labelled seeds are independently propagated for obtaining homogeneous connected components for
each of them. Then the image is divided in blocks, which are classified according to their probabilistic distance from the
classified regions. A topographic surface for each class is obtained, using Bayesian dissimilarities and a min-max criterion.
Two algorithms are proposed: a regularized classification based on the topographic surface and incorporating an MRF
model, and a priority multi-label flooding algorithm. Segmentation results on the LHI data set are presented.
14:10-14:30, Paper ThBT4.3
Steerable Filtering using Novel Circular Harmonic Functions with Application to Edge Detection
Papari, Giuseppe, Univ. of Groningen
Campisi, Patrizio, Univ. degli Studi Roma TRE
Petkov, N, Univ. of Groningen
In this paper, we perform approximate steering of the elongated 2D Hermite-Gauss functions with respect to rotations and
provide a compact analytical expressions for the related basis functions. A special notation introduced here considerably
simplifies the derivation and unifies the cases of even and odd indices. The proposed filters are applied to edge detection.
Quantitative analysis shows a performance increase of about 12.5% in terms of the Pratt’s figure of merit with respect to
the well-established Gaussian gradient proposed by Canny.
14:30-14:50, Paper ThBT4.4
3D Vertebral Body Segmentation using Shape based Graph Cuts
Aslan, Melih Seref, Univ. of Louisville
Ali, Asem, Univ. of Louisville
Farag, Aly A., Univ. of Louisville
Rara, Ham, Univ. of Louisville
Arnold, Ben, Image Analysis, Inc.
Ping, Xiang, Image Analysis, Inc.
Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies
(VBs). In this paper, we propose a novel 3D shape based method to segment VBs in clinical computed tomography (CT)
images without any user intervention. The proposed method depends on both image appearance and shape information.
3D shape information is obtained from a set of training data sets. Then, we estimate the shape variations using a distance
probabilistic model which approximates the marginal densities of the VB and background in the variability region. To
segment a VB, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D
shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear
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