Abstract book (pdf) - ICPR 2010

14:30-14:50, Paper TuBT6.4
Intensity-Based Congealing for Unsupervised Joint Image Alignment
Storer, Markus, Graz Univ. of Tech.
Urschler, Martin, Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
We present an approach for unsupervised alignment of an ensemble of images called congealing. Our algorithm is based
on image registration using the mutual information measure as a cost function. The cost function is optimized by a standard
gradient descent method in a multiresolution scheme. As opposed to other congealing methods, which use the SSD measure,
the mutual information measure is better suited as a similarity measure for registering images since no prior assumptions
on the relation of intensities between images are required. We present alignment results on the MNIST handwritten digit
database and on facial images obtained from the CVL database.
14:50-15:10, Paper TuBT6.5
An Illumination Quality Measure for Face Recognition
Rizo-Rodriguez, Dayron, Advanced Tech. Application Center
Mendez-Vazquez, Heydi, Advanced Tech. Application Center
Garcia, Edel, Advanced Tech. Application Center
A method to determine whether face images are affected or not by lighting problems is proposed. The method is the result
of combining the analysis of lighting effect on face regions with the analysis of special areas which have a weight on the
decision. Good results were obtained classifying well and badly illuminated images. The proposed method was inserted
on a face recognition framework in order to apply the preprocessing step only to those images affected by illumination
variations. The good performance achieved on verification and identification experiments, confirm that it is better to apply
the proposed methodology than to preprocess all images when the lighting conditions are variable.
TuBT7 Dolmabahçe Hall C
Biomedical Image Segmentation Regular Session
Session chair: Kato, Zoltan (Univ. of Szeged)
13:30-13:50, Paper TuBT7.1
Cascaded Segmentation of Grained Cell Tissue with Active Contour Models
Moeller, Birgit, Martin-Luther-Univ. Halle-Wittenberg
Stöhr, Nadine, ZAMED, Martin Luther Univ. Halle-Wittenberg
Hüttelmaier, Stefan, ZAMED, Martin Luther Univ. Halle-Wittenberg
Posch, Stefan, Martin-Luther-Univ. Halle-Wittenberg
Cell tissue in microscope images is often grained and its intensities do not well agree with Gaussian distribution assumptions
widely used in many segmentation approaches. We present a new cascaded segmentation scheme for inhomogeneous
cell tissue based on active contour models. Cell regions are iteratively expanded from initial nuclei regions applying a
data-dependent number of optimization levels. Experimental results on a set of microscope images from a human hepatoma
cell line prove high quality of the results with regard to the cell segmentation task and biomedical investigations.
13:50-14:10, Paper TuBT7.2
Live Cell Segmentation in Fluorescence Microscopy via Graph Cut
Lesko, Milan, Univ. of Szeged
Kato, Zoltan, Univ. of Szeged
Nagy, Antal, Univ. of Szeged
Gombos, Imre, Hungarian Acad. of Sciences
Torok, Zsolt, Hungarian Acad. of Sciences
Vigh Jr, Laszlo, Univ. of Szeged
Vigh, Laszlo, Hungarian Acad. of Sciences
We propose a novel Markovian segmentation model which takes into account edge information. By construction, the
model uses only pairwise interactions and its energy is sub modular. Thus the exact energy minima is obtained via a maxflow/min-cut
algorithm. The method has been quantitatively evaluated on synthetic images as well as on fluorescence microscopic
images of live cells.
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