Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT9.57
Detecting Faint Compact Sources using Local Features and a Boosting Approach
Torrent, Albert, Univ. of Girona
Peracaula, Marta, Univ. of Girona
Llado, Xavier, Univ. of Girona
Freixenet, Jordi, Univ. of Girona
Sanchez-Sutil, Juan Ramon, Univ. de Jaén
Martí, Josep, Univ. de Jaén
Paredes, Josep Maria, Univ. de Barcelona
Several techniques have been proposed so far in order to perform faint compact source detection in wide field interferometric
radio images. However, all these methods can easily miss some detections or obtain a high number of false positive
detections due to the low intensity of the sources, the noise ratio, and the interferometric patterns present in the images.
In this paper we present a novel strategy to tackle this problem. Our approach is based on using local features extracted
from a bank of filters in order to provide a description of different types of faint source structures. We then perform a
training step in order to automatically learn and select the most salient features, which are used in a Boosting classifier to
perform the detection. The validity of our method is demonstrated using 19 real images that compose a radio mosaic. The
comparison with two well-known state of the art methods shows that our approach is able to obtain more source detections,
reducing also the number of false positives.
13:30-16:30, Paper ThBCT9.58
Automatic Hair Detection in the Wild
Julian, Pauline, IRIT, FittingBox
Dehais, Christophe, FittingBox
Lauze, Francois, Univ. of Copenhagen
Charvillat, Vincent, IRIT
Bartoli, Adrien, UdA
Choukroun, Ariel, FittingBox
This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method
is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an
energy which uses active shape and active contour. The upper hair region then allows us to learn the hair appearance parameters
(color and texture) for the image considered. Finally, those parameters drive a pixel-wise segmentation technique
that yields the desired (complete) hair region. We demonstrate the applicability of our method on several real images.
13:30-16:30, Paper ThBCT9.59
De-Noising of SRμCT Fiber Images by Total Variation Minimization
Lindblad, Joakim, Swedish Univ. of Agricultural Sciences
Sladoje, Natasa, Univ. of Novi Sad
Lukic, Tibor, Univ. of Novi Sad
SRCT images of paper and pulp fiber materials are characterized by a low signal to noise ratio. De-noising is therefore a
common preprocessing step before segmentation into fiber and background components. We suggest a de-noising
method based on total variation minimization using a modified Spectral Conjugate Gradient algorithm. Quantitative
evaluation performed on synthetic 3D data and qualitative evaluation on real 3D paper fiber data confirm appropriateness
of the suggested method for the particular application.
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