Abstract book (pdf) - ICPR 2010

ectional and can be calculated from the projection data. A simple formula is presented for image reconstruction without
calculating the 2-D discrete Fourier transform in the case, when the size of image is Lr x Lr, when L is prime. The image
reconstruction is described by the discrete model that is used in the series expansion methods of image reconstruction.
The proposed method of reconstruction has been implemented and successfully applied for modeled images on Cartesian
grid of sizes up to 256x256.
09:00-11:10, Paper WeAT8.52
Segmentation of Cervical Cell Images
Kale, Asli, Bilkent Univ.
Aksoy, Selim, Bilkent Univ.
The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation
of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the
challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image
by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient
information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting
from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs
well for images containing both a single cell and many overlapping cells.
09:00-11:10, Paper WeAT8.53
Principal Contour Extraction and Contour Classification to Detect Coronal Loops from the Solar Images
Durak, Nurcan, Univ. of Louisville
Nasraoui, Olfa, Univ. of Louisville
In this paper, we describe a system that determines coronal loop existence from a given Solar image region in two stages:
1) extracting principal contours from the solar image regions, 2) deciding whether the extracted contours are in a loop
shape. In the first stage, we propose a principal contour extraction method that achieves 88% accuracy in extracting the
desired contours from the cluttered regions. In the second stage, we analyze the extracted contours in terms of their geometric
features such as linearity, elliptical features, curvature, proximity, smoothness, and corner points. To distinguish
loop contours from the other forms, we train an Adaboost classifier based C4.5 decision tree by using geometric features
of 150 loop contours and 250 non-loop contours. Our system achieves 85% F1-Score from 10-fold cross validation experiments.
09:00-11:10, Paper WeAT8.54
Human Shadow Removal with Unknown Light Source
Chen, Chia-Chih, The Univ. of Texas at Austin
Aggarwal, J. K., The Univ. of Texas at Austin
In this paper, we present a shadow removal technique which effectively eliminates a human shadow cast from an unknown
direction of light source. A multi-cue shadow descriptor is proposed to characterize the distinctive properties of shadows.
We employ a 3-stage process to detect then remove shadows. Our algorithm improves the shadow detection accuracy by
imposing the spatial constraint between the foreground subregions of human and shadow. We collect a dataset containing
81 human-shadow images for evaluation. Both descriptor ROC curves and qualitative results demonstrate the superior
performance of our method.
09:00-11:10, Paper WeAT8.55
Generalizing Tableau to Any Color of Teaching Boards
Oliveira, Daniel Marques, Univ. Federal de Pernambuco
Lins, Rafael Dueire, Univ. Federal de Pernambuco
Teaching boards are omnipresent in classrooms throughout the world. Tableau is a software environment for processing
images from teaching-boards acquired using portable digital cameras and cell-phones. The previous versions of Tableau
were restricted to white-board processing. This paper generalizes the enhancement algorithm to work with boards of any
color, being the first software environment able to process non-white boards.
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