Abstract book (pdf) - ICPR 2010

ThAT5 Dolmabahçe Hall B
Image Segmentation - I Regular Session
Session chair: Puig, Domenec (Univ. Rovira i Virgili)
09:00-09:20, Paper ThAT5.1
Robust Color Image Segmentation through Tensor Voting
Moreno, Rodrigo, Rovira i Virgili Univ.
Garcia Garcia, Miguel Angel, Autonomous Univ. of Madrid
Puig, Domenec, Univ. Rovira i Virgili
This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping
technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising
and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likelyinhomogeneous
by means of the edginess maps generated in the first step. Third, the likely-homosgeneous pixels are segmented
through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is
applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed
algorithm has a better performance than the state-of-the-art.
09:20-09:40, Paper ThAT5.2
An Improved Fluid Vector Flow for Cavity Segmentation in Chest Radiographs
Xu, Tao, Univ. of Alberta
Cheng, Irene, Univ. of Alberta
Mandal, Mrinal, Univ. of Alberta
Fluid vector flow (FVF) is a recently developed edge-based parametric active contour model for segmentation. By keeping
its merits of large capture range and ability to handle acute concave shapes, we improved the model from two aspects:
edge leakage and control point selection. Experimental results of cavity segmentation in chest radiographs show that the
proposed method provides at least 8% improvement over the original FVF method.
09:40-10:00, Paper ThAT5.3
Patchy Aurora Image Segmentation based on ALBP and Block Threshold
Fu, Rong, Xidian Univ.
Gao, Xinbo, Xidian Univ.
Jian, Yongjun, Xidian Univ.
The proportion of aurora region to the field of view is an important index to measure the range and scale of aurora. A
crucial step to obtain the index is to segment aurora region from the background. A simple and efficient aurora image segmentation
algorithm is proposed, which is composed of feature representation based on adaptive local binary patterns
(ALBP) and aurora region estimation through block threshold. First the ALBP features of sky image are extracted and the
threshold is determined. The aurora image to be segmented is then equally divided into detection blocks from which ALBP
features are also extracted. Aurora block is estimated through comparison its ALBP features with the threshold. Simple as
it is, processing in huge data set is possible. The experiment illustrates the segmentation effect of the proposed method is
satisfying from human visual aspect and segmentation accuracy.
10:00-10:20, Paper ThAT5.4
Retinal Image Segmentation based on Mumford-Shah Model and Gabor Wavelet Filter
Du, Xiaojun, Concordia Univ.
Bui, Tien D., Concordia Univ.
Automatic retinal image segmentation is desirable for some disease diagnosis such as diabetes. In this paper, we propose
a new image segmentation method to segment retinal images. The new method is based on the Mumford-Shah (MS)
model. As a region-based approach, the MS model is a good segmentation technique. However, due to non-uniform illumination,
some traditional approximations of the MS model cannot deal with this type of problems. We present a new
method that requires no approximations. Instead, Gabor wavelet filter is used, and the method can segment objects with
complicated image intensity distribution. The method is used to detect blood vessels in retinal images. The results are
comparable with or better than state-of-the-art. Our method requires no training and is relatively fast.
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