Abstract book (pdf) - ICPR 2010

16:00-16:20, Paper TuCT3.2
Level-Set Segmentation of Brain Tumors using a New Hybrid Speed Function
Cho, Wanhyun, Chonnam National Univ.
Park, Jonghyun, Chonnam National Univ.
Park, Soonyoung, Mokpo National Univ.
Kim, Soohyung, Chonnam National Univ.
Kim, Sunworl, Chonnam National Univ.
Ahn, Gukdong, Chonnam National Univ.
Lee, Myungeun, Chonnam National Univ.
Lee, Gueesang, Chonnam National Univ.
This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework.
This speed function provides a general form that incorporates the alignment term as a part of the driving force for the
proper edge direction of an active contour by using the probability term derived from the region partition scheme and, for
regularization, the geodesics contour term. First, we use an external force for active contours as the Gradient Vector Flow
field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we
partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two
Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the
image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves,
which allows stable boundary detection when the model’s gradients suffer from large variations including gaps or noise.
Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results
show that our method can properly segment low contrast, complex images.
16:20-16:40, Paper TuCT3.3
The Impact of Color on Bag-of-Words based Object Recognition
Rojas Vigo, David Augusto, Computer Vision Center Barcelona
Shahbaz Khan, Fahad, Computer Vision Center Barcelona
Van De Weijer, Joost, Computer Vision Center Barcelona
Gevers, Theo, Univ. of Amsterdam
In recent years several works have aimed at exploiting color information in order to improve the bag-of-words based
image representation. There are two stages in which color information can be applied in the bag-of-words framework.
Firstly, feature detection can be improved by choosing highly informative color-based regions. Secondly, feature description,
typically focusing on shape, can be improved with a color description of the local patches. Although both approaches
have been shown to improve results the combined merits have not yet been analyzed. Therefore, in this paper we investigate
the combined contribution of color to both the feature detection and extraction stages. Experiments performed on two
challenging data sets, namely Flower and Pascal VOC 2009; clearly demonstrate that incorporating color in both feature
detection and extraction significantly improves the overall performance.
16:40-17:00, Paper TuCT3.4
Pyramidal Model for Image Semantic Segmentation
Passino, Giuseppe, Queen Mary, Univ. of London
Patras, Ioannis, Queen Mary, Univ. of London
Izquierdo, Ebroul, Queen Mary, Univ. of London
We present a new hierarchical model applied to the problem of image semantic segmentation, that is, the association to
each pixel in an image with a category label (e.g. tree, cow, building, ...). This problem is usually addressed with a combination
of an appearance-based pixel classification and a pixel context model. In our proposal, the images are initially
over-segmented in dense patches. The proposed pyramidal model naturally embeds the compositional nature of a scene to
achieve a multi-scale contextualisation of patches. This is obtained by imposing an order on the patches aggregation operations
towards the final scene. The nodes of the pyramid (that is, a dendrogram) thus represent patch clusters, or superpatches.
The probabilistic model favours the homogeneous labelling of super-patches that are likely to contain a single
object instance, modelling the uncertainty in identifying such super-patches. The proposed model has several advantages,
including the computational efficiency, as well as the expandability. Initial results place the model in line with other works
in the recent literature.
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