Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT8.25
Background Modeling by Combining Joint Intensity Histogram with Time-Sequential Data
Kita, Yasuyo, National Inst. of Advanced Industrial Science and Technology
In this paper, a method for detecting changes from time-sequential images of outside scenes which are taken with several
minutes interval is proposed. Recently, statistical background intensity model per pixel using Gaussian mixture model
(GMM) has shown its effectiveness for detecting changes from video streams. However, when the time interval between
consecutive images is long, enough number of frames can not be sampled for building useful GMM. To robustly build a
pixel wise background model at time t0 from small number of fore and aft frames, we propose to use the joint intensity
histogram of the images at time t0 and t0 + 1, H(It0, Ito+1). Under background dominance condition, background probability
distribution for each intensity level at t0 can be estimated from H(It0, Ito+1). By taking this background probability
distribution per intensity as a prior probability, GMM which models the variation in each pixel is robustly calculated even
from several frames. Experimental results using actual field monitoring images have shown the advantage of the proposed
method.
09:00-11:10, Paper TuAT8.26
2LDA: Segmentation for Recognition
Perina, Alessandro, Univ. of Verona
Cristani, Marco, Univ. of Verona
Murino, Vittorio, Univ. of Verona
Following the trend of segmentation for recognition, we present 2LDA, a novel generative model to automatically segment
an image in 2 segments, background and foreground, while inferring a latent Dirichlet allocation (LDA) topic distribution
on both segments. The idea is to merge two separate modules, LDA and the segmentation module, explicitly considering
(and exchanging) the uncertainty between them. The resulting model adds spatial relationships to LDA, which in turn
helps in using the topics to segment an image. The experimental results show that, unlike LDA, our model can be used to
recognize objects, and also outperforms the state of the art algorithms.
09:00-11:10, Paper TuAT8.27
Modeling and Generalization of Discrete Morse Terrain Decompositions
De Floriani, L.
Magillo, Paola, Univ. of Genova
Vitali, Maria, DISI, Univ. of Genova
We address the problem of morphological analysis of real terrains. We describe a morphological model for a terrain by
considering extensions of Morse theory to the discrete case. We propose a two-level model of the morphology of a terrain
based on a graph joining the critical points of the terrain through integral lines. We present a new set of generalization operators
specific for discrete piece-wise linear terrain models, which are used to reduce noise and the size of the morphological
representation. We show results of our approach on real terrains.
09:00-11:10, Paper TuAT8.28
Region Description using Extended Local Ternary Patterns
Liao, Wen-Hung, National Chengchi Univ.
The local binary pattern (LBP) operator is a computationally efficient local texture descriptor and has found many useful
applications. However, its sensitivity to noise and the high dimensionality of histogram associated with a mediocre size
neighborhood have raised some concerns. In this paper, we attempt to improve the original LBP by proposing a novel extension
named extended local ternary pattern (ELTP). We will investigate the characteristics of ELTP in terms of noise
sensitivity, discriminability and computational efficiency. Preliminary experimental results have shown better efficacy of
ELTP over the original LBP.
09:00-11:10, Paper TuAT8.29
A Novel Multi-View Agglomerative Clustering Algorithm based on Ensemble of Partitions on Different Views
Mirzaei, Hamidreza, SFU
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