Abstract book (pdf) - ICPR 2010

done on MIT-BIH Arrhythmia database. The results have been examined and approved by medical doctors.
13:30-16:30, Paper ThBCT9.28
Crossmodal Matching of Speakers using Lip and Voice Features in Temporally Non-Overlapping Audio and Video
Streams
Roy, Anindya, Ec. Pol. Federale de Lausanne
Marcel, Sebastien, Ec. Pol. Federale de Lausanne
Person identification using audio (speech) and visual (facial appearance, static or dynamic) modalities, either independently
or jointly, is a thoroughly investigated problem in pattern recognition. In this work, we explore a novel task : person identification
in a cross-modal scenario, i.e., matching the speaker in an audio recording to the same speaker in a video recording,
where the two recordings have been made during different sessions, using speaker specific information which is
common to both the audio and video modalities. Several recent psychological studies have shown how humans can indeed
perform this task with an accuracy significantly higher than chance. Here we propose two systems which can solve this
task comparably well, using purely pattern recognition techniques. We hypothesize that such systems could be put to practical
use in multimodal biometric and surveillance systems.
13:30-16:30, Paper ThBCT9.29
Image Parsing with a Three-State Series Neural Network Classifier
Seyedhosseini Tarzjani, Seyed Mojtaba, Univ. of Utah
Paiva, Antonio, Univ. of Utah
Tasdizen, Tolga, Univ. of Utah
We propose a three-state series neural network for effective propagation of context and uncertainty information for image
parsing. The activation functions used in the proposed model have three states instead of the normal two states. This makes
the neural network more flexible than the two-state neural network, and allows for uncertainty to be propagated through
the stages. In other words, decisions about difficult pixels can be left for later stages which have access to more contextual
information than earlier stages. We applied the proposed method to three different datasets and experimental results demonstrate
higher performance of the three-state series neural network.
13:30-16:30, Paper ThBCT9.30
Pan-Sharpening using an Adaptive Linear Model
Liu, Lining, Beihang Univ.
Wang, Yiding, North China Univ. of Tech.
Wang, Yunhong, Beihang Univ.
Yu, Haiyan, Beihang Univ.
In this paper, we propose an algorithm to synthesize high-resolution multispectral images by fusing panchromatic (Pan)
images and multispectral (MS) images. The algorithm is based on an adaptive linear model, which is automatically estimated
by least square fitting. In this model, a virtual difference band is appended to the MS to guarantee the correlation
between the Pan and MS. Then, an iterative procedure is carried out to generate the fused images using steepest descent
method. The efficiency of the presented technique is tested by performing pan-sharpening of IKONOS, Quick Bird, and
Landsat-7 ETM+ datasets. Experimental results show that our method provides better fusion results than other methods.
13:30-16:30, Paper ThBCT9.31
A Study of Voice Source and Vocal Tract Filter based Features in Cognitive Load Classification
Le, Phu, The Univ. of New South Wales
Epps, Julien, The Univ. of New South Wales
Choi, Eric, ational ICT Australia
Ambikairajah, Eliathamby, The Univ. of New South Wales
Speech has been recognized as an attractive method for the measurement of cognitive load. Previous approaches have
used mel frequency cepstral coefficients (MFCCs) as discriminative features to classify cognitive load. The MFCCs contain
information from both the voice source and the vocal tract, so that the individual contributions of each to cognitive load
variation are unclear. This paper aims to extract speech features related to either the voice source or the vocal tract and use
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