Abstract book (pdf) - ICPR 2010

MoAT6 Topkapı Hall B
Human Computer Interaction Regular Session
Session chair: Drygajlo, Andrzej (EPFL)
11:00-11:20, Paper MoAT6.1
Gaze Probing: Event-Based Estimation of Objects being Focused On
Yonetani, Ryo, Kyoto Univ.
Kawashima, Hiroaki, Kyoto Univ.
Hirayama, Takatsugu, Kyoto Univ.
Matsuyama, Takashi, Kyoto Univ.
We propose a novel method to estimate the object that a user is focusing on by using the synchronization between the
movements of objects and a user’s eyes as a cue. We first design an event as a characteristic motion pattern, and we then
embed it within the movement of each object. Since the user’s ocular reactions to these events are easily detected using a
passive camera-based eye tracker, we can successfully estimate the object that the user is focusing on as the one whose
movement is most synchronized with the user’s eye reaction. Experimental results obtained from the application of this
system to dynamic content (consisting of scrolling images) demonstrate the effectiveness of the proposed method over
existing methods.
11:20-11:40, Paper MoAT6.2
A Covariate Shift Minimisation Method to Alleviate Non-Stationarity Effects for an Adaptive Brain-Computer Interface
Satti, Abdul Rehman, Univ. of Ulster
Guan, Cuntai, Inst. For Infocomm Res.
Coyle, Damien, Univ. of Ulster
Prasad, Girijesh, Univ. of Ulster
The non-stationary nature of the electroencephalogram (EEG) poses a major challenge for the successful operation of a
brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements
and the proceeding multiple sessions can originate as a result of alterations in the subject’s brain process, new
cortical activities, change of recording conditions and/or change of operation strategies by the subject. These differences
and alterations over multiple sessions cause deterioration in BCI system performance if periodic or continuous adaptation
to the signal processing is not carried out. In this work, the covariate shift is analyzed over multiple sessions to determine
the non-stationarity effects and an unsupervised adaptation approach is employed to account for the degrading effects this
might have on performance. To improve the system’s online performance, we propose a covariate shift minimization
(CSM) method, which takes into account the distribution shift in the feature set domain to reduce the feature set overlap
and unbalance for different classes. The analysis and the results demonstrate the importance of CSM, as this method not
only improves the accuracy of the system, but also reduces the classification unbalance for different classes by a significant
amount.
11:40-12:00, Paper MoAT6.3
A Probabilistic Language Model for Hand Drawings
Akce, Abdullah, Univ. of Illinois at Urbana-Champaign
Bretl, Timothy, Univ. of Illinois at Urbana-Champaign
Probabilistic language models are critical to applications in natural language processing that include speech recognition,
optical character recognition, and interfaces for text entry. In this paper, we present a systematic way to learn a similar
type of probabilistic language model for hand drawings from a database of existing artwork by representing each stroke
as a sequence of symbols. First, we propose a language in which the symbols are circular arcs with length fixed by a scale
parameter and with curvature chosen from a fixed low-cardinality set. Then, we apply an algorithm based on dynamic
programming to represent each stroke of the drawing as a sequence of symbols from our alphabet. Finally, we learn the
probabilistic language model by constructing a Markov model. We compute the entropy of our language in a test set as
measured by the expected number of bits required for each symbol. Our language model might be applied in future work
to create a drawing interface for noisy and low-bandwidth input devices, for example an electroencephalograph (EEG)
that admits one binary command per second. The results indicate that by leveraging our language model, the performance
of such an interface would be enhanced by about 20 percent.
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