Sigmedia Talks Page

Upcoming Talks, 2009-2010 Talks?, 2011-2012 Talks?, 2012-2013 Talks?, 2013-2014 Talks?

Upcoming Speakers

 Semester 1 (Wednesdays at Noon)
22-Oct-14Yun Feng Wang
29-Oct-14Ailbhe Cullen
12-Nov-13Andrew Hines
10-Dec-13Colm O'Reilly

Speaker Yun Feng Wang
Title Automated Registration of Low and High Resolution Atomic Force Microscopy Images Using Scale Invariant Features
Time & Venue Printing House Hall - 12:00 22-Oct-14

Our work introduces a method for registering scans acquired by Atomic Force Microscopy (AFM). Due to compromises between scan size, resolution, and scan rate, high resolution data is only attainable in a very limited field of view. Our proposed method uses a sparse set of feature matches between the low and high resolution AFM scans and maps them onto a common coordinate system. This can provide a wider field of view of the sample and give context to the regions where high resolution AFM data has been obtained. The algorithm employs a robust approach overcoming complications due to temporal sample changes and sample drift of the AFM system which becomes significant at higher-resolutions. To our knowledge, this is the first approach for automatic high resolution AFM image registration. Experimental results show the correctness and robustness of our approach and shows that the estimated transforms can be used to deduce plausible measures of sample drift.

Speaker Ailbhe Cullen
Title Building a Database of Political Speech - Does culture matter in charisma annotations?
Time & Venue Printing House Hall - 12:00 29-Oct-14

For both individual politicians and political parties the internet has become a vital tool for self-promotion and the distribution of ideas. The rise of streaming has enabled political debates and speeches to reach global audiences. In this paper, we explore the nature of charisma in political speech, with a view to automatic detection. To this end, we have collected a new database of political speech from YouTube and other on-line resources. Annotation is performed both by native listeners, and Amazon Mechanical Turk (AMT) workers. Detailed analysis shows that both label sets are equally reliable. The results support the use of crowd-sourced labels for speaker traits such as charisma in political speech, even where cultural subtleties are present. The impact of these different annotations on charisma prediction from political speech is also investigated.

Page last modified on October 28, 2014