Arrow Research search

Author name cluster

Jan Larsen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers
1 author row

Possible papers

4

AIIM Journal 2002 Journal Article

Exploring fMRI data for periodic signal components

  • Lars Kai Hansen
  • Finn Årup Nielsen
  • Jan Larsen

We use a Bayesian framework to detect periodic components in fMRI data. The resulting detector is sensitive to periodic components with a flexible number of harmonics and with arbitrary amplitude and phases of the harmonics. It is possible to detect the correct number of harmonics in periodic signals even if the fundamental frequency is beyond the Nyquist frequency. We apply the signal detector to locate regions that are highly affected by periodic physiological artifacts, such as cardiac pulsation.

Details DOI

YNIMG Journal 1999 Journal Article

Generalizable Patterns in Neuroimaging: How Many Principal Components?

  • Lars Kai Hansen
  • Jan Larsen
  • Finn Årup Nielsen
  • Stephen C. Strother
  • Egill Rostrup
  • Robert Savoy
  • Nicholas Lange
  • John Sidtis

Generalization can be defined quantitatively and can be used to assess the performance of principal component analysis (PCA). The generalizability of PCA depends on the number of principal components retained in the analysis. We provide analytic and test set estimates of generalization. We show how the generalization error can be used to select the number of principal components in two analyses of functional magnetic resonance imaging activation sets.

Details DOI

NeurIPS Conference 1995 Conference Paper

Classifying Facial Action

  • Marian Bartlett
  • Paul Viola
  • Terrence Sejnowski
  • Beatrice Golomb
  • Jan Larsen
  • Joseph Hager
  • Paul Ekman

The Facial Action Coding System, (FACS), devised by Ekman and Friesen (1978), provides an objective meanS for measuring the facial muscle contractions involved in a facial expression. In this paper, we approach automated facial expression analysis by detecting and classifying facial actions. We generated a database of over 1100 image sequences of 24 subjects performing over 150 distinct facial actions or action combinations. We compare three different ap(cid: 173) proaches to classifying the facial actions in these images: Holistic spatial analysis based on principal components of graylevel images; explicit measurement of local image features such as wrinkles; and template matching with motion flow fields. On a dataset contain(cid: 173) ing six individual actions and 20 subjects, these methods had 89%, 57%, and 85% performances respectively for generalization to novel subjects. When combined, performance improved to 92%.

PDF Details
v2026.09.13