T
Theodosios Pavlidis
Researcher at State University of New York System
Publications - 93
Citations - 10199
Theodosios Pavlidis is an academic researcher from State University of New York System. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 41, co-authored 93 publications receiving 10089 citations. Previous affiliations of Theodosios Pavlidis include Princeton University & Symbol Technologies.
Papers
More filters
Journal ArticleDOI
Populations of interacting oscillators and circadian rhythms
TL;DR: It is shown that under strong coupling the oscillators oscillate at only a fraction of the frequency that each one of them would oscillate if they were not interconnected, suggesting a possible mechanism for obtaining 24-hour periods from biochemical oscillators whose period is only a few minutes.
Journal ArticleDOI
Bar code waveform recognition using peak locations
E. Joseph,Theodosios Pavlidis +1 more
TL;DR: A new feature is presented that is more resistant to the blurring process, the image, and waveform peaks, and the recognition algorithm showed a 43% performance improvement over current commercial bar code reading equipment.
Journal ArticleDOI
Information encoding with two-dimensional bar codes
TL;DR: The concept of information density is introduced, and the constraints imposed by the reading technology are addressed, and various recently proposed stacked bar codes are surveyed.
Journal ArticleDOI
Segmentation of pictures and maps through functional approximation
TL;DR: In this article, the picture segmentation problem is formulated within the framework of numerical analysis as an optimization problem of functional approximation, and a suboptimal solution is presented together with examples of its application on the segmentation of maps and pictures from a scanning electron microscope.
Journal ArticleDOI
Image segmentation as an estimation problem
TL;DR: In this article, a split-and-merge algorithm is used for image segmentation, where the regions of an arbitrary initial segmentation are tested for uniformity and if not uniform they are subdivided into smaller regions, or set aside if their size is below a given threshold.