BACKGROUND RESTORATION IN FRAME AREAS WITH SMALL-SIZE OBJECTS IN VIDEO SEQUENCES


Цитировать

Полный текст

Аннотация

A general concept of removal of artificially overlaid images, natural damages of video images and other small-size objects is presented. The classification of artificially overlaid images is developed. The algorithms of feature points detection and feature points tracking used in video sequence restoration are considered.

Полный текст

The task of restoration of video sequences is getting more and more actual as a result of computer engineering development. The restoration of the original images under artificially overlaid object (TV channel logotypes, subtitles etc.) and other small-size objects such as a man, a tree, a stone etc. on a certain background as well as the images distorted as a result of damage of information carrier (scratches on the film etc.) is of primary importance. The solution of this problem in general will result in reduction of costs of video reutilization such as old film remastering, original video forwarding by different TV channels with removal of earlier overlaid, but irrelevant now, computer graphics, and accidentally shot objects, for example, advertising structures. Overlaid computer graphic images occurred in video can be divided into several groups. There are TV channels logotypes that can be defined as small-size images arranged in one or several frame corners or frame borders; the second group is titles, that is, text areas with information about film makers arranged in any place of a frame; the third group is subtitles, which can be defined as text areas near the top or bottom frame borders with Mathematics, mechanics, computer science 20 periodically changed static text; and finally a creeping line that can be defined as a text area at the top or bottom frame borders with the text moving according to generally accepted rules of reading and writing.
×

Об авторах

M. V. Damov

Список литературы

  1. Damov М. V. Spatial method of localization of logotypes images in video sequences // Science. Technology. Innovations. NTI-2008 : Materials of the All-Russian scientific Conference of young scientists. P. 1. Novosibirsk, 2008. P. 191–193.
  2. Lucas B. D., Kanade T. An iterative image registration technique with an application to stereo vision // Proc. of Imaging understanding workshop. 1981. P. 121–133.
  3. Making good features to track better / T. Tommasini [et al.] // Proc/ IEEE Computer Society Conference on Computer Vision Pattern Recognition. 1998. P. 145–149.
  4. Rares A., Reinders M. J. T., Biemond J. Recovery of partially degraded colors in old movie // Proc. of EUSIPCO-2002. Toulouse, 2003.
  5. Forsyth D. А., Ponce J. Computer vision: modern approach : transl. from English. М. : Williams, 2004. P. 928.

Дополнительные файлы

Доп. файлы
Действие
1. JATS XML

© Damov M.V., 2010

Creative Commons License
Эта статья доступна по лицензии Creative Commons Attribution 4.0 International License.

Данный сайт использует cookie-файлы

Продолжая использовать наш сайт, вы даете согласие на обработку файлов cookie, которые обеспечивают правильную работу сайта.

О куки-файлах