On globally optimal local modeling: From moving least squares to over-parametrization

Shachar Shem-Tov, Guy Rosman, Gilad Adiv, Ron Kimmel, Alfred M. Bruckstein

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים

תקציר

This paper discusses a variational methodology, which involves locally modeling of data from noisy samples, combined with global model parameter regularization. We show that this methodology encompasses many previously proposed algorithms, from the celebrated moving least squares methods to the globally optimal over-parametrization methods recently published for smoothing and optic flow estimation. However, the unified look at the range of problems and methods previously considered also suggests a wealth of novel global functionals and local modeling possibilities. Specifically, we show that a new non-local variational functional provided by this methodology greatly improves robustness and accuracy in local model recovery compared to previous methods. The proposed methodology may be viewed as a basis for a general framework for addressing a variety of common problem domains in signal and image processing and analysis, such as denoising, adaptive smoothing, reconstruction and segmentation.

שפה מקוריתאנגלית
כותר פרסום המארחMathematics and Visualization
עורכיםMichael BreuB, Petros Maragos, Alfred Bruckstein
עמודים379-405
מספר עמודים27
מסת"ב (אלקטרוני)9783642341410
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2013
אירועDagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011 - Dagstuhl, גרמניה
משך הזמן: 3 אפר׳ 20118 אפר׳ 2011

סדרות פרסומים

שםMathematics and Visualization
כרך0

כנס

כנסDagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, 2011
מדינה/אזורגרמניה
עירDagstuhl
תקופה3/04/118/04/11

ASJC Scopus subject areas

  • ???subjectarea.asjc.2600.2611???
  • ???subjectarea.asjc.2600.2608???
  • ???subjectarea.asjc.1700.1704???
  • ???subjectarea.asjc.2600.2604???

טביעת אצבע

להלן מוצגים תחומי המחקר של הפרסום 'On globally optimal local modeling: From moving least squares to over-parametrization'. יחד הם יוצרים טביעת אצבע ייחודית.

פורמט ציטוט ביבליוגרפי