Please use this identifier to cite or link to this item:
doi:10.22028/D291-26234
Title: | Lucas/Kanade meets Horn/Schunck : combining local and global optic flow methods |
Author(s): | Weickert, Joachim Bruhn, Andres Schnörr, Christoph |
Language: | English |
Year of Publication: | 2003 |
Free key words: | computer vision differential techniques confidence measures |
DDC notations: | 510 Mathematics |
Publikation type: | Other |
Abstract: | Differential methods belong to the most widely used techniques for optic flow computation in image sequences. They can be classified into local methods such as the Lucas-Kanade technique or Bigün's structure tensor method, and into global methods such as the Horn/Schunck approach and its extensions. Often local methods are more robust under noise, while global techniques yield dense flow fields. The goal of this paper is to contribute to a better understanding and the design of differential methods in four ways: (i) We juxtapose the role of smoothing/regularisation processes that are required in local and global differential methods for optic flow computation. (ii) This discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches: It yields dense flow fields that are robust against noise. (iii) Spatiotemproal and nonlinear extensions to this hybrid method are presented. (iv) We propose a simple confidence measure for optic flow methods that minimise energy functionals. It allows to sparsify a dense flow field gradually, depending on the reliability required for the resulting flow. Comparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure. |
Link to this record: | urn:nbn:de:bsz:291-scidok-44179 hdl:20.500.11880/26290 http://dx.doi.org/10.22028/D291-26234 |
Series name: | Preprint / Fachrichtung Mathematik, Universität des Saarlandes |
Series volume: | 82 |
Date of registration: | 6-Dec-2011 |
Faculty: | MI - Fakultät für Mathematik und Informatik |
Department: | MI - Mathematik |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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