Multiple combined constraints for optical flow estimation

Ahmed Fahad, Tim Morris

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Several approaches to optical flow estimation use differential methods to model changes in image brightness over time. In computer vision it is often desirable to over constrain the problem to more precisely determine the solution and enforce robustness. In this paper, two new solutions for optical flow computation are proposed which are based on combining brightness and gradient constraints using more than one quadratic constraint embedded in a robust statistical function. Applying the same set of differential equations to different quadratic error functions produces different results. The two techniques combine the advantages of different constraints to achieve the best results. Experimental comparisons of estimation errors against those of well-known synthetic ground-truthed test sequences showed good qualitative performance. © Springer-Verlag Berlin Heidelberg 2007.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages11-20
    Number of pages9
    Volume4842
    ISBN (Print)9783540768555
    Publication statusPublished - 2007
    Event3rd International Symposium on Visual Computing, ISVC 2007 - Lake Tahoe, NV
    Duration: 1 Jul 2007 → …

    Other

    Other3rd International Symposium on Visual Computing, ISVC 2007
    CityLake Tahoe, NV
    Period1/07/07 → …

    Fingerprint

    Dive into the research topics of 'Multiple combined constraints for optical flow estimation'. Together they form a unique fingerprint.

    Cite this