The Weibull manifold in low-level image processing: an application to automatic image focusing.
2013 (English)In: Image and Vision Computing, ISSN 0262-8856, Vol. 31, no 5, 401-417 p.Article in journal (Refereed) Published
In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve low-level image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy
Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 31, no 5, 401-417 p.
Weibull distribution;image processing;Weibull manifold;image autofocus
Engineering and Technology Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-90879DOI: 10.1016/j.imavis.2013.03.004ISI: 000319713100004OAI: oai:DiVA.org:liu-90879DiVA: diva2:614862
FunderEU, FP7, Seventh Framework Programme, 249747Swedish Research CouncilLinnaeus research environment CADICSSwedish Foundation for Strategic Research , IIS11-0081