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Feature match for underwater image via superpixel tracking

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Feature matching is fundamental to many vision tasks. Due to the low visibility of images in underwater environments, traditional pixels-based matching methods suffer from miss-matching or error-matching. Recently, Superpixel based features have been applied to image feature analysis. However, most of existing methods dedicate to rectified stereo matching with images captured in the air. This paper presents a novel feature matching scheme aiming at underwater images. It targets the un-rectified image pair from the video sequence. The Superpixel matching process is fulfilled with multiclass labelling based on Markov Random Field (MRF). Experiments show that the proposed method produces competitive performance.
Original languageEnglish
Title of host publication2017 23rd International Conference on Automation and Computing (ICAC)
ISBN (Electronic)978-0701702601
ISBN (Print)978-1509050406
Publication statusPublished - 26 Oct 2017
Event2017 23rd International Conference on Automation and Computing - , United Kingdom
Duration: 7 Sep 20178 Sep 2017


Conference2017 23rd International Conference on Automation and Computing
CountryUnited Kingdom


  • Superpixel matching2017

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    Accepted author manuscript (Post-print), 752 KB, PDF document

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