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Improving the Fast Evaluation of the Robust Stochastic Watershed
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The stochastic watersheds algorithm was first proposed by Angulo and Jeulin (2007) as a marker-controlled watershed-based stochastic segmentation method using Monte Carlo simulation. This project is based on the work of Selig et al. (2015), Fast Evaluation of the Robust Stochastic Watershed, which was the extension of Malmberg and Luengo Hendriks (2014) and Malmberg et al. (2014) who introduced an exact and efficient evaluation method of the stochastic watershed. The algorithm proposed running the exact evaluation method three times after adding noise to the input image then averaging the three edge probabilities together. Their method was identical in terms of average F-measure, but it was an order of magnitude shorter. This project aimed to propose an improved version of Selig et al.'s algorithm which is better in terms of accuracy and faster in terms of processing time. The final result was an algorithm that is matching in accuracy but about 25% faster.

Place, publisher, year, edition, pages
2015. , 36 p.
IT, 15085
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-272187OAI: oai:DiVA.org:uu-272187DiVA: diva2:893329
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2016-01-13Bibliographically approved

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