Bacterial colony counting could be rapid, adaptive and automated

Minghua Zheng, Na Helian, Peter Lane, Yi Sun, Allen Donald

Research output: Contribution to conferencePaperpeer-review

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Abstract

Although many attempts have been made to automate bacterial colony counting, little has tackled the counting of clustered colonies and adaptations to handle different bacteria species. In this work, we explore the counting by density estimation method via few-shot learning. We have avoided the difficult localisation and detection of clustered colonies by estimating a density map from the input image. We have also exploited exemplars provided by users to make the method agnostic and adaptive to different bacteria species. Our experiments show that using the counting by density estimation method via few-shot learning results in a promising accuracy from the data set provided by Synoptics Ltd.
Original languageEnglish
Pages1-3
Number of pages3
Publication statusPublished - 4 Dec 2022
Event2nd School of Physics, Engineering and Computer Science Research Conference - Online - hosted by the School of Physics, Engineering and Computer Science, Hatfield, United Kingdom
Duration: 12 Apr 202212 Apr 2022
https://uhra.herts.ac.uk/handle/2299/25867

Conference

Conference2nd School of Physics, Engineering and Computer Science Research Conference
Abbreviated titleSPECS 2022
Country/TerritoryUnited Kingdom
CityHatfield
Period12/04/2212/04/22
Internet address

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