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 language | English |
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Pages | 1-3 |
Number of pages | 3 |
Publication status | Published - 4 Dec 2022 |
Event | 2nd 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 2022 → 12 Apr 2022 https://uhra.herts.ac.uk/handle/2299/25867 |
Conference
Conference | 2nd School of Physics, Engineering and Computer Science Research Conference |
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Abbreviated title | SPECS 2022 |
Country/Territory | United Kingdom |
City | Hatfield |
Period | 12/04/22 → 12/04/22 |
Internet address |