EMDS-5: Environmental Microorganism Image Dataset Fifth Version for
Multiple Image Analysis Tasks

EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks

20 February 2021

Papers citing "EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks"

7 / 7 papers shown

Title
Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features Frank Kulwa Chen Li M. Grzegorzek M. Rahaman Kimiaki Shirahama S. Kosov 46 8 0 31 Aug 2022
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation Penghui Zhao Chen Li M. Rahaman Hao Xu Pingli Ma Hechen Yang Hongzan Sun Tao Jiang N. Xu M. Grzegorzek 29 19 0 14 Dec 2021
EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation Hechen Yang Chen Li Xin Zhao Bencheng Cai Jiawei Zhang ... Hongzan Sun Yueyang Teng Shouliang Qi Tao Jiang M. Grzegorzek 46 10 0 11 Oct 2021
Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer Jinghua Zhang Chen Li Yi Yin Jiawei Zhang M. Grzegorzek 34 140 0 01 Aug 2021
A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers Penghui Zhao Chen Li M. Rahaman Hao Xu Hechen Yang Hongzan Sun Tao Jiang M. Grzegorzek VLM 35 39 0 16 Jul 2021
A Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers Hechen Yang Chen Li Jinghua Zhang Penghui Zhao Ao Chen Xin Zhao Tao Jiang M. Grzegorzek 33 1 0 22 Jun 2021
A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches Pingli Ma Chen Li M. Rahaman Yudong Yao Jiawei Zhang Shuojia Zou Xin Zhao M. Grzegorzek 37 62 0 07 May 2021