Search Results for author: Pingli Ma

Found 7 papers, 0 papers with code

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

no code implementations18 Feb 2022 Jiawei Zhang, Chen Li, Md Mamunur Rahaman, YuDong Yao, Pingli Ma, Jinghua Zhang, Xin Zhao, Tao Jiang, Marcin Grzegorzek

This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.

Image Segmentation Semantic Segmentation

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

no code implementations16 Feb 2022 Wenwei Zhao, Pingli Ma, Chen Li, Xiaoning Bu, Shuojia Zou, Tao Jiang, Marcin Grzegorzek

The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey.

object-detection Object Detection

EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation

no code implementations11 Oct 2021 Hechen Yang, Chen Li, Xin Zhao, Bencheng Cai, Jiawei Zhang, Pingli Ma, Peng Zhao, Ao Chen, Hongzan Sun, Yueyang Teng, Shouliang Qi, Tao Jiang, Marcin Grzegorzek

The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ". XML" format file.

Object object-detection +1

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

no code implementations7 May 2021 Pingli Ma, Chen Li, Md Mamunur Rahaman, YuDong Yao, Jiawei Zhang, Shuojia Zou, Xin Zhao, Marcin Grzegorzek

In this review, first, we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods.

object-detection Object Detection

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