Search Results for author: Jinghua Zhang

Found 18 papers, 0 papers with code

Few-shot Class-incremental Learning: A Survey

no code implementations13 Aug 2023 Jinghua Zhang, Li Liu, Olli Silvén, Matti Pietikäinen, Dewen Hu

In our in-depth examination, we delve into various facets of FSCIL, encompassing the problem definition, the discussion of the primary challenges of unreliable empirical risk minimization and the stability-plasticity dilemma, general schemes, and relevant problems of IL and Few-shot Learning (FSL).

Few-Shot Class-Incremental Learning Few-Shot Learning +3

Few-shot Class-incremental Pill Recognition

no code implementations24 Apr 2023 Jinghua Zhang, Li Liu, Kai Gao, Dewen Hu

In practice, the expensive cost of data annotation and the continuously increasing categories of new pills make it meaningful to develop a few-shot class-incremental pill recognition system.

Few-Shot Class-Incremental Learning Graph Attention +2

Deep Learning for Iris Recognition: A Review

no code implementations15 Mar 2023 Yimin Yin, Siliang He, Renye Zhang, Hongli Chang, Xu Han, Jinghua Zhang

This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning.

Feature Engineering Iris Recognition

ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos

no code implementations15 Jan 2023 Ao Chen, Jinghua Zhang, Md Mamunur Rahaman, Hongzan Sun, M. D., Tieyong Zeng, Marcin Grzegorzek, Feng-Lei Fan, Chen Li

The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.

object-detection Object Detection

Artificial Neural Networks for Finger Vein Recognition: A Survey

no code implementations29 Aug 2022 Yimin Yin, Renye Zhang, PengFei Liu, Wanxia Deng, Siliang He, Chen Li, Jinghua Zhang

To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.

Feature Engineering Finger Vein Recognition

Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend

no code implementations5 Jul 2022 Renye Zhang, Yimin Yin, Wanxia Deng, Chen Li, Jinghua Zhang

Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields.

Finger Vein Recognition

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 Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers

no code implementations22 Jun 2021 Hechen Yang, Chen Li, Jinghua Zhang, Peng Zhao, Ao Chen, Xin Zhao, Tao Jiang, Marcin Grzegorzek

We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.

Secrecy Outage Probability of Cognitive Small-Cell Network with Unreliable Backhaul Connections

no code implementations9 Mar 2021 Jinghua Zhang, Chinmoy Kundu, Emi Garcia-Palacios

The backhaul reliability of secondary and the desired outage probability of the primary also have significant impact on the system.

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

no code implementations24 Feb 2021 Frank Kulwa, Chen Li, Jinghua Zhang, Kimiaki Shirahama, Sergey Kosov, Xin Zhao, Hongzan Sun, Tao Jiang, Marcin Grzegorzek

In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms.

Specificity

Transmitter Selection for Secrecy in Cognitive Small-Cell Networks with Backhaul Knowledge

no code implementations16 Feb 2021 Burhan Wafai, Chinmoy Kundu, Ankit Dubey, Jinghua Zhang, Mark F. Flanagan

The small-cell network is operating under a spectrum sharing agreement with a primary network in a cognitive radio system.

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

no code implementations8 Mar 2020 Jinghua Zhang, Chen Li, Frank Kulwa, Xin Zhao, Changhao Sun, Zihan Li, Tao Jiang, Hong Li

In order to assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multi-scale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper.

Image Segmentation Segmentation +1

Gastric histopathology image segmentation using a hierarchical conditional random field

no code implementations3 Mar 2020 Changhao Sun, Chen Li, Jinghua Zhang, Muhammad Rahaman, Shiliang Ai, Hao Chen, Frank Kulwa, Yixin Li, Xiaoyan Li, Tao Jiang

This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance.

Image Segmentation Segmentation +2

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