Search Results for author: Zhun Fan

Found 19 papers, 4 papers with code

Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm

no code implementations29 Oct 2020 Jiahong Wei, Zhun Fan

Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation.

Retinal Vessel Segmentation

Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture

no code implementations1 Jul 2020 Zhun Fan, Chong Li, Ying Chen, Jiahong Wei, Giuseppe Loprencipe, Xiaopeng Chen, Paola Di Mascio

Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection.

Object Detection

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

no code implementations8 Feb 2020 Zhun Fan, Chong Li, Ying Chen, Paola Di Mascio, Xiaopeng Chen, Guijie Zhu, Giuseppe Loprencipe

In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement.

Evolutionary Neural Architecture Search for Retinal Vessel Segmentation

no code implementations18 Jan 2020 Zhun Fan, Jiahong Wei, Guijie Zhu, Jiajie Mo, Wenji Li

The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases.

Neural Architecture Search Retinal Vessel Segmentation

An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming

no code implementations31 Oct 2019 Zhun Fan, Zhaojun Wang, Xiaomin Zhu, Bingliang Hu, Anmin Zou, Dongwei Bao

Most existing swarm pattern formation methods depend on a predefined gene regulatory network (GRN) structure that requires designers' priori knowledge, which is difficult to adapt to complex and changeable environments.

Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network

2 code implementations28 Jun 2019 Zhun Fan, Jiajie Mo, Benzhang Qiu, Wenji Li, Guijie Zhu, Chong Li, Jianye Hu, Yibiao Rong, Xinjian Chen

Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions and octave transposed convolutions for learning multiple-spatial-frequency features, thus can better capture retinal vasculatures with varying sizes and shapes.

Retinal Vessel Segmentation

Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks

1 code implementation3 Jun 2019 Zhun Fan, Jiewei Lu, Benzhang Qiu, Tao Jiang, Kang An, Alex Noel Josephraj, Chuliang Wei

The proposed CNN-DC can achieve 99. 26% accuracy for steel bar counting and 4. 1% center offset for center localization on the established steel bar dataset, which demonstrates that the proposed CNN-DC can perform well on automated steel bar counting and center localization.

Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems

no code implementations2 Jun 2019 Zhun Fan, Zhaojun Wang, Wenji Li, Yutong Yuan, Yugen You, Zhi Yang, Fuzan Sun, Jie Ruan, Zhaocheng Li

In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations.

Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems

no code implementations16 Dec 2018 Zhun Fan, Wenji Li, Zhaojun Wang, Yutong Yuan, Fuzan Sun, Zhi Yang, Jie Ruan, Zhaocheng Li, Erik Goodman

In the top sub-population, the search process is divided into two different stages --- push and pull stages. An adaptive DE variant with three trial vector generation strategies is employed in the proposed PPS-DE.

Automated Strabismus Detection for Telemedicine Applications

1 code implementation9 Sep 2018 Jiewei Lu, Zhun Fan, Ce Zheng, Jingan Feng, Longtao Huang, Wenji Li, Erik D. Goodman

Telemedicine, which has great potential to alleviate the growing demand of the diagnosis of ophthalmologic diseases, is an effective method to achieve timely strabismus detection.

MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems

no code implementations10 Feb 2018 Zhun Fan, Yi Fang, Wenji Li, Xinye Cai, Caimin Wei, Erik Goodman

The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.

Object Sorting Using a Global Texture-Shape 3D Feature Descriptor

no code implementations4 Feb 2018 Zhun Fan, Zhongxing Li, Benzhang Qiu, Wenji Li, Jianye Hu, Alex Noel Josephraj, Heping Chen

In this paper, we present a global texture-shape 3D feature descriptor which can be utilized in a system of object recognition and grasping, and can perform object sorting tasks well.

Object Detection Object Recognition

Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network

1 code implementation1 Feb 2018 Zhun Fan, Yuming Wu, Jiewei Lu, Wenji Li

In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions.

Multi-Label Classification Structured Prediction

Push and Pull Search for Solving Constrained Multi-objective Optimization Problems

no code implementations15 Sep 2017 Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Caimin Wei, Qingfu Zhang, Kalyanmoy Deb, Erik D. Goodman

Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and non-dominated regions by applying push and pull search strategies at different stages.

An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions

no code implementations27 Jul 2017 Zhun Fan, Wenji Li, Xinye Cai, Han Huang, Yi Fang, Yugen You, Jiajie Mo, Caimin Wei, Erik Goodman

In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs.

A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images

no code implementations4 Jan 2017 Zhun Fan, Jiewei Lu, Wenji Li, Caimin Wei, Han Huang, Xinye Cai, Xinjian Chen

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images.

Image Matting

Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit

no code implementations21 Dec 2016 Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Caimin Wei, Qingfu Zhang, Kalyanmoy Deb, Erik D. Goodman

Multi-objective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems.

A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems

no code implementations1 Apr 2015 Zhun Fan, Wenji Li, Xinye Cai, Huibiao Lin, Shuxiang Xie, Erik Goodman

In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them.

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