Search Results for author:
Found 23 papers, 5 papers with code
A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems
In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them.
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit
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 Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images
In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images.
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
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.
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
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.
Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network
In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions.
Object Sorting Using a Global Texture-Shape 3D Feature Descriptor
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.
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems
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.
Automated Strabismus Detection for Telemedicine Applications
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.
Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems
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.
Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
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.
Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks
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.
Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network
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.
An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming
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.
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation
The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases.
Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement
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.
Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture
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.
Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm
Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation.
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation
In this paper, we propose a network injected with contextual information (CI-Net) to solve the problem.
Ranked #33 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems
This study proposes a distributed algorithm that makes agents' adaptive grouping entrap multiple targets via automatic decision making, smooth flocking, and well-distributed entrapping.
RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation
In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy.
Revisiting Stereo Triangulation in UAV Distance Estimation
In this work, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors.
CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback
Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks.
