29 Result(s)
within
Fazhi **e
Artificial Intelligence
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Article
MEAN: An attention-based approach for 3D mesh shape classification
3D shape processing is a fundamental computer application. Specifically, 3D mesh could provide a natural and detailed way for object representation. However, due to its non-uniform and irregular data structure...
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Article
A decomposition-based many-objective evolutionary algorithm with weight grou** and adaptive adjustment
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted tremendous interest and have been thoroughly developed because of their excellent performance in multi/many-objective optim...
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Article
WalkFormer: 3D mesh analysis via transformer on random walk
A 3D mesh is a popular representation of 3D shapes. For mesh analysis tasks, one typical method is to map 3D mesh data into 1D sequence data with random walk sampling. However, existing random walk-based appro...
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Article
UnifiedSC: a unified framework via collaborative optimization for multi-task person re-identification
Person re-identification (ReID) encompasses two independent study branches, i.e., single-modality and cross-modality identifications. Since single-modality and cross-modality pedestrian data have completely di...
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Chapter and Conference Paper
An Intelligent Image Segmentation Annotation Method Based on Segment Anything Model
Training of supervised neural network models requires a large amount of high-quality datasets with true values. In computer vision tasks such as object detection and image segmentation, the process of annotati...
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Article
A human activity recognition method using wearable sensors based on convtransformer model
Deep learning models have recently attracted great interest as an effective solution to the challenging problem of human activity recognition (HAR) and its widespread applications in medical rehabilitation and...
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Article
Hybrid feature constraint with clustering for unsupervised person re-identification
Unsupervised person re-identification (Re-ID) has better scalability and usability in real-world deployments due to the lack of annotations, which is more challenging than supervised methods. State-of-the-art ...
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Article
Open AccessNormal vibration distribution search-based differential evolution algorithm for multimodal biomedical image registration
In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preproc...
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Article
Attention deep residual networks for MR image analysis
Prostate diseases often occur in men. For further clinical treatment and diagnosis, we need to do accurate segmentation on prostate. There are already many methods that concentrate on solving the problem of au...
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Article
DRDDN: dense residual and dilated dehazing network
Recently, deep convolutional neural networks (CNNs) have made great achievements in image restoration. However, there exists a large space to improve the performance of CNN-based dehazing model. In this paper,...
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Chapter and Conference Paper
Unsupervised Anomaly Detection Method Based on DNS Log Data
In order to solve the problem of network attack by malicious code using Domain Name System (DNS), on the basis of analyzing the characteristics of malicious code lines and abnormal operation behaviors, this pa...
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Article
A novel privacy-preserving outsourcing computation scheme for Canny edge detection
With the advancement of cloud computing technology, cloud servers are utilized to process large-scale data, especially multimedia data. However, concerns about leakage of private information prevent cloud comp...
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Chapter and Conference Paper
A Semi-supervised Learning Based on Variational Autoencoder for Visual-Based Robot Localization
Robot localization, the task of determining the current pose of a robot, is a crucial problem of mobile robotic. Visual-based robot localization, which using only cameras as exteroceptive sensors, has become e...
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Chapter and Conference Paper
A Novel Construction Approach for Dehazing Dataset Based on Realistic Rendering Engine
Image dehazing is an important pre-processing for computer vision systems. Modern dehazing techniques are based deep learning and training data. Typical datasets are constructed with depth camera for indoor sc...
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Article
MLFS-CCDE: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution
Feature selection is a pre-processing procedure of choosing the optimal feature subsets for constructing model, yet it is difficult to satisfy the requirements of reducing number of features and maintaining cl...
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Article
A multi-phase blending method with incremental intensity for training detection networks
Object detection is an important topic for visual data processing in the visual computing area. Although a number of approaches have been studied, it still remains a challenge. There is a suitable way to promo...
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Article
Open AccessWeight asynchronous update: Improving the diversity of filters in a deep convolutional network
Deep convolutional networks have obtained remarkable achievements on various visual tasks due to their strong ability to learn a variety of features. A well-trained deep convolutional network can be compressed...
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Article
DRCDN: learning deep residual convolutional dehazing networks
Single image dehazing, which is the process of removing haze from a single input image, is an important task in computer vision. This task is extremely challenging because it is massively ill-posed. In this pa...
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Article
A dividing-based many-objective evolutionary algorithm for large-scale feature selection
Feature selection is a critical preprocess for constructing model in computer vision and machine learning, yet it is difficult to simultaneously satisfy both reducing features’ number and maintaining classific...
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Article
Part-based visual tracking with spatially regularized correlation filters
Discriminative Correlation Filters (DCFs) have demonstrated excellent performance in visual object tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifie...
