Wujie Zhou
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View article: Frequency-Aware Integrity Learning Network for Semantic Segmentation of Remote Sensing Images
Frequency-Aware Integrity Learning Network for Semantic Segmentation of Remote Sensing Images Open
The semantic segmentation of remote sensing images is crucial for computer perception tasks. Integrating dual-modal information enhances semantic understanding. However, existing segmentation methods often suffer from incomplete feature in…
View article: Wvcts-Net: Word Vector Convolutional Time Series Network Based on E-Nose for Origin Classification of Pickled Cabbage
Wvcts-Net: Word Vector Convolutional Time Series Network Based on E-Nose for Origin Classification of Pickled Cabbage Open
View article: Wvcts-Net: Word Vector Convolutional Time Series Network Based on E-Nose for Origin Classification of Pickled Cabbage
Wvcts-Net: Word Vector Convolutional Time Series Network Based on E-Nose for Origin Classification of Pickled Cabbage Open
View article: FASFLNet: feature adaptive selection and fusion lightweight network for RGB-D indoor scene parsing
FASFLNet: feature adaptive selection and fusion lightweight network for RGB-D indoor scene parsing Open
RGB-D indoor scene parsing is a challenging task in computer vision. Conventional scene-parsing approaches based on manual feature extraction have proved inadequate in this area because indoor scenes are both unordered and complex. This st…
View article: MISNet: Multiscale Cross-Layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images
MISNet: Multiscale Cross-Layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images Open
Although progress has been made in multisource data scene parsing of natural scene images, extracting complex backgrounds from aerial images of various types and presenting the image at different scales remain challenging. Various factors …
View article: Autoencoder-Like Knowledge Distillation Network for Anomaly Detection
Autoencoder-Like Knowledge Distillation Network for Anomaly Detection Open
Anomaly detection is a crucial research field in computer vision with diverse applications in practical scenarios. The common anomaly detection methods employed currently consist of autoencoders, generative adversarial networks, and knowle…
View article: DASFNet: Dense-Attention–Similarity-Fusion Network for scene classification of dual-modal remote-sensing images
DASFNet: Dense-Attention–Similarity-Fusion Network for scene classification of dual-modal remote-sensing images Open
Although significant progress has been made in scene classification of high-resolution remote-sensing images (HRRSIs), dual-modal HRRSI scene classification is still an active and challenging issue. In this study, we introduce an end-to-en…
View article: Deep-Separation Guided Progressive Reconstruction Network for Semantic Segmentation of Remote Sensing Images
Deep-Separation Guided Progressive Reconstruction Network for Semantic Segmentation of Remote Sensing Images Open
The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various s…
View article: Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing
Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing Open
RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level feat…
View article: Dmftnet: Dense Multimodal Fusion Transfer Network for Free-Space Detection
Dmftnet: Dense Multimodal Fusion Transfer Network for Free-Space Detection Open
View article: Mlanet: Multilevel Aggregation Network for Binocular Eye-Fixation Prediction
Mlanet: Multilevel Aggregation Network for Binocular Eye-Fixation Prediction Open
View article: Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing
Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing Open
RGB thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high level feat…
View article: Deep Multimodal Fusion Autoencoder for Saliency Prediction of RGB‐D Images
Deep Multimodal Fusion Autoencoder for Saliency Prediction of RGB‐D Images Open
In recent years, the prediction of salient regions in RGB‐D images has become a focus of research. Compared to its RGB counterpart, the saliency prediction of RGB‐D images is more challenging. In this study, we propose a novel deep multimo…
View article: Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency
Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency Open
Visual saliency prediction for RGB-D images is more challenging than that for their RGB counterparts. Additionally, very few investigations have been undertaken concerning RGB-D-saliency prediction. The proposed study presents a method bas…
View article: Hybrid-Attention Network for RGB-D Salient Object Detection
Hybrid-Attention Network for RGB-D Salient Object Detection Open
Depth information has been widely used to improve RGB-D salient object detection by extracting attention maps to determine the position information of objects in an image. However, non-salient objects may be close to the depth sensor and p…
View article: All-solid-state Potentiometric Biosensors Based on Electropolymerized Poly(3,4-ethylenedioxythiophene) as Solid Contact for Acetylcholine Determination in Artificial Cerebrospinal Fluid
All-solid-state Potentiometric Biosensors Based on Electropolymerized Poly(3,4-ethylenedioxythiophene) as Solid Contact for Acetylcholine Determination in Artificial Cerebrospinal Fluid Open
View article: TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples
TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples Open
Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is ge…
View article: Cross-Modal Feature Integration Network for Human Eye-Fixation Prediction in RGB-D Images
Cross-Modal Feature Integration Network for Human Eye-Fixation Prediction in RGB-D Images Open
With the advent of convolutional neural networks, research progress in visual saliency prediction has been impressive. While integrating features at different stages from the backbone network is important, feature extraction itself is equa…
View article: EMSGD: An Improved Learning Algorithm of Neural Networks With Imbalanced Data
EMSGD: An Improved Learning Algorithm of Neural Networks With Imbalanced Data Open
In this paper, the influence of data imbalance on neural networks is discussed, and an improved learning algorithm to solve this problem is proposed. The experimental results show that in the case of imbalanced data, the training error of …
View article: Attention-based fusion network for human eye-fixation prediction in 3D images
Attention-based fusion network for human eye-fixation prediction in 3D images Open
Human eye-fixation prediction in 3D images is important for many 3D applications, such as fine-grained 3D video object segmentation and intelligent bulletproof curtains. While the vast majority of existing 2D-based approaches cannot be app…
View article: Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks
Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks Open
Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples c…
View article: Learning to Measure Stereoscopic S3D Image Perceptual Quality on the Basis of Binocular Rivalry Response
Learning to Measure Stereoscopic S3D Image Perceptual Quality on the Basis of Binocular Rivalry Response Open
Blind perceptual quality measurement of stereoscopic 3D (S3D) images has become an important and challenging issue in the research field of S3D imaging. In this paper, a blind S3D image quality measurement (IQM) method that does not depend…
View article: Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network
Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network Open
In order to obtain the distances between the surrounding objects and the vehicle in the traffic scene in front of the vehicle, a monocular visual depth estimation method based on the depthwise separable convolutional neural network is prop…
View article: Nondestructive Early Detection of Bruising in Pear Fruit Using Optical Coherence Tomography
Nondestructive Early Detection of Bruising in Pear Fruit Using Optical Coherence Tomography Open
Pear fruit is susceptible to mechanical injury during harvesting, packaging, and transportation. Optical coherence tomography (OCT) can provide information concerning chemical and microstructural changes of fruit tissues. Therefore, using …
View article: Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting
Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting Open
With the rapid development of stereo image applications, there is an increasing demand to develop a versatile tool to evaluate the perceived quality of stereo images. Therefore, in this study, a blind stereo image quality evaluation (SIQE)…
View article: DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation
DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation Open
Indoor scene segmentation is a difficult task in computer vision. We propose an indoor scene segmentation framework, called DFMNet, incorporating RGB and complementary depth information to establish indoor scene segmentation. We use the sq…
View article: Correction: Corrigendum: Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images
Correction: Corrigendum: Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images Open
Scientific Reports 6: Article number: 33819; published online: 26 September 2016; updated: 23 February 2017 In the original version of this Article, all instances of “freshwater” were incorrectly given as “fleshwater”. This error has now b…
View article: Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images
Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images Open
Optical coherence tomography (OCT) has been applied to inspect the internal defect of beadless Chinese ZhuJi freshwater pearls. A novel fully automated algorithm is proposed to classify between normal and defective sub-layer in nacre layer…
View article: Simulating binocular vision for no-reference 3D visual quality measurement
Simulating binocular vision for no-reference 3D visual quality measurement Open
Perceptual quality measurement of three-dimensional (3D) visual signals has become a fundamental challenge in 3D imaging fields. This paper proposes a novel no-reference (NR) 3D visual quality measurement (VQM) metric that uses simulations…