Weiqing Yan

• Tianjin University

Multiview data, characterized by rich features, are crucial in many machine learning applications. However, effectively extracting intraview features and integrating interview information present significant challenges in multiview learning (MVL). Traditional deep network-based approaches often involve learning multiple layers to derive latent. In...

Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2...

Graph neural networks (GNNs) rely heavily on graph structures and artificial hyperparameters, which may increase computation and affect performance. Most GNNs use original graphs, but the original graph data has problems with noise and incomplete information, which easily leads to poor GNN performance. For this kind of problem, recent graph structu...

Owing to the development of convolutional neural networks (CNNs), the detection of defects on rail surfaces has significantly improved. Although existing methods achieve good results, they incur huge computational and parameter costs associated with CNNs. The usual approach to this problem is to design lightweight models that meet the needs of real...

Semantic segmentation is crucial for a wide range of downstream applications in remote sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The dramatic variations in grayscale and the stacking of categories within RSIs lead to unstable inter-class variance and exacerbate the uncertainty around category boundari...

Multi-view graph clustering can divide similar objects into the same category through learning the relationship among samples. To improve clustering efficiency, instead of all sample-based graph learning, the bipartite graph learning method can achieve efficient clustering by establishing the graph between data points and a few anchors, so it becom...

Facial retouching, aiming at enhancing an individual’s appearance digitally, has become popular in many parts of human life, such as personal entertainment, commercial advertising, etc. However, excessive use of facial retouching can affect public aesthetic values and accordingly induce issues of mental health. There is a growing need for comprehen...

Video inpainting has been extensively used in recent years. Established works usually utilise the similarity between the missing region and its surrounding features to inpaint in the visually damaged content in a multi-stage manner. However, due to the complexity of the video content, it may result in the destruction of structural information of ob...

Detecting 3D pedestrian from point cloud data in real-time while accounting for scale is crucial in various robotic and autonomous driving applications. Currently, the most successful methods for 3D object detection rely on voxelbased techniques, but these tend to be computationally inefficient for deployment in aerial scenarios. Conversely, the pi...

In recent years, colorectal polyp segmentation has attracted increasing attention in academia and industry. Although most existing methods can achieve commendable outcomes, they often confront difficulty when localizing challenging polyps with complex background, variable shape/size, and ambiguous boundary, because of the limitations in modeling gl...

Recently, many deep neural network-based methods have been proposed for polyp segmentation. Nevertheless, most methods primarily analyze spatial information and usually fail to accurately localize polyps with inconsistent sizes, irregular shapes, and blurry boundaries. In this paper, we propose a Dual-domain Feature Interaction Network (DFINet) for...

Crowd density estimation is a practical application task in which speed efficiency is as crucial as the accuracy of the results. Hence, we propose the hybrid knowledge distillation network (HKDNet) for RGB-thermal (RGB-T) crowd density estimation to address the limitations of computational cost and training time from the perspective of ensuring acc...

Scene parsing of high-resolution remote sensing images with complex backgrounds has received extensive attention in recent years. As unimodal networks are significantly affected by weather conditions, reflecting complex ground conditions fully and accurately is difficult; therefore, multimodal scene analysis is particularly important. Current multi...

In recent years, Incomplete Multi-View Clustering (IMVC) has become an important and challenging task. Although several methods have been proposed to address IMVC, they still have the following drawbacks: i) Due to the presence of missing samples in the views, clustering prototypes obtained from different views may have positional deviations, leadi...

The detection of black and odorous water using remote sensing technology has become an effective method. The high-resolution remote sensing images can extract target features better than low-resolution images. However, the high-resolution images often introduce complex background details and intricate textures, which often have problems with accura...

3D object detection is a critical task in the fields of virtual reality and autonomous driving. Given that each sensor has its own strengths and limitations, multi-sensor-based 3D object detection has gained popularity. However, most existing methods extract high-level image semantic features and fuse them with point cloud features, focusing solely...

Pedestrian re-identification (re-ID) has gained considerable attention as a challenging research area in smart cities. Its applications span diverse domains, including intelligent transportation, public security, new retail, and the integration of face re-ID technology. The rapid progress in deep learning techniques, coupled with the availability o...

Currently, various deep learning methods have been developed to address the image enhancement tasks based on paired high-quality images as references. For the low-light endoscopic image enhancement task, it is difficult to obtain paired high-quality images and to extract features from dark areas. In addition, the enhanced images easily appear color...

Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way,...

In recent years, there has been significant progress in polyp segmentation in white-light imaging (WLI) colonoscopy images, particularly with methods based on deep learning (DL). However, little attention has been paid to the reliability of these methods in narrow-band imaging (NBI) data. NBI improves visibility of blood vessels and helps physician...

Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance. Nevertheless, these methods are hindered in their performance by the fact that they treat all spectral bands and spatial information equally in the un...

Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult...

Remote sensing image segmentation plays an important role in many industrial-grade image processing applications. However, the problem of uncertainty caused by intraclass heterogeneity and interclass blurring is prevalent in high-resolution remote sensing images. Moreover, the complexity of information in high-resolution remote sensing images leads...

Camouflaged object detection (COD) is an important yet challenging task, with great application values in industrial defect detection, medical care, etc. The challenges mainly come from the high intrinsic similarities between target objects and background. In this paper, inspired by the biological studies that object detection consists of two steps...

Nowadays, it is a common practice to retouch face images before sharing them on websites, social media, and even identification cards. In response, increased criticisms have appeared about taking photo retouching to an extreme. This naturally leads to the necessity of designing perceptual quality assessment methods that can measure how much a retou...

3D object detection is a crucial and complex undertaking in the realm of 3D scene comprehension. Monocular-based 3D detectors, in comparison to LiDAR 3D detectors that utilize point clouds as input, often exhibit a significant performance gap. Incorporating guidance from LiDAR-based detectors has led to notable advancements in monocular 3D detectio...

Surveillance video has been widely used in business, security, search, and other fields. Identifying and locating specific pedestrians in surveillance video has an important application value in criminal investigation, search and rescue, etc. However, the requirements for real-time capturing and accuracy are high for these applications. It is essen...

Multiview clustering, which partitions data into different groups, has attracted wide attention. With increasing data, bipartite graph-based multiview clustering has become an important topic since it can achieve efficient clustering by establishing relationship between data points and anchor points instead of all samples. Current most methods lear...

At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirement...

Semantic segmentation of high-resolution remote sensing images plays an important role in the remote sensing community. However, many indistinguishable objects are prevalent within urban remote sensing images, and some objects belonging to the same class are different and many objects that do not belong to the same class are similar. These tricky o...

Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation but ignore the data imbalance issue, limiting their flexibility and accuracy across multiple scenario...

Most existing image dehazing methods rely on the solution of the atmospheric scattering model or supervised learning based on paired images. However, owing to incomplete prior knowledge and the lack of paired hazy and haze‐free images of the same scenes as training samples, their performances for single image dehazing are unsatisfactory. Here, the...

Recently, network representation learning has been widely used to mine and analyze network characteristics, and it is also applied to blockchain, but most of the embedding methods in blockchain ignore the heterogeneity of network, so it is difficult to accurately describe the characteristics of the transaction. As smart society evolves, Ethereum ma...

Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized vie...

Large view stereoscopic images can provide users with immersive depth experience. Image stitching techniques aim to obtain large view stitched images, and there have been various image stitching algorithms proposed recently. However, there is still no effective objective quality assessment for stereoscopic stitched images. In this paper, we propose...

Panoramic images can provide users with large view scene, which is widely used in various fields. Image stitching can combine images of adjacent views with small horizon field into a single image with large horizon. Currently stitching method can provide a rectangle panorama by cropped method to view or print. However, this method can occur shape d...

Landcover classifications have large uncertainty related to the heterogeneity of similar objects and complex spatial correlations in satellite images, making it difficult to obtain ideal classification results using traditional classification methods. Therefore, to address the uncertainty in landcover classifications based on remotely sensed inform...

Multi-view clustering aims to group data points into their classes. Exploiting the complementary information underlying multiple views to benefit the clustering performance is one of the topics of multi-view clustering. Most of existing multi-view clustering methods only constrain diversity and consistency in the data space, but not consider the di...

In this paper, we propose a novel shape-optimizing mesh warping method for stereoscopic panorama stitching, which aims to resolve shape distortion and unnatural rotation of traditional stitching methods, simultaneously coping with the challenges, misalignment, and stereoscopic inconsistency. Specifically, based on the grid mesh analysis of projecti...

Remotely sensed imagery classification have a large amount of uncertainty related to the intraclass heterogeneity and the interclass ambiguity of objects. Fuzzy set theory can address the uncertainty effectively, while interval-valued model can improve the separability of samples. Therefore, we propose a novel interval-valued fuzzy c-means algorith...

Nowadays, the standard dynamic range (SDR) image acquired at a fixed exposure exposes weakness in portraying fine-grained details of real scenes. The high dynamic range (HDR) image and other types of SDR images generated by multi-exposure fusion techniques provide us new choices for scene representation. To display on SDR screens, an HDR image must...

In this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the non-circularity of the sources, an extended signal is generated by concatenating the received array data and its conjug...

Image stitching can provide a large view image. Currently stitching method can provide a rectangle panorama by cropped method to view or print. However, this method can occur shape distortion, and information loss. In this paper, we propose a novel shape-optimization method based on feature selection for rectangle panorama. First, to avoid local di...

Unknown mutual coupling effect can degrade the performance of a direction of arrival (DOA) estimation method. In this letter, a new method is proposed for uniform linear arrays (ULAs) to tackle this problem. Considering the sparse representation exploiting the inherent structure of the received data, the effective block sparse representation and th...

In this paper, a novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm for the mixed circular and strictly noncircular sources is proposed. A general array model with a mixture of signals is firstly built based on uniform rectangular arrays (URAs), and then, the approach, which uses the rank-reduction-based ROOT-MUSIC, can solv...

In this paper, we present an effective disparity mapping method for binocular stereoscopic image. It is inspired by the observation that its displayed depth would change, when a stereoscopic image is displayed on different size screens. The phenomenon may bring an uncomfortable experience for viewers. To make a comfortable stereoscopic image for vi...

Traditional image editing techniques cannot be directly used to process stereoscopic media, as extra constraints are required to ensure consistent changes between left and right images. In this paper, we propose a hybrid warping model for stereoscopic image stitching by combining projective and contentpreserving warping. Firstly, a uniform homograp...

The paper proposes a content- and disparity-adaptive stereoscopic image retargeting. To simultaneously avoid the saliency content and disparity distortion, firstly, we calculate the image saliency region distortion difference, and conclude the factors causing visual distortion. Then, the proposed method via a convex quadratic programming can simult...

Two-dimensional (2-D) direction-of-arrival (DOA) estimation method using three-parallel uniform linear arrays (ULAs) is proposed in this letter. The 2-D DOA estimation problem is addressed by making full use of elements of the three-parallel ULAs. Furthermore, the proposed algorithm has better angle estimation performance in practical mobile elevat...

In this paper, a new high-resolution approach called fourth-order cumulants-based Toeplitz matrices reconstruction (FOC-TMR) method, is presented for two-dimensional (2-D) direction-of-arrival (DOA) estimation of incident narrowband coherent signals. The angle estimation problem is addressed by arranging the cumulants elements of received signals f...

DIBR is a promising technology for rendering new views of scenes from a collection of densely sampled images or videos. It has potential application in virtual reality, immersive, advanced visualization, and 3D television systems. However, due to imperfect depth maps and the illumination difference between reference images, annoying artifacts appea...

The target of stereo image quality assessment is to establish a computational model so as to predict the visual quality of a stereo image. A few new depth map methods have been proposed for the last few years. However, these methods are not suitable for all stereo images. Since the depth map fails to capture the directional reflection of human ster...

This paper proposes a new metric to evaluate and rank the relevance of words in a text. The method uses the Shannon's entropy difference between the intrinsic and extrinsic mode, which refers to the fact that relevant words significantly reflect the author's writing intention, i.e., their occurrences are modulated by the author's purpose, while the...