Yonghuai Liu

Edge Hill University · Computer Science

With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high cos...

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framewo...

While 3D visual saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and has been well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art 3D visual saliency methods remain poor at predicting human fixations. Cues emerging prominently from...

Background: Various types of noise artifacts inevitably exist in some medical imaging modalities due to limitations of imaging techniques, which impair either clinical diagnosis or subsequent analysis. Recently, deep learning approaches have been rapidly developed and applied on medical images for noise removal or image quality enhancement. Nevert...

Background Optical coherence tomography angiography (OCTA) enables fast and non-invasive high-resolution imaging of retinal microvasculature and is suggested as a potential tool in the early detection of retinal microvascular changes in Alzheimer’s Disease (AD). We developed a standardised OCTA analysis framework and compared their extracted parame...

Pixel-level Artery/Vein (A/V) classification of retinal blood vessels is important in diagnosing and understanding a wide spectrum of diseases. Deep learning-based methods have achieved promising results, but manual image annotation is a labor-intensive and time-consuming process and annotations between different datasets cannot be fully utilized....

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) sin...

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) sin...

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint seg...

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint seg...

We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN instantiating it for salient view selection and interest point d...

This paper proposes a novel method for the detection of the symmetrical axis of the cropped face required for the aesthetic outcome estimation from the facial images of patients after their cleft treatment. It firstly applies the Gaussian filter to smooth the images in order to compress noise on the subsequent tasks, then the bilateral semantic seg...

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity....

Parapneumonic effusion (PPE) is a common condition that causes death in patients hospitalized with pneumonia. Rapid distinction of complicated PPE (CPPE) from uncomplicated PPE (UPPE) in Computed Tomography (CT) scans is of great importance for the management and medical treatment of PPE. However, UPPE and CPPE display similar appearances in CT sca...

Current methods of assessing the quality of a surgically repaired cleft lip rely on humans scoring photographs. This is only practical for research purposes due to the resources necessary and is not used in routine audit. It has poor validity due to human subjectivity and thus low inter-rater reliability. An automatic method for aesthetic outcome a...

This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end...

Appearance scanning; BRDF measurement; Reflectometry

Optical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in clinic provide high-quality 2D en face representations, while their 3D data quality are largely limited by low signal-to-noise ratio and strong...

The development of medical imaging techniques has greatly supported clinical decision making. However, poor imaging quality, such as non-uniform illumination or imbalanced intensity, brings challenges for automated screening, analysis and diagnosis of diseases. Previously, bi-directional GANs (e.g., CycleGAN), have been proposed to improve the qual...

While point clouds hold promise for measuring the geometrical features of 3D objects, their application to plants remains problematic. Plants are three dimensional (3D) organisms whose morphology is complex, varies from one individual to another and changes over time. Objective measurement of attributes in 3D point cloud domain is increasingly attr...

The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to...

This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end...

There is significant progress in recognizing traditional human activities from videos focusing on highly distinctive actions involving discriminative body movements, body-object and/or human-human interactions. Driver's activities are different since they are executed by the same subject with similar body parts movements, resulting in subtle change...

Local feature description is to assign a unique signature to a key-point such that it becomes distinctive from the others regardless of changes in viewpoint, illumination, rotation, scale as well as distortions and noise. This paper proposes a novel approach to construct such a descriptor. For preserving both homogeneous and heterogeneous features...

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding th...

Scale-invariance, good localization and robustness to noise and distortions are the main properties that a local feature detector should possess. Most existing local feature detectors find excessive unstable feature points that increase the number of keypoints to be matched and the computational time of the matching step. In this paper, we show tha...

We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN for the salient view selection of 3D objects, which quintessentially c...

Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in l...

Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h...

In the above article [1] , there were two errors in the printed article that the authors want to correct.

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding th...

The non-uniform illumination or imbalanced intensity in medical images brings challenges for automated screening, examination and diagnosis of diseases. Previously, CycleGAN was proposed to transform input images into enhanced ones without paired images. However, it did not consider many local details of the structures, which are essential for medi...

Automated classification of retinal artery (A) and vein (V) is of great importance for the management of eye diseases and systemic diseases. Traditional colour fundus images usually provide a large field of view of the retina in color, but often fail to capture the finer vessels and capillaries. In contrast, the new Optical Coherence Tomography Ang...

The non-uniform illumination or imbalanced intensity in medical images brings challenges for automated screening, examination and diagnosis of diseases. Previously, CycleGAN was proposed to transform input images into enhanced ones without paired images. However, it did not consider many local details of the structures, which are essential for medi...

In the real world, out-of-distribution samples, noise and distortions exist in test data. Existing deep networks developed for point cloud data analysis are prone to overfitting and a partial change in test data leads to unpredictable behaviour of the networks. In this paper, we propose a smart yet simple deep network for analysis of 3D models usin...

In the real world, out-of-distribution samples, noise and distortions exist in test data. Existing deep networks developed for point cloud data analysis are prone to overfitting and a partial change in test data leads to unpredictable behaviour of the networks. In this paper, we propose a smart yet simple deep network for analysis of 3D models usin...

Precise characterization and analysis of corneal nerve fiber tortuosity are of great importance in facilitating examination and diagnosis of many eye-related diseases. In this paper we propose a fully automated method for image-level tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classifi...

Feature extraction plays a vital role in visual action recognition. Many existing gradient-based feature extractors, including histogram of oriented gradients (HOG), histogram of optical flow (HOF), motion boundary histograms (MBH), and histogram of motion gradients (HMG), build histograms for representing different actions over the spatio-temporal...

Background and objective: The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion det...

Recently, effort has been made to apply deep learning to the detection of mesh saliency. However, one major barrier is to collect a large amount of vertex-level annotation as saliency ground truth for training the neural networks. Quite a few pilot studies showed that this task is quite difficult. In this work, we solve this problem by developing a...

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. How...

In this paper, we propose a compressive sensing-based method to pan-sharpen the low-resolution multispectral (LRM) data, with the help of high-resolution panchromatic (HRP) data. In order to successfully implement the compressive sensing theory in pan-sharpening, two requirements should be satisfied: (i) forming a comprehensive dictionary in which...

This article gives an overview of range‐imaging techniques with an aim to let the reader better understand how the difficult issue, such as the registration of overlapping range images, can be approached and solved. It firstly introduces the characteristics of range images and highlights examples of 3D image visualizations, associated technical iss...

Automatic video summarization aims to provide brief representation of videos. Its evaluation is quite challenging, usually relying on comparison with user summaries. This study views it in a different perspective in terms of verifying the consistency of user summaries, as the outcome of video summarization is usually judged based on them. We focus...

Video summarization aims to create a succinct representation of videos for efficient browsing and retrieval. We propose an innovative method for the task. It includes two main steps: (i) the first step proposes a Distinct Frame Patch (DFP) index for selecting a set of good candidate frames, and (ii) the second step proposes a novel Appearance based...

Data integration techniques provide a communication bridge between isolated sources and offer a platform for information exchange. When the schemas of heterogeneous data sources map to the centralized schema in a mediated data integration system or a source schema maps to a target schema in a peer-to-peer system, multiple schema mappings may exist...

Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesi...

The classification of the retinal vascular tree into arteries and veins is important in understanding the relation between vascular changes and a wide spectrum of diseases. In this paper, we have proposed a novel framework that is capable of making the artery/vein (A/V) distinction in retinal color fundus images. We have successfully adapted the co...

As a measure of regional importance in agreement with human perception of 3D shape, mesh saliency should be based on local geometric information within a mesh but more than that. Recent research has shown that global consideration has a significant role in mesh saliency. This paper proposes a local-to-global framework for computing mesh saliency wh...

Entity Linking (EL) is the task of resolving mentions to referential entities in a knowledge base, which facilitates applications such as information retrieval, question answering, and knowledge base population. In this paper, we propose a novel embedding method specifically designed for EL. The proposed model jointly learns word and entity embeddi...

Text representations is a key task for many natural language processing applications such as document classification, ranking, sentimental analysis and so on. The goal of it is to numerically represent the unstructured text documents so that they can be computed mathematically. Most of the existing methods leverage the power of deep learning to pro...

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the...

Camera calibration and image correction is an important step for most of the computer vision tasks, like 3D metric measurement and multi-view stereo. In this paper, we propose a novel algorithm for camera calibration and image correction. It considers the inaccuracy of 3D pattern (like chessboard) and the lens distortion effect on the feature point...

Video summarization is useful to find a concise representation of the original video, nevertheless its evaluation is somewhat challenging. This paper proposes a simple and efficient method for precisely evaluating the video summaries produced by the existing techniques. This method includes two steps. The first step is to establish a set of matched...

Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The pro...

Computer aided diagnosis systems (CADx) play a major role in the early diagnosis of breast cancer. Extracting the breast region precisely from a mammogram is an essential component of CADx for mammography. The appearance of the pectoral muscle on medio-lateral oblique (MLO) views increases the false positive rate in CADx. Therefore, the pectoral mu...

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopa-thy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is...

Video summarization aims to manage video data by providing succinct representation of videos, however its evaluation is somewhat challenging. IMage Euclidean Distance (IMED) has been proposed for the measurement of the similarity of two images. Though it is effective and can tolerate the distortion and/or small movement of the objects, its computat...

Video summarization is useful to find a concise representation of the original video, nevertheless its evaluation is somewhat challenging. This paper proposes a simple and efficient method for precisely evaluating the video summaries produced by the existing techniques. This method includes two steps. The first step is to establish a set of matched...

Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, and object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. Firstly, two convex hulls are extracted from the source and target respectively...

Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is...

Feature extraction and matching provide the basis of many methods for object registration, modeling, retrieval, and recognition. However, this approach typically introduces false matches, due to lack of features, noise, occlusion, and cluttered backgrounds. In registration, these false matches lead to inaccurate estimation of the underlying transfo...

Feature extraction and matching has been widely used for the registration of overlapping partial shapes. However, the established tentative point matches (TPMs) usually include a large proportion of false ones due to a number of factors: imaging noise, occlusion, repetitive structures, and cluttered background. To apply the rigidity constraint to r...

The detection of salient regions is an important pre-processing step for many 3D shape analysis and understanding tasks. This paper proposes a novel method for saliency detection in 3D free form shapes. Firstly, we smooth the surface normals by a bilateral filter. Such a method is capable of smoothing the surfaces and retaining the local details. S...