Q. M. Jonathan Wu

University of Windsor · Department of Electrical and Computer Engineering

We are pleased to inform that we are organising 2nd International Workshop on Big Data in Healthcare in conjunction with IEEE MIPR 2023 going to held during August 30- Sepetember 1, 2023 at Singapore. The intent of this workshop is to bring together researchers, practitioners, and scientific communities to report and discuss the common challenges,...

Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is d...

The high-frequency surface wave radar (HFSWR) detects ship targets in the exclusive economic zone (EEZ) effectively. Most of the existing ship target detection algorithms for HFSWR process the spatial domain features. However, the ship target is usually concealed and interfered with various clutters and background noise in the Doppler spectrum. In...

Human action recognition (HAR) is one of most important tasks in video analysis. Since video clips distributed on networks are usually untrimmed, it is required to accurately segment a given untrimmed video into a set of action segments for HAR. As an unsupervised temporal segmentation technology, subspace clustering learns the codes from each vide...

Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations among the pixels of each manip...

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integr...

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in compl...

Defects on rail surfaces, which have become critical problems, need to be detected and removed as quickly as possible to ensure the fast, safe, and stable operation of trains. At present, although many solutions have been proposed to address these problems, the comprehensiveness, rapidity, and accuracy of defect detection remain unsatisfactory. Thi...

Semisupervised classification with a few labeled training samples is a challenging task in the area of data mining. Moore-Penrose inverse (MPI)-based manifold regularization (MR) is a widely used technique in tackling semisupervised classification. However, most of the existing MPI-based MR algorithms can only generate loosely connected feature enc...

Automatic fingerprint identification system (AFIS) uses fingerprint to authenticate users, which is legal if the user is enrolled. However, numerous studies reveal that it is susceptible to spoofing attacks where a third person might freely synthesize counterfeit fingerprints to trick the scanner. To resist spoofing attacks, it makes fingerprint li...

Biometrics spoofing attack (BsSA) frequently occurs when an adversary impersonates a lawful user to access to the biometric system by means of some forged or synthetic samples, especially in fingerprint or face authentication. In allusion to the problem above, the mainstream countermeasure, called biometrics liveness detection (BLD), is raised. In...

The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To...

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. T...

The echo of shipborne high-frequency surface wave radar (HFSWR) is modulated by six-degrees-of-freedom (6-DOF) motion, affecting the detection of the target and the remote sensing of ocean surface dynamics parameters. Commonly, motion compensation methods of shipborne HFSWR describe each aspect of the 6-DOF motion as the superposition of sinusoidal...

The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images to locate the manipulated regions. However, these approaches ignore the feature correlations, i.e.,...

In recent years, deep learning has been successfully applied to image super-resolution. It is still a challenge to reconstruct high-frequency details from low-resolution images. However, many works lack attention to the high-frequency part. We find that edge prior information can be used to extract high-frequency parts and applying soft edges to im...

Text steganography has received a lot of attention in the application of covert communication. How to ensure desirable capacity and imperceptibility has become a key issue in text steganography. There are two typical approaches, i.e., text-selection-based steganography and text-generation-based steganography. However, the text-selection-based appro...

Computer-aided diagnosis based on deep learning is progressively deployed for the analysis of medical images, yet the poor robustness and generalization of the model poses a challenge for clinical application. In addition, the lack of large amount of training data aggravates this problem. To mitigate this issue, we investigate and research from...

Twin delayed deep deterministic (TD3) policy gradient is an effective algorithm for continuous action spaces. However, it cannot efficiently explore the spatial space and suffers from slow convergence, which is mainly due to the serial mode strategy in learning policies. On the other hand, asynchronous reinforcement learning algorithms, e.g., async...

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain with a correlative distribution. Numerous existing approaches process this hard nut by directly matching the marginal distribution between two domains, which confront the obstacle of rough alignment and blurred decision bo...

Steganography is an essential way to ensure secure communication in networks. Most steganographic algorithms imperceptibly embed secret information into an existing cover image. However, they generally cannot find a good trade-off between embedding capacity and security, as the existing covers available for users are usually far from optimal for em...

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the...

Artificial neural network training algorithms aim to optimize the network parameters regarding the pre-defined cost function. Gradient-based artificial neural network training algorithms support iterative learning and have gained immense popularity for training different artificial neural networks end-to-end. However, training through gradient meth...

Pattern recognition is significantly challenging in real-world scenarios by the variability of visual statistics. Therefore, most existing algorithms relying on the independent identically distributed assumption of training and test data suffer from the poor generalization capability of inference on unseen testing datasets. Although numerous studie...

In recent years, image super-resolution (SR) based on deep learning technology has made significant progress. However, most methods are difficult to apply in real life because of their large parameters and heavy computation. Recently, residual learning has been widely applied to the problem of super-resolution. It can make the shallow features extr...

At present, many studies have shown that partitioning the gait sequence and its feature map can improve the accuracy of gait recognition. However, most models just cut the feature map at a fixed single scale, which loses the dependence between various parts. So, our paper proposes a structure called Part Feature Relationship Extractor (PFRE) to dis...

Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above pro...

Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation d...

It is a typical problem in the field of object detection to simultaneously detect objects with large scale variation in one image. Recently proposed state-of-the-art object detectors generally learn pyramidal feature representation to deal with the scale variation, which has been proved effective via various feature pyramid networks. However, the m...

With the rapid development of E-commerce, more and more people are used to shopping online, in which the reputation scores of sellers play an important role in helping consumers purchase satisfactory products. However, in the existing E-commerce environments, the reputation scores of users (including sellers and buyers) are centrally computed and s...

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result,...

High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime surveillance because of its low-cost maintenance and coverage of wide area. However, when it comes to the extraction of key areas, such as vessel-target detection and vessel-path tracking, the HFSWR signal is strongly interfered by clutters and noise, which makes marit...

High-frequency surface wave radar (HFSWR) is of great significance for maritime detection, but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and interference, making it difficult to detect vessels. In this paper, we propose an intelligent detection algorithm for targets concealed in strong clutter and complex in...

High-frequency surface wave radar (HFSWR) can be effectively used to detect ships in the exclusive economic zone. However, the ship signal is concealed and interfered with various clutter and background noise in the Doppler spectrum. In this letter, a range-Doppler (RD) image-based novel ship detection algorithm is proposed by exploiting spatial-fr...

The pose estimation is the critical technology in industrial robot. Nowadays, many machine vision-based approaches have applied the technology and achieved excellent results. However, the rapid detection of the pose estimation in complex multiscene environments is still a challenge, due to the interference of multiangle light and multibackground. T...

An echo state network (ESN) can provide an efficient dynamic solution for predicting time series problems. However, in most cases, ESN models are applied for predictions rather than classifications. The applications of ESN in time series classification (TSC) problems have yet to be fully studied. Moreover, the conventional randomly generated ESN is...

Camera model identification (CMI) has gained significant importance in image forensics as digitally altered images are becoming increasingly commonplace. In this paper, a novel convolutional neural network (CNN) architecture is proposed for CMI with emphasis given on the preprocessing task considered to be inevitable for removing the scene content...

High-frequency surface wave radar (HFSWR) plays an important role in vessel target surveillance. However, HFSWR's inaccuracy of azimuth estimation caused by wide beams severely limits its detection ability. To solve this problem, a novel direction of arrival (DOA) estimation method based on extreme learning machine optimized by particle swarm optim...

Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to ex...

高频地波雷达 (High-frequency surface wave radar, HFSWR) 在超视距舰船目标检测跟踪中有广泛应用. 然而, HFSWR 工作频段的电磁环境十分复杂, 舰船目标信号往往被淹没在各种噪声中. 本文提出一种基于最优误差自校正极限学习机 (Optimized error self-adjustment extreme learning machine, OES-ELM) 的 HFSWR 海面目标识别算法. 该算法利用二级级联分类策略, 可以显著提高目标的检测效率. 首先利用灰度特征和线性分类器快速找出目标的潜在区域. 然后利用 Haar-like 特征和 OES-ELM 分类器进一步辨识目标和海杂波. 在 OES-ELM 中, 首先利用L1/2正则算子裁剪隐层...

Salient Object Detection aims to detect the most visually distinctive objects in an image. We solve this problem by introducing the average pool to explore the multi-level deep average pool convolution features different from the max pool information. Based on the U-net structure, we propose an Average-and Max-Pool Network (AMPNet) that leverages t...

Face sketch synthesis, as a key technique for solving face sketch recognition, has made considerable progress in recent years. Due to the difference of modality between face photo and face sketch, traditional exemplar-based methods often lead to missed texture details and deformation while synthesizing sketches. And limited to the local receptive f...

Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses...

While fingerprint identification systems have been widely applied to daily life, how to protect them from presentation attacks has become a hot topic in the field of biometric verification. A feasible strategy of fingerprint recognition, called Fingerprint Liveness Detection (FLD), has attracted a lot of attention from researchers. Convolutional Ne...

Visual texture classification plays a critical role in computer vision and pattern recognition. As one of the most popular texture descriptors, local binary pattern(LBP) has achieved extensive development and applications due to its simplicity and high efficiency. However, it is hard for most LBP-based methods to represent completed local texture i...

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavy-tailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter, the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized b...

Nowadays, semantic segmentation methods for systems in road scene have a great demand. Most existing methods focus on high accuracy with low inference speed. And some approaches emphasize on speed, significantly sacrificing model accuracy. To make a trade-off between accuracy and inference speed, we propose a real-time network for semantic segmenta...

The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that consists of multichannel deep autoencoders with subnetwork neurons (MCDAE $_{sn}$ ). After we define tw...

With the increasing popularity of various deep neural networks in the area of computational intelligence, the research attention for content-based image detection/retrieval has been shifted from the handcrafted local features such as scale invariant feature transform (SIFT) to the features derived from convolutional neural networks (CNN). However,...

The stochastic gradient descent (SGD) method plays a central role in training deep convolutional neural networks (DCNNs). The recent advances in the field of optimization methods for DCNNs follow the direction of gradients. The innovations mainly lie in adopting different techniques to manage the history of gradients or automatically adapt the step...

For onshore high-frequency surface-wave radar (HFSWR), target parameter estimation focuses mainly on distance and velocity demodulation, then on azimuth determination. However, these parameters are difficult to solve accurately for shipborne HFSWR targets due to additional modulation on the echo signal introduced by the forward and six-degree-of-fr...

The coronavirus disease, also known as the COVID-19, is an ongoing pandemic of a severe acute respiratory syndrome. The pandemic has led to the cancellation of many religious, political, and cultural events around the world. A huge number of people have been stuck within their homes because of unprecedented lockdown measures taken globally. This pa...

To fundamentally resist the steganalysis, coverless information hiding has been proposed, and it has become a research hotspot in the field of covert communication. However, the current methods not only require a huge image database, but also have a very low hidden capacity, making it difficult to apply practically. In order to solve the above prob...

Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate mean...

Recently, fingerprint recognition systems are widely deployed in our daily life. However, spoofing via using special materials such as silica, gelatin, Play-Doh, clay, etc., is one of the most common methods of attacking fingerprint recognition systems. To handle the above defects, a fingerprint liveness detection (FLD) technique is proposed. In th...

The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student’s $t$ -distribution and the approximate $t$ -filter. The performance of the...

Most recent large-scale image search approaches build on a bag-of-visual-words model, in which local features are quantized and then efficiently matched between images. However, the limited discriminability of local features and the BOW quantization errors cause a lot of mismatches between images, which limit search accuracy. To improve the accurac...

IET Image Processing Special Issue on "Recent Trends in Multimedia Analytics and Security" ************************************************************************ URL: https://digital-library.theiet.org/files/IET_IPR_CFP_RTMAS.pdf Recently information and communication technologies have experienced colossal growth, of which multimedia has been o...

For pedestrian detection, many deep learning approaches have shown effectiveness, but they are not accurate enough for the positioning of obstructed pedestrians. A novel segmentation and context network (SCN) structure is proposed that combines the segmentation and context information for improving the accuracy of bounding box regression for pedest...

The face super-resolution method is used for generating high-resolution images from low-resolution ones for better visualization. The super-resolution generative adversarial network (SRGAN) can generate a single super-resolution image with realistic textures, which is a groundbreaking work. Based on SRGAN, we proposed improved face super-resolution...

Inspired by the phenomenon that the decoding weights of a well-trained autoencoder contain the information of the training samples, we proposed a data augmentation method by utilizing the decoding weights. Given a batch of training data, the autoencoder is trained and the decoding weights are activated; the decoding weights are then combined with t...

This paper presents a new supervised multilayer subnetwork-based feature refinement and classification model for representation learning. The novelties of this algorithm are as follows: 1) Different from most multi-layer networks that go deeper with increased number of network layers, this work architects a model with wider subnetwork nodes. 2) The...

We are pleased to inform that we are organising International Workshop on Big Data in Healthcare in conjunction with IEEE BigMM 2020 going to held during September 24-26, 2020 New Delhi. The intent of this workshop is to bring together researchers, practitioners, and scientific communities to report and discuss the common challenges, advancements...

The quality detection of pharmaceutical liquid products is inevitable and crucial in drug manufacture because drugs contaminated with foreign particles are definitely not to be used. However, with the current detection methods, it is still a challenge to detect and identify the small moving particles using an imaging system. In this article, a deep...

Student's t distribution is a useful tool that can model heavy-tailed noises appearing in many practical systems. Although t distribution based filter has been derived, the information filter form is not presented and the data fusion algorithms for dynamic systems disturbed by heavy-tailed noises are rarely concerned. In this paper, based on multiv...

Over the year, visual texture analysis has come to be recognized as one of the most important methods in the area of medical image analysis and understanding, face description and detection, and so on. The goal of texture descriptors is to capture the general characteristic of textures such as dependency as well as invariance properties. Among all...

Various reports have shown that the rate of road traffic accidents has increased due to reduced driver vigilance. Therefore, an accurate estimation of the driver's alertness status plays an important part. To estimate vigilance, we adopt a novel strategy that is a deep autoencoder with subnetwork nodes (DAE <sub xmlns:mml="http://www.w3.org/1998/Ma...

Recently, fingerprint recognition systems are widely deployed in our daily life. However, spoofing via using special materials such as silica, gelatin, Play-Doh, clay, etc., is one of the most common methods of attacking fingerprint recognition systems. In order to solve these defects, fingerprint liveness detection (FLD) technique has been raised....

The popularity of biometric authentication technology benefits from the rapid development of smart mobile devices in recent years, and fingerprints, which are inherent human traits and neither easily revealed nor deciphered, can be used for real-time individual authentication systems. However, the main security issue of real-time fingerprint authen...

Echo State Network (ESN) is a specific class of recurrent neural networks, which displays very rich dynamics owing to its reservoir based hidden neurons. ESN has been viewed as a powerful approach to model real-valued time series processes. In order to integrate with deep learning theory, Deep Belief Echo State Network (DBESN) is employed to addres...