Wee Hong Ong

Universiti Brunei Darussalam · Computer Science

p>Open-Set Recognition (OSR) has been emphasizing its capability to reject unknown classes and maintain closed-set performance simultaneously. The primary objective of OSR is to minimize the risk of unknown classes being predicted as one of the known classes. The OSR assumes that unknown classes are present during testing and identifies only one di...

Human activity discovery aims to cluster human activities without any prior knowledge of what defines each activity. However, most existing methods for human activity recognition are supervised, relying on labeled inputs for training. In reality, it is challenging to label human activity data due to its large volume and the diversity of human activ...

Classical map-based navigation methods are commonly used for robot navigation, but they often struggle in crowded environments due to the Frozen Robot Problem (FRP). Deep reinforcement learning-based methods address the FRP problem, however, suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method...

p>Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of t...

p>Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of t...

Despite many advances in Human Activity Recognition (HAR), most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian m...

A traditional deep neural network-based classifier assumes that only training classes appear during testing in closed-world settings. In most real-world applications, an open-set environment is more realistic than a conventional approach where unseen classes are potentially present during the model’s lifetime. Open-set recognition (OSR) provides th...

Smart city architecture brings all the underlying architectures, i.e., Internet of Things (IoT), Cyber-Physical Systems (CPSs), Internet of Cyber-Physical Things (IoCPT), and Internet of Everything (IoE), together to work as a system under its umbrella. The goal of smart city architecture is to come up with a solution that may integrate all the rea...

p>Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. Moreover, These methods cannot be used in real-time applications due to high calculations. In this paper, we provide a novel flexible multi-objective particle swarm optimization clustering method based on game theory (FMOPG)...

p>Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. Moreover, These methods cannot be used in real-time applications due to high calculations. In this paper, we provide a novel flexible multi-objective particle swarm optimization clustering method based on game theory (FMOPG)...

p>Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. In this paper, we provide a novel flexible multi-objective particle swarm optimization (PSO) clustering method based on game theory (FMOPG) to discover human activities fully unsupervised. Unlike conventional clustering meth...

Human activity recognition has been considered as the main capability of an intelligent system in understanding of human activities. Human activity recognition focuses on classifying activities with predefined models learned from labelled data based on supervised or semi-supervised approaches. These approaches have assumed the availability of abund...

Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. In this paper, we provide a novel flexible multi-objective particle swarm optimization (PSO) clustering method based on game theory (FMOPG) to discover human activities fully unsupervised. Unlike conventional clustering method...

The reuse of the pre-trained deep neural network models has been found successful in improving the classification accuracy for the plant species identification task. However, most of these models have a large number of parameters, and layers and take more storage space which makes them difficult to deploy on embedded or mobile devices for real-time...

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutatio...

Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. In this paper, we provide a novel flexible multi-objective particle swarm optimization (PSO) clustering method based on game theory (FMOPG) to discover human activities fully unsupervised. Unlike conventional clustering method...

Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. In this paper, we provide a novel flexible multi-objective particle swarm optimization (PSO) clustering method based on game theory (FMOPG) to discover human activities fully unsupervised. Unlike conventional clustering method...

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutatio...

p>Many algorithms have been proposed to solve the clustering problem. However, most of them lack a proper strategy to maintain a good balance between exploration and exploitation to prevent premature convergence. Multi-Trial Vector-based Differential Evolution (MTDE) is an improved differential evolution (DE) algorithm that is done by combining thr...

p>Many algorithms have been proposed to solve the clustering problem. However, most of them lack a proper strategy to maintain a good balance between exploration and exploitation to prevent premature convergence. Multi-Trial Vector-based Differential Evolution (MTDE) is an improved differential evolution (DE) algorithm that is done by combining thr...

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutatio...

This paper presents a performance comparison of mobile robot obstacle avoidance between using Deep Reinforcement Learning (DRL) and two classical Reinforcement Learning (RL). For the DRL-based method, Deep Q-Learning (DQN) algorithm was used whereas for the RL-based method, Q-Learning and Sarsa algorithms were used. In our experiments, we have used...

Herbaria contain the treasure of millions of specimens that have been preserved for several years for scientific studies. To increase the rate of scientific discoveries, digitization of these specimens is currently ongoing to facilitate the easy access and sharing of data to a wider scientific community. Online digital repositories such as Integrat...

Human activity discovery aims to distinguish the activities performed by humans, without any prior information of what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label data because of its huge volume and the variety of...

This paper presents an implementation of autonomous navigation functionality based on Robot Operating System (ROS) on a wheeled differential drive mobile platform called Eddie robot. ROS is a framework that contains many reusable software stacks as well as visualization and debugging tools that provides an ideal environment for any robotic project...

With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing ot...

Herbarium contains treasures of millions of specimens which have been preserved for several years for scientific studies. To speed up more scientific discoveries, a digitization of these specimens is currently on going to facilitate easy access and sharing of its data to a wider scientific community. Online digital repositories such as IDigBio and...

Leaf is one of the most commonly used organs for species identification. The traditional identification process involves a manual analysis of individual dried or fresh leaf’s features by the botanists. Recent advancements in computer vision techniques have assisted in automating the plant's families/species identification process based on the digit...

Automated facial expression recognition (AFER) has become an important research area with several computer vision (CV) applications. A robust AFER system requires sufficient good quality training and testing data for development and evaluation of a robust AFER model. There exist a number of AFER datasets and an increasing number of research works i...

This paper describes the proposed methodology, data used and the results of our participation in the ChallengeTrack 2 (Expr Challenge Track) of the Affective Behavior Analysis in-the-wild (ABAW) Competition 2020. In this competition, we have used a proposed deep convolutional neural network (CNN) model to perform automatic facial expression recogni...

This study is to solve the problem of low accuracy and slow processing speed for real-time face detection and tracking systems. A margin-based region of interest approach with fixed and dynamic margin concepts is proposed to speed up the processing time. In addition, a hybrid system is developed to boost the accuracy and overcome the deficiency of...

Automated identification of herbarium species is of great interest as quite a number of these collections are still unidentified while others need to be updated following recent taxonomic knowledge. One challenging task in automated identification process of these species is the existence of visual noise such as plant information labels, color code...

The identification of plant species is fundamental for effective study and management of biodiversity. For automated plant species classification, a combination of leaf features like shapes, texture and color are commonly used. However, in herbariums, the samples collected for each species are often limited and during preservation step some of the...

Automated identification of herbarium species is of great interest as quite a number of these collections are still unidentified while others need to be updated following recent taxonomic knowledge. One challenging task in automated identification process of these species is the existence of visual noise such as plant information labels, color code...

The identification of plant species is fundamental for effective study and management of biodiversity. For automated plant species classification, a combination of leaf features like shapes, texture and color are commonly used. However, in herbariums, the samples collected for each species are often limited and during preservation step some of the...

This paper intends to understand the cultural, stylistic and historical significance of architectural heritage in Brunei Darussalam in order to ensure its safeguarding and sustainability. This paper focuses on the use of digital technologies to support the surveying and archival analysis of architectural heritage in Brunei Darussalam. Through the m...

As the internet has become more accessible, the use of Internet of Things (IoT) systems is increasing. An IoT system can be accessed either by directly connecting to the network configured with external access and appropriate port forwarding; or through a cloud server. Direct access through network may not be possible for networks with restriction...

Face detection and tracking algorithms mainly suffer from low accuracy, slow processing speed, and poor robustness when meet with real-time setup. The problem becomes crucial in real-time situations such as in human robot interactions (HRI) or video analysis. A margin-based region of interest (ROI) hybrid approach that combines Haar cascade and tem...

At current stage, the majority of the human activity recognition (HAR) technologies are based on supervised learning, where there are labeled data to train an expert system. In this paper, we proposed a framework based on the unsupervised learning to autonomously discover, learn and recognize atomic activities, i.e., the actions. The input to the H...

One of the challenges in human activity recognition is the ability for an intelligent system to discover the activity models by itself. In this paper, we propose an incremental approach to discover human activities from unlabeled data using K-means. The approach does not require prior specification of the number of clusters, or k-value, and has the...

Human activity recognition is an important ability in any system that supports human in performing their daily activities. However, current supervised approach in human activity recognition is difficult to be deployed in the natural human living environment where labeled observations are scarce. In this paper, we demonstrate the use of K-means clus...

Human activity recognition is an important ability in any system that supports human in performing their daily activities. However, current supervised approach in human activity recognition is difficult to be deployed in the natural human living environment where labeled observations are scarce. In this paper, we demonstrate the use of K-means clus...

An approach for unsupervised human activity discovery has been proposed in this paper. The approach automatically discover unknown activities from unlabeled data and has the ability to reject random activities. This ability will enable intelligent systems to discover and learn new activities autonomously. K-means is used to cluster a pool of unlabe...

Human activity recognition is an important functionality in any intelligent system designed to support human daily activities. While majority of human activity recognition systems use supervised learning, these systems lack the ability to detect new activities by themselves. In this paper, we report the results of our investigation of unsupervised...

The ability to understand what humans are doing is crucial for any intelligent system to autonomously support human daily activities. Technologies to enable such ability, however, are still undeveloped due to the many challenges in human activity analysis. Among them are the difficulties in extracting human poses and motions from raw sensor data, e...

The functionality of a robot greatly depends on its sensory capability. Adding sensors to a robot is one major strategy to extend the robot's function and intelligence. However, this strategy can only be effective if the performance of the sensors meet the requirements of the robot application. In particular, in many systems, it is crucial for the...