Farrah Wong

Comparison of hand segmentation methodologies for Hand gesture recognition

The Hand gesture has provided significant means of communication in human daily interaction and has been widely explored in the Human-Computer Interaction (HCI) studies. This paper presents a comparison of hand segmentation methodologies in the early stage development of an appearance-based hand gesture recognition system for sign language application. The hand segmentation method is based on Jones and Rehg generic skin color model and frame differencing technique that utilized the color and motion cues of image content. Several issues and challenges occurred during the experiments are also discussed and later tackled by the current approach. The present approach applied the idea of integrating both color and motion cues into a single probability map and yield robust features for further tasks. It is merely a first step progress towards an independent environment and signer for the hand gesture recognition system.

Hidden Markov Model-Based Gesture Recognition with Overlapping Hand-Head/Hand-Hand Estimated Using Kalman Filter

In this paper, we introduce a hand gesture recognition system to recognize isolated Malaysian Sign Language (MSL). The system consists of four modules: collection of input images, feature extraction, Hidden Markov Model (HMM) training, and gesture recognition. First, we apply skin segmentation procedure throughout the input frames in order to detect only skin region. Then, we proceed to feature extraction process consisting of centroids, hand distance and hand orientation collecting. Kalman Filter is used to identify the overlapping hand-head or hand-hand region. After having extracted the feature vector, the hand gesture trajectory is represented by gesture path in order to reduce system complexity. We apply Hidden Markov Model (HMM) to recognize the input gesture. The gesture to be recognized is separately scored against different states of HMMs. The model with the highest score indicates the corresponding gesture. In the experiments, we have tested our system to recognize 112 MSL, and the recognition rate is about 83%.

Tracking and localisation of moving vehicle license plate via signature analysis

In this paper, a new algorithm for dynamic vehicle license plate localisation is proposed on the basis of signature analysis and connected component analysis. Most existing techniques use low level features such as detection of colour and edges, these methods may have issues with reliability. The proposed algorithm however, aims to manipulate more accurate perceptual motion information. The camera is set to record the moving vehicle, while the angle of view and the distance from the moving vehicle is changed according to the observational setup. Image differencing is used to detect the motion, and noise is filtered out to obtain a binary image that can stand out from the motion of the moving vehicle. The algorithm was tested using moving vehicles with different backgrounds, under illumination change and varying poses and angle.

Adaptive Tracking of Overlapping Vehicles Via Markov Chain Monte Carlo with CUSUM Path Plot Algorithm

Vehicle detection and tracking is essential in traffic surveillance and traffic flow optimization. However, occlusion or overlapped vehicle tracking is difficult and remain a challenging research topic in image processing. In this paper, a conventional Markov Chain Monte Carlo (MCMC) is enhanced via Cumulative Sum (CUSUM) path plot in order to track vehicles in overlapping situation. By calculating the hairiness of CUSUM path plot, MCMC can be diagnosed as converged based on its sampling outputs. Varying sample size of MCMC provides enhancement to the tracking performance and capability of overcoming the limitation of conventional fix sample size algorithm. In addition, implementation of m-th order prior probability distribution and fusion of color and edge distance likelihood have further improved the tracking accuracy. MCMC with fixed sample size and CUSUM path plot are implemented and their corresponding performances are analyzed. Experimental results show that MCMC with CUSUM path plot has better performance where it is able to track the overlapped vehicle accurately with lesser processing time.

Comparison study of Hidden Markov Model gesture recognition using fixed state and variable state

This paper presents a method of gesture recognition using Hidden Markov Model (HMM). Gesture itself is based on the movement of each right hand (RH) and left hand (LH), which represents the word intended by the signer. The feature vector selected, gesture path, hand distance and hand orientations are obtained from RH and LH then trained using HMM to produce the respective gesture class. While training, in handling HMM state, we introduce fixed state and variable state, where in fixed state, the numbers of state is generally fixed for all gestures and while the number of state in variable state is determined by the movement of the gesture. It was found that fixed state gave the highest rate of recognition achieving 83.1%.

Multiple Vehicles License Plate Tracking and Recognition via Isotropic Dilation

In this paper, a new algorithm for Automatic License Plate Localisation and Recognition (ALPR) is proposed on the basis of isotropic dilation that can be achieved using the binary image Euclidean distance transform. In a blob analysis problem, any two Region of Interest (RoIs) that is discontinuous are typically treated as separate blobs. However, the proposed algorithm combine with Connected Component Analysis (CCA) are coded to seek for RoI within a certain distance of other RoI to be treated as non-unique. This paper investigates the design and implementation of several pre-processing techniques and isotropic dilation algorithm to classify moving vehicles with different backgrounds and varying angles. A multi-layer feed-forward back-propagation Neural Network is used to train the segmented and refined characters. The results obtained can be used for implementation in the vehicle parking management system.

A stereo auditory display for visually impaired

According to information from the World Health Organization (WHO) as of 1990, there are, approximately, 38 million people who are blind. This paper reports the progress of a funded research project to invent Malaysias very own electronic travel aid (ETA). The research aims to develop an auditory display system that converts pictures to sound in order to aid the blind in navigation. The experimental system consists of a digital video camera, and a portable computer as well as earphones. The developed software functions as an extractor of the data collected by the camera, processes and transforms the information into sound patterns to indicate the presence of an obstacle while the user is navigating. Assessment for the system is currently being carried out at the laboratory level and preliminary test has shown success in interpreting the sound pattern generated.

A direct assessment method of the achievement of the program outcomes from the courses outcomes

The achievement of the program outcomes (POs) is very important for engineering institutions who have adopted Outcome Based Education (OBE). Generally, program outcomes assessment methods can be divided into direct and indirect methods. Direct methods are generally based on grades obtained from examination or project works while indirect methods are based on perception obtained from surveys, questionnaires and observations. In this paper, a direct assessment method is proposed whereby the assessment of the achievement of the program outcomes is based on the marks obtained by students in the final exam. There are three main advantages of the system. First, the relationship between the program outcomes and courses outcomes is based on Set Theory and therefore the use of the weight matrix, used in most assessment systems, is not necessary. Second, the system can deal with courses outcomes of all learning domains by converting marks into rubric scale. Thirdly, the system can assess the performance of the cohort as well as the individual graduates.

Analysis of Surface Electromyography for On-Off Control

Myogram on-and-off controller is important for improving or assisting the elderly people. One of the most important aspects of the controller development is to determine the on and off time with respect to the body movement. In this project, high accuracy signal filtering, high gain amplifier, signal converter, microcontroller and electrodes are used for circuit simulation and development to obtain muscle signal (Electromyogram). Precision rectifier is used to solve the ordinary semiconductor problem to avoid signal block. To ensurethe user-friendliness in the development of this device, non-invasive electrodes are used in this project instead of invasive electrodes.

Wavelet-based skin segmentation for detecting occluded arms in human body pose modelling system

The task of correctly tracking the body parts is one of the crucial problems in the human body pose modelling. Various factors need to be investigated as the variety of body poses is unlimited and the visual appearance varies according to the environment. The human body can be composed into several parts such as the head, torso, arms and legs. The arms can be considered as the most challenging body part to be tracked since it tends to move fast and usually occluded within other body parts. This paper addresses the problem of extracting the arms which are occluded in the torso part. A wavelet-based skin segmentation method is applied to detect the skin region. The segmentation procedure is performed using six different colour spaces namely the RGB, rgb, HSI, TSL, SCT and CIELAB. The segmentation performances are evaluated on colour component basis. The aim of this paper is to determine the best colour components that are suitable for this segmentation procedure.