Mohd Marzuki Mustafa

Feature Extraction Technique using Discrete Wavelet Transform for Image Classification

The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Low level feature extraction involves automatic extraction of features from an image without doing any processing method. In this paper, we consider the use of high level feature extraction technique to investigate the characteristic of narrow and broad weed by implementing the 2 dimensional discrete wavelet transform (2D-DWT) as the processing method. Most transformation techniques produce coefficient values with the same size as the original image. Further processing of the coefficient values must be applied to extract the image feature vectors. In this paper, we propose an algorithm to implement feature extraction technique using the 2D-DWT and the extracted coefficients are used to represent the image for classification of narrow and broad weed. Results obtained suggest that the extracted 2D-DWT coefficients can uniquely represents the two different weed type.

Development of vehicle driver drowsiness detection system using electrooculogram (EOG)

Driver drowsiness is one of the major causes of road accident. Various driver drowsiness detection systems have been designed to detect and warn the driver of impending drowsiness. Most available prototype and ongoing research have focused on video-based eye tracking system, which demands high computing power due to real time video processing. In our research, the use of electrooculogram (EOG) as an alternative to video-based systems in detecting eye activities caused by drowsiness is evaluated. The EOG, which is the electrical signal generated by eye movements, is acquired by a mobile biosignal acquisition module and are processed offline using personal computer. Digital signal differentiation and simple information fusion techniques are used to detect signs of drowsiness in the EOG signal. EOG signal is found to be a promising drowsiness detector, with detection rate of more than 80%. Based on the tested offline processing techniques, an online fatigue monitoring system prototype based on a Personal Digital Assistant (PDA) has been designed to detect driver dozing off through EOG signal.

Driver fatigue detection using steering grip force

This paper describes an automobile driver fatigue detection method by monitoring the drivers grip force on the steering wheel, based on the variation in steering grip force due to fatigue or loosing alertness. Steering grip force data is obtained by using two resistive force sensors attached to the steering wheel and connected to a personal computer with the aid of a data acquisition module. The alertness of the driver is then assessed by utilizing change detection algorithm based on log-likelihood ratio. The aforementioned system is a module of a driver safety system for smart vehicle, which uses sensor fusion technology to prevent driver-related road accidents.

Shape Characteristics Analysis for Papaya Size Classification

Prior to export, papaya are subjected to inspection for the purpose of quality control and grading. For size grading, the fruit is weighed manually hence the practice is tedious, time consuming and labor intensive. Therefore, this paper will discuss the development of a computer vision system for papaya size grading using shape characteristic analysis. The methodology involves data acquisition to collect the images and their weights. The RGB images were converted to binary images using automatic thresholding based on the Otsu method. Some morphological procedures were involved for image enhancement to distinguish the papaya object from the background. Then the shape characteristics consisting of area, mean diameter and perimeter were extracted from the papaya images. We classified according to combinations of the three features to study the uniqueness of the extracted features. Each combination was fed separately to a neural network for training and testing. The proposed technique showed the ability to perform papaya size classification with more than 94% accuracy in this research.

Machine Vision System for Automatic Weeding Strategy in Oil Palm Plantation using Image Filtering Technique

Machine vision is an application of computer vision to automate conventional work in industry, manufacturing or any other field. Nowadays, people in agriculture industry have embarked into research on implementation of engineering technology in their farming activities. One of the precision farming activities that involve machine vision system is automatic weeding strategy. Automatic weeding strategy in oil palm plantation could minimize the volume of herbicides that is sprayed to the fields. This paper discusses an automatic weeding strategy in oil palm plantation using machine vision system for the detection and differential spraying of weeds. The implementation of vision system involved the used of image processing technique to analyze weed images in order to recognized and distinguished its types. Image filtering technique has been used to process the images as well as a feature extraction method to classify the type of weed images. As a result, the image processing technique contributes a promising result of classification to be implemented in machine vision system for automated weeding strategy.

Motion detection using Lucas Kanade algorithm and application enhancement

Currently, computational of the optical flow of a sequence of images still remains a challenge in video processing. There are no specific techniques that can sufficiently generate an accurate and dense optical flow. Computational using local variable such as Lucas Kanade algorithm does not provide a good segmentation which indirectly affects the pattern of the optical flow obtained. In this paper, we will only focus on differential methods which are Lucas Kanade and Horn Schunck. We investigated the difference in standalone Lucas Kanade algorithm and the effect when it is combined with global variable such as number of iteration and smoothing from Horn Schunck algorithm and filtering. Comparison is made based on the optical flow pattern, segmentation of the motion of the images and the processing time. Experiments on the images show that by using the derivation of partial derivative in Lucas Kanade in Horn Schunck algorithm with the smoothing effect and number of iteration along with filters will result in better segmentation and better optical flow. Thus, this shows that the computation of intensity will influence the optical flow.

Telepointer technology in telemedicine : a review

Telepointer is a powerful tool in the telemedicine system that enhances the effectiveness of long-distance communication. Telepointer has been tested in telemedicine, and has potential to a big influence in improving quality of health care, especially in the rural area. A telepointer system works by sending additional information in the form of gesture that can convey more accurate instruction or information. It leads to more effective communication, precise diagnosis, and better decision by means of discussion and consultation between the expert and the junior clinicians. However, there is no review paper yet on the state of the art of the telepointer in telemedicine. This paper is intended to give the readers an overview of recent advancement of telepointer technology as a support tool in telemedicine. There are four most popular modes of telepointer system, namely cursor, hand, laser and sketching pointer. The result shows that telepointer technology has a huge potential for wider acceptance in real life applications, there are needs for more improvement in the real time positioning accuracy. More results from actual test (real patient) need to be reported. We believe that by addressing these two issues, telepointer technology will be embraced widely by researchers and practitioners.

Real-time control of non-minimum phase electro-hydraulic system using trajectory-adaptive ZPETC

Hydraulic actuator has been widely used in industrial equipments and processes principally due to its high-power density and system solution that it can provided. The natural nonlinear property of hydraulic cylinder has challenged researchers in designing suitable controller for positioning control, motion control and tracking control. This paper proposes a controller design using trajectory-adaptive ZPETC without factorization of zeros and implementing real-time control to non-minimum phase electro-hydraulic system. Simulation and real-time experimental results were compared and evaluated and they show interesting tracking performances.

Trajectory zero phase error tracking control using comparing coefficients method

This paper presents the studies on trajectory zero phase error tracking control without factorisation of zeros polynomial where the controller parameters are determined using comparing coefficients methods. The controller was applied to two types of third-order non-minimum phase plant. The first plant was having a zero outside and far from the unity circle. Another plant was having a zero outside and near to the unity circle. Simulation and experimental results will be presented to discuss its tracking performance.

Support vector machines for automated classification of plastic bottles

Many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification.[3]Therefore, in this work, an automated classification of plastic bottles based on the extraction of best feature vectors to represent the type of plastic bottles is performed using the morphological based approach. Morphological operations are used to describe the structure or form of an image. By using the two-dimensional description of plastic bottle silhouettes, edge detection of the object silhouette is performed followed by the erosion process. This procedure can be considered as two stages; a) a feature vector is extracted from the analysis of morphological operation and structure element used and b) a classification technique is applied to that input vector in order to provide a meaningful categorization of the data content. In this study, Support Vector Machines (SVM) was employed merely to classify the image of two groups of plastic bottles namely polyethylene-terephthalate (PET) and non-PET. Additionally, for detailed classification task, the pattern of decision boundary for classification of extracted feature vectors based on morphological approach is also illustrated. Furthermore, the optimal features for input to SVM classifier is identified. The initial results indicate that the performance of the SVM in terms of classification accuracy is more than 90%.