Kamarul Hawari Ghazali

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.

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.

A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Z-source inverter pulse width modulation: A survey

The paper presents switching techniques for pulse-width-modulated-based z-source inverter; simple boost PWM, maximum boost PWM, maximum constant boost PWM, modified-reference PWM, modified space vector PWM, hysteresis current control, and sine carrier PWM. Concepts and ideas behind the techniques are briefly explained. An overall view is summed up of the PWM controls, in terms of their achievements and system involvement in ZSI.

Detection Improvised Explosive Device (IED) Emplacement Using Infrared Image

This paper presents a method to detect an improvised explosive device (IED) by using infrared thermography (IRT) technology. The detection of IED will be done automatically and accurately even the IED detection expert is not present. Combining the advantage of IRT and image processing technique, the proposed method is very efficient and responsive to detect the existence of hidden IED. The captured images are filtered and segmented to extract the heat pattern before the decision is made. Based on the experimental result, the proposed system produced about 92 % of detection accuracy.

Detecting ROIs in the thermal image of electrical installations

This paper presents a method of detecting and segmenting regions of interest (ROIs) of the thermal image of electrical installations. These regions are very important in diagnosing the thermal condition of electrical equipment. Due to the nature of thermal imaging, segmentation with the conventional approach will make inaccurate ROI detection, especially when qualitative approach is considered in evaluating the equipments condition. Therefore, we take the advantage of extracting local features to identify, locate, and match multiple repeated objects and grouping a look-like similar objects in the images. Experimental results show that the proposed method achieves better performance for detecting the target ROIs with various irregular intensity variations, dim target equipments and cluttered background. The performance of the proposed method is qualitatively and quantitatively evaluated.

Thermal condition monitoring of electrical installations based on infrared image analysis

Infrared imaging is a commonly used tool for monitoring and assessing the thermal condition of electrical installations for ensuring a reliable power supply. However, the conventional evaluation approach is quite time consuming as the image is analyzed manually by a qualified personnel. Therefore, this paper proposed a fast thermal anomaly detection and classification based on qualitative infrared image analysis. First, regions of interest (ROIs) are semi-automatically selected by employing normalized cross correlation (NCC) for finding similar objects in the image. Statistical features are extracted from each detected region and classified using multilayer perceptron (MLP) neural network for determining the thermal condition of electrical equipment. The overall accuracy obtained by the proposed method is approximately 95%, which is highly encouraging.

Multi-Angle Face Detection Using Back Propagation Neural Network

In machine vision application, the main part to analyze an image is to identify its features which contribute to efficiency of the system. Many applications in vision system and image analysis used face detection as a feature of their whole system development. In application such as video surveillance, fatigue detection and security system, face is a fundamental step in the analysis before proceed to system implementation. It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. In this paper, we propose a neural network based approach to identify multi-angle face which falls into five categories: all left-side face, half left-side face, positive face, half right-side face, and all right-side face. More than 100 images of each category have been used for training and testing of face detection and its features was extracted to be an input to BP neural network. We analyzed the result of training and testing set of neural network and the best classification achieved was 90.7%.

Classification of Fresh N36 Pineapple Crop Using Image Processing Technique

Malaysia is one of the world pineapple producers besides Thailand, Philippine, Indonesia, Brazil and South Africa. The government encourage farmers to have more production to meet increasing demand for export. Most of the pineapple production activities is still in manual process and rely on labor workers. In this paper, we proposed a system that can be used in production house to automatically detect the maturity index of pineapple. We implement image processing method to determine the maturity of a pineapple based on yellowish skin color. Binary ellipse mask has been used for extracting region of interest (ROI) as well as morphology normalized RGB to filter out the background and unwanted pixel image. Finally, linear method using threshold values has been selected to classify the maturity index. 910 pineapple images has been used at the development and testing stage and we obtained promising result with 94.29% good classification rate.