Musa Mohd Mokji

Gray Level Co-Occurrence Matrix Computation Based On Haar Wavelet

In this paper, a new computation for gray level co-occurrence matrix (GLCM) is proposed. The aim is to reduce the computation burden of the original GLCM computation. The proposed computation will be based on Haar wavelet transform. Haar wavelet transform is chosen because the resulting wavelet bands are strongly correlated with the orientation elements in the GLCM computation. The second reason is because the total pixel entries for Haar wavelet transform is always minimum. Thus, the GLCM computation burden can be reduced. The proposed computation is tested with the classification performance of the Brodatz texture images. Although the aim is to achieve at least similar performance with the original GLCM computation, the proposed computation gives a slightly better performance compare to the original GLCM computation.

Adaptive thresholding based on co-occurrence matrix edge information

In this paper, an adaptive thresholding technique based on gray level co-occurrence matrix (GLCM) is presented to handle images with fuzzy boundaries. As GLCM contains information on the distribution of gray level transition frequency and edge information, it is very useful for the computation of threshold value. Here the algorithm is designed to have flexibility on the edge definition so that it can handle the objects fuzzy boundaries. By manipulating information in the GLCM, a statistical feature is derived to act as the threshold value for the image segmentation process. The proposed method is tested with the starfruit defect images. To demonstrate the ability of the proposed method, experimental results are compared with three other thresholding techniques

Performance evaluation of wavelet-based PCB defect detection and localization algorithm

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB) manufacturing. Due to the fatigue and speed requirement, manual inspection is ineffective to inspect every printed circuit board. Hence, this paper presents an efficient algorithm for an automated visual PCB inspection system that is able to automatically detect and locate any defect on PCBs. The defect is detected by utilizing wavelet-based image difference algorithm. The coarse resolution defect localization algorithm, is also presented. The coarse resolution defect localization algorithm is applied to the coarse resolution differenced image in order to locate the defective area on the fine resolution tested PCB image. In addition, the performance of the algorithm is evaluated to verify the efficiency of the proposed algorithm in term of computation time. This new method turned out to be computationally less intensive than traditional image difference operation. One conclusion from this paper is that the second level Haar wavelet transform should be chosen for the application of automated visual PCB inspection.

License plate localization based on edge-geometrical features using morphological approach

Malaysian car plates in general appear in different character styles, types (either single or double row), sizes, spacing and character counts. Such variations cause even detecting and localizing these plates a difficult problem. The problem of localization is aggravated further during night time due to poor illumination. In this paper, we introduce the idea of edge-geometrical features in detecting these plates. The edge part is obtained from the use of Difference of Gaussian operation followed by Sobel vertical edge mask. Prior to that, gamma correction is applied to increase the detection of edges. We then apply morphological operations to get the plate region candidates. Using these regions, together with the edge image, we calculate geometrical features of these regions and use rule-based classifier to correctly identify the true plate region. Finally, we test out method using our own data set which contained 250 images captured during day time and 100 images captured during night time. The result of the proposed method shows 96.9% success rate.

Feature Extraction for Traditional Malay Musical Instruments Classification System

Automatic musical instrument classification system deals with a large number of sound database and various types of features schemes. With the lack of data pre-processing, it might become invaluable asset that can impact the whole classification tasks. In handling an effective classification system, finding the best data sets with the best features schemes often a vital step in the data representation and feature extraction process. Thus, this study is conducted in order to investigate the impact of several factors that might affecting the classification accuracy such as audio length, segmented frame size and data sets size (for training and testing) towards Traditional Malay musical instruments sounds classification system. The perception-based and MFCC features schemes with total of 37 features was utilized in this study. Meanwhile, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets and extracted features schemes in terms of their classification performance. Results show that the highest accuracy of 99.57% was obtained from the best data sets with the combination of full features. It is also revealed that the identified factors had a significant role to the performance of classification accuracy. Hence, this study suggest that further feature analysis research is necessary for better solution in Traditional Malay musical instruments sounds classification system problem.

Starfruit Color Maturity Classification Using Cr as Feature

The quality inspection for export star fruit is still perform manually by human labor until today. Due to manual process, a real-time system for star fruit color maturity inspection is developed in this paper. In real-time application, most of the image acquisition device is using YCbCr color space data such as CCD camera. This paper presents the modification on the previous star fruit color maturity classification algorithm which is based on RGB color space into YCbCr color space. In this new modified algorithm, the system is faster than before because the star fruit maturity classification process operates without any mathematical operation involved in the feature extraction process. This process is possible as the color information can be obtained directly from the Cb and Cr component which is not the case in the RGB color space. Based on the experiment results, the classification accuracy for the modified algorithm is 96%.

Background subtraction for object detection under varying environments

Background subtraction is widely used for extracting unusual motion of object of interest in video images. In this paper, we propose a fast and flexible approach of object detection based on an adaptive background subtraction technique that also effectively eliminates shadows based on color constancy principle in RGB color space. This approach can be used for both outdoor and indoor environments. Our proposed method of background subtraction makes use of multiple thresholding technique for detecting object of interests for any given scene. Once the moving object has been detected from the complex background, then the shadows are detected and eliminated by considering some environmental parameters.

The ideal data representation for feature extraction of traditional Malay musical instrument sounds classification

In presenting the appropriate data sets, various data representation and feature extraction methods have been discovered previously. However, almost all the existing methods are utilized based on the Western musical instruments. In this study, the data representation and feature extraction methods are applied towards Traditional Malay musical instruments sounds classification. The impact of five factors that might affecting the classification accuracy which are the audio length, segmented frame size, starting point, data distribution and data fraction (for training and testing) are investigated. The perception-based and MFCC features schemes with total of 37 features was used. While, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets in terms of the classification performance. The results show that the highest accuracy of 97.37% was obtained from the best data sets with the combination of full features.

Brain lesion segmentation of Diffusion-weighted MRI using gray level co-occurrence matrix

This paper presents an automated segmentation of brain lesion from Diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on region and boundary information in gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, GLCM is computed to segment the lesions. Different peaks from the GLCM cross-section indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed method provides very good segmentation results even in a small brain lesion.

A Noise Elimination Procedure for Printed Circuit Board Inspection System

Image difference operation is frequently used in automated printed circuit board (PCB) inspection system as well as in many other image processing applications. During the implementation, this operation brings along the unwanted noise due to misalignment and uneven binarization. Thus, this paper proposes a method to eliminate, if possible, or to reduce as much as possible such noise during the computation of defect detection. This paper used a template PCB image and the tested PCB image as the input. Image subtraction operation will be applied between the images. The results of applying the proposed method showed a significant improvement during the real-time inspection of printed circuit boards.