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All Journal Perfecting a Video Game with Game Metrics Indonesian Journal of Electrical Engineering and Computer Science
Zuraini Othman
Universiti Teknikal Malaysia Melaka
Computer Science & IT

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Road crack detection using adaptive multi resolution thresholding techniques Zuraini Othman; Azizi Abdullah; Fauziah Kasmin; Sharifah Sakinah Syed Ahmad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.12755

Abstract

Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist’s knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales.
Pixel-wise classification using support vector machine for binarization of degraded historical document image Fauziah Kasmin; Zuraini Othman; Sharifah Sakinah Syed Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1329-1336

Abstract

Binarization of historical documents nowadays is very important as digital archiving has become the best and preferred solution for the retrieval and storage of valuable archives. However, the process becomes more challenging due to the degradation of historical documents. Hence, this paper described a method on binarization of historical documents using the learning concept. Support vector machine (SVM) learning was used as a classifier in this work. After training some images with the help of ground truth images, a model was developed. Testing images then used the model to segregate each pixel as text or non-text. The grey level and RGB values were chosen as descriptors for a particular pixel and comparisons were made between these two descriptors. The intensities of the local neighbourhood for every pixel were used in the experiment. To compare these descriptors, standard dataset HDIBCO2014, DIBCO2012 and DIBCO2016 were used in the training and testing phase. The results from the experiment clearly showed that grey level values gave better performance compared to RGB values.
Dimentionality reduction based on binary cooperative particle swarm optimization Sharifah Sakinah Syed Ahmad; Ezzatul Farhain Azmi; Fauziah Kasmin; Zuraini Othman
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1382-1391

Abstract

Even though there are numerous classifiers algorithms that are more complex, k-Nearest Neighbour (k-NN) is regarded as one amongst the most successful approaches to solve real-world issues. The classification process’s effectiveness relies on the training set’s data. However, when k-NN classifier is applied to a real world, various issues could arise; for instance, they are considered to be computationally expensive as the complete training set needs to be stored in the computer for classification of the unseen data. Also, intolerance of k-NN classifier towards irrelevant features can be seen. Conversely, imbalance in the training data could occur wherein considerably larger numbers of data could be seen with some classes versus other classes. Thus, selected training data are employed to improve the effectiveness of k-NN classifier when dealing with large datasets. In this research work, a substitute method is present to enhance data selection by simultaneously clubbing the feature selection as well as instances selection pertaining to k-NN classifier by employing Cooperative Binary Particle Swarm Optimisation (CBPSO). This method can also address the constraint of employing the k-nearest neighbour classifier, particularly when handling high dimensional and imbalance data. A comparison study was performed to demonstrate the performance of our approach by employing 20 real world datasets taken from the UCI Machine Learning Repository. The corresponding table of the classification rate demonstrates the algorithm’s performance. The experimental outcomes exhibit the efficacy of our proposed approach.
Co-Authors Azizi Abdullah Ezzatul Farhain Azmi Fauziah Kasmin Sharifah Sakinah Syed Ahmad