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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Loay E. George
University of Information Technology and Communication
Computer Science & IT

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An efficient method for stamps recognition using Haar wavelet sub-bands Maha A. Rajab; Loay E. George
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18763

Abstract

The problem facing certain organizations such as insurance companies and government institutions where a huge amount of documents is handled every day, hence an automated stamp recognition system is required. The image of the stamp may be on a different background, with different sizes, and suffers from rotating in different directions, also, the appearance of soft areas (patches) or small points as noise. Thus, the main objective of this paper is to extract and recognize the color stamp image. This paper proposed a method to recognize stamps, by using a technique named Haar wavelet sub-bands. The devised method has four stages: 1) extracts the stamp image; 2) preprocessing the image; 3) feature extraction; and 4) matching. This paper is implemented using C sharp (Microsoft Visual Studio 2012) programming language. The experiments conducted on a stamp dataset showed that the proposed method has a great capability to recognize stamps when using Haar wavelet transform with two sets of features (i.e., 100% recognition rate for energy features and 99.93% recognition rate for low order moment).
The detection of oil spill onshore using the thermal band of landsat-8 Dheya Uldeen K. Abbas; Loay E. George
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.22462

Abstract

Remote sensing is an advanced technique that can be used to detect hydrocarbons on land, and its advantages include rapid and low-cost detection. The detection of hydrocarbons on the surface is essential for environmental monitoring and the purpose of exploration. For oil and gas companies, the ability to locate hydrocarbon spill sites is important information for the success of future exploration wells. This study aimed to determine the quantities of hydrocarbons mixed with the soil as an indicator of its accumulation in the subsurface. In this paper, temperature data obtained from the landsat-8 satellite were used to determine the extent of the oil spill in the area using spatial interpolation and gradient techniques. Kriging interpolation is based on statistical models (i.e. including auto-correlation) geostatistical techniques can produce a forecast surface and also provide accurate forecasts. The results showed that there are 60 sites of thermal anomalies and temperature values are lowest and highest 23.2 oC to 91.11 oC, in the study area. Three different sites were chosen in terms of area and effect. They are located in the northern and southern Rumaila and Zubair fields. The location of the oil spills was determined with high accuracy after identifying the anomalies. 
Stamps extraction using local adaptive k- means and ISODATA algorithms Maha A. Rajab; Loay E. George
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp137-145

Abstract

One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cluster from stamp cluster. Thirdly, a region growing algorithm is applied to segment the image and then choosing the connected region to produce a binary mask for the stamp area. Finally, the binary mask is combined with the original image to extract the stamp regions. The results indicate that the number of clusters can be determined dynamically and the largest cluster that has minimum standard deviation (i.e., always the largest cluster is the background cluster). Also, show that the binary mask can be established from more than one segment to cover are all stamp’s disconnected pieces and it can be useful to remove the noise appear with stamp region.
An ear recognition system based on local wavelet subband energy distribution Ruaa Isam Fadhil; Loay E. George
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1212-1219

Abstract

The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.
Co-Authors Dheya Uldeen K. Abbas Maha A. Rajab Ruaa Isam Fadhil