Articles

Found 34 Documents
Search

Enhancement of Iris Recognition System Based on Phase Only Correlation Arnia, Fitri; Pramita, Nuriza
TELKOMNIKA Telecommunication, Computing, Electronics and Control Vol 9, No 2: August 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (51.295 KB)

Abstract

Iris recognition system is one of biometric based recognition/identification systems. Numerous techniques have been implemented to achieve a good recognition rate, including the ones based on Phase Only Correlation (POC). Significant and higher correlation peaks suggest that the system recognizes iris images of the same subject (person), while lower and unsignificant peaks correspond to recognition of those of difference subjects. Current POC methods have not investigated minimum iris point that can be used to achieve higher correlation peaks. This paper proposed a method that used only one-fourth of full normalized iris size to achieve higher (or at least the same) recognition rate. Simulation on CASIA version 1.0 iris image database showed that averaged recognition rate of the proposed method achieved 67%, higher than that of using one-half (56%) and full (53%) iris point. Furthermore, all (100%) POC peak values of the proposed method was higher than that of the method with full iris points.
Improvement of binarization performance using local otsu thresholding Saddami, Khairun; Munadi, Khairul; Away, Yuwaldi; Arnia, Fitri
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1775.514 KB) | DOI: 10.11591/ijece.v9i1.pp264-272

Abstract

Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermore, the manuscript contrast was adjusted by mapping intensity of image value using intensity transformation method. Next, the local Otsu thresholding was applied. For removing spotting noise, we applied labeled connected component. The proposed method had been testing on H-DIBCO 2014 and degraded Jawi handwritten ancient documents. It performed better regarding recall and precision values, as compared to Otsu, Niblack, Sauvola, Lu, Su, and Nina, especially in the documents with show-through, water-spilling and combination noises.
KLASIFIKASI OTOMATIS MOTIF TEKSTIL MENGGUNAKAN SUPPORT VECTOR MACHINE MULTI KELAS Ramadhani, Ramadhani; Arnia, Fitri; Muharar, Rusdha
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7, No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tekstur merupakan pola atau motif tertentu yang tersusun secara berulang-ulang pada citra. Tekstur mudah dikenali/dikelompokkan oleh manusia, tetapi sulit bagi mesin. Klasifikasi tekstur secara otomatis berguna dan dibutuhkan pada banyak bidang seperti industri tekstil, pendaratan pesawat otomatis, fotografi dan seni. Pada industri tekstil, klasifikasi tekstur otomatis dapat meningkatkan efisiensi proses desain motif. Motif tekstil terdiri dari banyak kelompok, sehingga diperlukan metode klasifikasi multi kelas untuk mengelompokkan motif-motif tersebut. Artikel ini memaparkan kinerja tiga metode Support Vector Machine (SVM) multi kelas: One Against One (OAO), Directed Acyclic Graph (DAG) dan One Against All (OAA) pada klasifikasi motif dari citra tekstil, dimana Wavelet Gabor digunakan sebagai pengekstraksi fitur. Kinerja SVM diukur berdasarkan parameter akurasi dan fitur Gabor diekstraksi dengan skala dan orientasi yang berbeda. Tujuan penelitian ini adalah menentukan kinerja SVM dan pengaruh jumlah skala dan orientasi Gabor yang digunakan pada klasifikasi motif tekstil. Pada simulasi digunakan 120 citra tekstil yang terbagi menjadi tiga kategori motif: bunga, kotak dan polkadot. Akurasi pengelompokan SVM mencapai kisaran 90%-100%, bahkan untuk citra yang terpotong. Pengujian dengan k-fold validation menunjukkan bahwa SVM DAG lebih baik daripada SVM OAO dan SVM OAA, dengan akurasi mencapai 78%. AbstractTexture is a repetition of a specific pattern concatenation in an image. The Texture can be defined as a repetition of pattern in an image.  The texture is easy for the human to classify, but it is not easy for a machine. Automatic texture classification is useful and required in many fields such as textile industry, automatic aircraft landing, photography and art. In the textile industry, automatic texture classification can enhance the efficiency of motif designing process. The textile motif is various and should be grouped into more than two classes; therefore a multiclass classification is required. This article discusses the performance of multiclass Support Vector Machine (SVM): One Against One (OAO), Directed Acyclic Graph (DAG) and One Against All (OAA) in classifying textile motifs, in which the Gabor Filter was used to extract the texture features. The SVM performance was measured in terms of accuracy, while the Gabor features were extracted in a different combination of scales and orientations. The purpose of the work is to measure the SVM performance and determine the effect of using various Gabor scales and orientations in textile motifs classification. We used 120 textile images with three motifs: flower, boxes and polka dot. The SVM accuracy of 90%-100% was achieved; even for cropped textile images. Using the k-fold validation, the accuracy of SVM DAG was 78%, higher than those of SVM OAO and SVM OAA
Ordinal Measure of Discrete Cosine Transform Blocks for Iris Identification Arnia, Fitri; Irianda, Fery; Aisyah, Siti; Munadi, Khairul
Proceedings of The Annual International Conference, Syiah Kuala University - Life Sciences & Engineering Chapter Vol 2, No 2 (2012): Engineering
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.779 KB)

Abstract

Currently, a common method for identifying a person is by means of an identitycard (ID) or combination of an ID and password. The approaches are not very reliable, since the ID can be stolen and password can be forgotten. A more reliable identification system is required. In the last decades, identification systems based on biometrics have been gaining attention, since they are more reliable. Biometrics-based devices identify people based on their physical or psychological characteristics, such as palmprints, fingerprints, gait and iris. Unlike fingerprints or palmprints, irides features distribute randomly, and the features were unique; the features between right and left eyes aredifferent, as well as between twins. Therefore, in addition to reliability, the use of irides can enhance identification accuracy. Purpose of the paper was to improve identification rate of an iris identification method, using ordinal measure of Discrete Cosine Transform (DCT) coefficient. The input iris image was tiled into blocks of 8x8 pixels, then the DCT was applied to each blocks. The AC coefficients of each block were sorted from the smallest to the largest values, in which the sorted values were referred to as ordinal measures.Identification was accomplished by measuring a distance between the ordinal measure of the input images with the ones of the existing images in the database using Minkwoski distance metric. Proposed method increased the averaged identification rate as compared to the previous method by nearly twice from 33% to 61.4%.
KOMBINASI METODE NILAI AMBANG LOKAL DAN GLOBAL UNTUK RESTORASI DOKUMEN JAWI KUNO Saddami, Khairun; Arnia, Fitri; Away, Yuwaldi; Munadi, Khairul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7, No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020701741

Abstract

Dokumen Jawi kuno merupakan warisan budaya yang berisi informasi penting tentang peradaban masa lalu yang dapat dijadikan pedoman untuk masa sekarang ini. Dokumen Jawi kuno telah mengalami penurunan kualitas yang disebabkan oleh beberapa faktor seperti kualitas kertas atau karena proses penyimpanan. Penurunan kualitas ini menyebabkan informasi yang terdapat pada dokumen tersebut menghilang dan sulit untuk diakses. Artikel ini mengusulkan metode binerisasi untuk membangkitkan kembali informasi yang terdapat pada dokumen Jawi kuno. Metode usulan merupakan kombinasi antara metode binerisasi berbasis nilai ambang lokal dan global. Metode usulan diuji terhadap dokumen Jawi kuno dan dokumen uji standar yang dikenal dengan nama Handwritten Document Image Binarization Contest (HDIBCO) 2016. Citra hasil binerisasi dievaluasi menggunakan metode: F-measure, pseudo F-measure, peak signal-to-noise ratio, distance reciprocal distortion, dan misclasification penalty metric. Secara rata-rata, nilai evaluasi F-measure dari metode usulan mencapai 88,18 dan 89,04 masing-masing untuk dataset Jawi dan HDIBCO-2016. Hasil ini lebih baik dari metode pembanding yang menunjukkan bahwa metode usulan berhasil meningkatkan kinerja metode binerisasi untuk dataset Jawi dan HDIBCO-2016. AbstractAncient Jawi document is a cultural heritage, which contains knowledge of past civilization for developing a better future. Ancient Jawi document suffers from severe degradation due to some factors such as paper quality or poor retention process. The degradation reduces information on the document and thus the information is difficult to access. This paper proposed a binarization method for restoring the information from degraded ancient Jawi document. The proposed method combined a local and global thresholding method for extracting the text from the background. The experiment was conducted on ancient Jawi document and Handwritten Document Image Binarization Contest (HDIBCO) 2016 datasets. The result was evaluated using F-measure, pseudo F-measure, peak signal-to-noise ratio, distance reciprocal distortion, dan misclassification penalty metric. The average result showed that the proposed method achieved 88.18 and 89.04 of F-measure, for Jawi and HDIBCO-2016, respectively. The proposed method resulted in better performance compared with several benchmarking methods. It can be concluded that the proposed method succeeded to enhance binarization performance.
Metode Band-Limited Phase Only Correlation (BLPOC) untuk Identifikasi Plat Kendaraan Arnia, Fitri; Wahyudi, Syahrul; Aisyah, Siti
Jurnal Rekayasa Elektrika Vol 10, No 1 (2012)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1193.739 KB) | DOI: 10.17529/jre.v10i1.148

Abstract

Digital image processing and computer vision technologies have developed so rapidly and have numerous applications. Automatic lisence plate recognition systems (ALPRS) based on those technologies are not exceptions. In general, the ALPRSs required several steps including image capturing, plate location searching, character segmentation and character recognition. Successful of the whole systems depended heavily on the used segmentation method. A common drawback of many segmentation techniques is that they are very sensitive to illumination variability. The paper proposed a method for license plate recognition based on correlation of phase componenet with limited bandwidth. The method is widely known as band-limited phase only correlation (BLPOC). The method compared input plate’s image with plate’s images in the database. Based on simulation, detection rate can achieve 90% if an appropriate threshold value was selected.
Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code Abidin, Taufik Fuadi; AzZuhri, Abbas Adam; Arnia, Fitri
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.8932

Abstract

A license plate is one of the vehicle identities. It consists of alphabetic characters and numbers and represents provincial and area code where the vehicle is registered. This article discusses the character recognition of plate number using zoning and Freeman Chain Code (FCC). Zoning divides character image into several zones i.e. 4, 6, and 8, and then, the pattern of each character in the zone is extracted using FCC as the numerical features. The character is then classified using Support Vector Machines (SVM). It is a multi-class classification problem with 36 categories. The results show that FCC features with 8 zones give the best accuracy (87%) when compared to the other two zones.
Studi Pencocokan Plat Kendaraan Dengan Metode Phase Only Correlation Putri, Listia Sukma; Roslidar, Roslidar; Arnia, Fitri
Jurnal Rekayasa Elektrika Vol 9, No 4 (2011)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (650.171 KB)

Abstract

Salah satu cara pengenalan kendaraan adalah dengan identifikasi plat. Umumnya identifikasi yang dilakukan mengacu pada proses segmentasi tiap karakter dari citra plat. Makalah ini mengajukan suatu metode identifikasi plat yang sederhana tanpa melakukan pengenalan melainkan langsung pada proses pencocokan yang berbasis Phase Only Correlation (POC). POC mencocokkan plat dengan mengorelasikan fasa dari dua  citra plat. Fasa diperoleh dengan mengubah citra dari domain spasial menjadi domain frekuensi menggunakan Transformasi Fourier Waktu Diskrit (TFWD). Nilai puncak POC akan tinggi jika citra plat yang dicocokkan adalah citra yang berasal dari plat yang sama. Sebaliknya akan rendah jika yang dicocokkan berasal dari plat yang berbeda. Hasil simulasi menggunakan 20 citra plat menunjukkan bahwa metode POC dapat digunakan dalam pencocokan citra plat.
Penggunaan Gray Level Co-Occurance Matrix Dari Koefisien Aproksimasi Wavelet untuk Deteksi Cacat Tekstil Islamadina, Raihan; Arnia, Fitri; Munadi, Khairul
Jurnal Buana Informatika Vol 6, No 2 (2015): Jurnal Buana Informatika Volume 6 Nomor 2 April 2015
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.216 KB)

Abstract

Pendeteksian cacat tekstil saat ini masih dilakukan secara manualmengakibatkan seseorang sulit mendeteksi lebih dari 60% dari cacat yang ada.Untuk itu, penelitian ini menerapkan metode deteksi cacat tekstil secara otomatismenggunakan Gray Level Co-Occurance Matrix (GLCM) dari koefisienaproksimasi wavelet yang bertujuan untuk mengevaluasi analisis kinerja metode.Tahapannya, sampel citra tekstil dibagi menjadi delapan bagian untukmendapatkan tekstur cacat yang lebih jelas. Bagian tersebut didekomposisikedalam dua level. GLCM dihitung dari koefisien aproksimasi wavelet level satudan dua untuk dijadikan fitur. Penelitian ini dilakukan empat set simulasi citradengan orientasi latar berbeda. Setiap set terdiri dari satu citra noncacat dan duajenis citra cacat. Setiap bagian citra noncacat dihitung jaraknya dengan semuabagian pada citra cacat pertama dan kedua menggunakan jarak euclidean. Hasilsimulasi menunjukkan bahwa GLCM dari koefisien aproksimasi wavelet levelkedua mampu mendeteksi lebih dari 70% dari cacat yang ada.
Karakterisasi Kematangan Buah Kopi Berdasarkan Warna Kulit Kopi Menggunakan Histogram dan Momen Warna Syahputra, Hendri; Arnia, Fitri; Munadi, Khairul
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 1: March 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v8n1.615.2019

Abstract

Conventionally, the coffee maturity level is determined by observing the fruit colour, and it is done manually. This approach may result in inconsistency in colour classification. Thus, an automatic colour classification method based on colour of coffee maturity level is required. This paper presents the characterization of coffee maturity level based on two colour features: colour histogram and colour moment. Characterization of coffee maturity level was grouped into four class: green for unripe coffee, greenish-yellow for half ripe coffee, red for ripe coffee, and dark red for too ripe coffee. The purpose of the research is to determine the colour features that can characterize the coffee maturity level based on computer simulation in extracting and calculating the statistical values of the colour histogram and colour moments. It turned out from 200 coffee images that the statistical values of colour histogram are more suitable for characterising the coffee maturity. The kurtosis values of hue histogram for each maturity level of coffee were different: kurtosis value of unripe coffee was 17.2-28.3, those of half ripe coffee, ripe coffee and too ripe coffee were 29.2-31.4, 32.7-83.5, and more than 84.2 respectively..Keywords : colour histogram kurtosis, colour moment, image processing.AbstrakSecara tradisional, tingkat kematangan buah kopi ditentukan dari warna kulitnya yang dikelompokan secara manual. Cara ini menghasilkan pengelompokan warna yang kurang konsisten, sehingga diperlukan sebuah metode otomatis pengelompokan buah kopi berdasarkan warna dari tingkat kematangannya. Penelitian ini memaparkan hasil karakterisasi kematangan buah kopi arabika menggunakan dua fitur warna citra, yaitu histogram dan momen warna. Karakterisasi kematangan dibagi menjadi empat kelompok: hijau untuk kopi muda, hijau kekuningan untuk kopi setengah masak, merah untuk kopi masak, dan merah tua untuk kopi tua. Tujuan penelitian ini adalah menentukan fitur warna yang dapat mewakili karakter kematangan buah kopi dengan melakukan simulasi komputer untuk mengekstrak dan menghitung nilai statistik dari histogram warna dan nilai momen warna dari empat kelompok buah kopi.  Hasil penelitian menggunakan 200 citra kopi menunjukkan bahwa nilai statistik dari histogram warna lebih menggambarkan karakter kematangan buah kopi, dibandingkan dengan momen warna. Nilai kurtosis dari histogram hue memiliki nilai berbeda untuk setiap kategori kematangan buah kopi: kopi muda memiliki nilai kurtosis 17.2-28.3, kopi setengah masak 29.2-31.4, kopi masak 32.7-83.5dan kopi tua lebih dari 84.2.  Kata Kunci : kurtosis histogram warna, momen warna, pengolahan citra.