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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image Arif Muntasa; Indah Agustien Sirajudin; Moch Kautsar Sophan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 3: September 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i3.97

Abstract

Diabetic Retinopathy is one of the diseases that have the effect of a high mortality rate after heart disease and cancer.  However, the disease can be early detected through blood vessels and the optic nerve head in Fundus images. Blood vessels separation of the optic nerve head required high effort when it is conducted manually, therefore it is necessary that the appropriate method to perform segmentation of the object. Level Set method is well-known as object segmentation method based on object deformable. However, the methods have the disadvantage; it requires initialization before the segmentation process. In this research, segmentation method without initialization process is proposed. The segmentation is conducted by using the maximum value selection results of convolution 8 directions. Experimental results show that, proposed method has obtained 89.48% accuracy. Segmentation errors are caused by small branches, where they are not connected, so that the objects are supposed as noises
Appearance Global and Local Structure Fusion for Face Image Recognition Arif Muntasa; Indah Agustien Sirajudin; Mauridhi Hery Purnomo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 1: April 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v9i1.678

Abstract

Principal component analysis (PCA) and linear descriminant analysis (LDA) are an extraction method based on appearance with the global structure features. The global structure features have a weakness; that is the local structure features can not be characterized. Whereas locality preserving projection (LPP) and orthogonal laplacianfaces (OLF) methods are an appearance extraction with the local structure features, but the global structure features are ignored. For both the global and the local structure features are very important. Feature extraction by using the global or the local structures is not enough. In this research, it is proposed to fuse the global and the local structure features based on appearance. The extraction results of PCA and LDA methods are fused to the extraction results of LPP. Modelling results were tested on the Olivetty Research Laboratory database face images. The experimental results show that our proposed method has achieved higher recognation rate than PCA, LDA, LPP and OLF Methods.
Multi-Criteria in Discriminant Analysis to Find the Dominant Features Arif Muntasa; Indah Agustien Siradjuddin; Rima Tri Wahyuningrum
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3472

Abstract

A crucial problem in biometrics is enormous dimensionality. It will have an impact on the costs involved. Therefore, the feature extraction plays a significant role in biometrics computational. In this research, a novel approach to extract the features is proposed for facial image recognition. Four criteria of the Discriminant Analysis have been modeled to find the dominant features. For each criterion is an objective function, it was derived to obtain the optimum values. The optimum values can be solved by using generalized the Eigenvalue problem associated to the largest Eigenvalue. The modeling results were employed to recognize the facial image by the multi-criteria projection to the original data. The training sets were also processed by using the Eigenface projection to avoid the singularity problem cases. The similarity measurements were performed by using four different methods, i.e. Euclidian Distance, Manhattan, Chebyshev, and Canberra.  Feature extraction and analysis results using multi-criteria have shown better results than the other appearance method, i.e. Eigenface (PCA), Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal Locality Preserving Projection or O-LPP). 
Contradictory of the Laplacian Smoothing Transform and Linear Discriminant Analysis Modeling to Extract the Face Image Features Arif Muntasa; Indah Agustien Siradjuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6576

Abstract

Laplacian smoothing transform uses the negative diagonal element to generate the new space. The negative diagonal elements will deliver the negative new spaces. The negative new spaces will cause decreasing of the dominant characteristics. Laplacian smoothing transform usually singular matrix, such that the matrix cannot be solved to obtain the ordered-eigenvalues and corresponding eigenvectors. In this research, we propose a modeling to generate the positive diagonal elements to obtain the positive new spaces. The secondly, we propose approach to overcome singularity matrix to found eigenvalues and eigenvectors. Firstly, the method is started to calculate contradictory of the laplacian smoothing matrix. Secondly, we calculate the new space modeling on the contradictory of the laplacian smoothing. Moreover, we calculate eigenvectors of the discriminant analysis. Fourth, we calculate the new space modeling on the discriminant analysis, select and merge features. The proposed method has been tested by using four databases, i.e. ORL, YALE, UoB, and local database (CAI-UTM). Overall, the results indicate that the proposed method can overcome two problems and deliver higher accuracy than similar methods. 
New Modelling of Modified Two Dimensional Fisherface Based Feature Extraction Arif Muntasa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 1: March 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i1.20

Abstract

Biometric researches have been interesting field for many researches included facial recognition. Crucial process of facial recognition is feature extraction. One Dimensional Linear Discriminant Analysis is one of feature extraction method is development of Principal Component Analysis mostly used by researches. But, it has limitation, it can efficiently work when number of training sets greater or equal than number of dimensions of image training set. This limitation has been overcome by using Two Dimensional Linear Discriminant Analysis. However, search value of matrix identity R and L by using Two Dimensional Linear Discriminant Analysis takes high cost, which is O(n3). In this research, the seeking of “Scatter between Class” and “Scatter within Class” by using Discriminant Analysis without having to find the value of R and L advance are proposed. Time complexity of proposed method is O(n2). Proposed method has been tested by using AT&T face image database. The experimental results show that maximum recognition rate of proposed method is 100%.