Browsing by Author "Tewfik, A. H."

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Open Access
Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics
(IEEE, 2004-05) Çetin, A. Enis; Pearson, T. C.; Tewfik, A. H.
An algorithm was developed to separate pistachio nuts with closed-shells from those with open-shells. It was observed that upon impact on a steel plate, nuts with closed-shells emit different sounds than nuts with open-shells. Two feature vectors extracted from the sound signals were melcepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed-shells and open-shells were used to obtain a representative vector of each class. The accuracy of closed-shell nuts was more than 99% on the test set.
Open Access
Classification of closed-and open-shell pistachio nuts using voice-recognition technology
(American Society of Agricultural and Biological Engineers, 2004) Çetin, A. Enis; Pearson, T. C.; Tewfik, A. H.
An algorithm using speech recognition technology was developed to distinguish pistachio nuts with closed shells from those with open shells. It was observed that upon impact with a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Features extracted from the sound signals consisted of mel-cepstrum coefficients and eigenvalues obtained from the principle component analysis (PCA) of the autocorrelation matrix of the sound signals. Classification of a sound signal was performed by linearly combining the mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable, as are most speech-recognition algorithms. During the training phase, sounds of nuts with closed shells and with open shells were used to obtain a representative vector of each class. During the recognition phase, the feature vector from the sample under question was compared with representative vectors. The classification accuracy of closed-shell nuts was more than 99% on the validation set, which did not include the training set.
Open Access
Detection of insect damaged wheat kernels by impact acoustics
(IEEE, 2005-03) Pearson, T. C.; Çetin, A. Enis; Tewfik, A. H.
Insect damaged wheat kernels (IDK) are characterized by a small hole bored into the kernel by insect larvae. This damage decreases flour quality as insect proteins interfere with the bread-making biochemistry and insect fragments are very unsightly. A prototype system was set up to detect IDK by dropping them onto a steel plate and processing the acoustic signal generated when kernels impact the plate. The acoustic signal was processed by three different methods: 1) modeling of the signal in the time domain, 2) computing time domain signal variances in short time windows, and 3), analysis of the frequency spectra magnitudes. Linear discriminant analysis was used to select a subset of features and perform classification. 98% of un-damaged kernels and 84.4% of IDK were correctly classified.
Open Access
Discrimination between closed-and open-shell (Turkish) pistachio nuts using undecimated wavelet packet transform
(American Society of Agricultural and Biological Engineers, 2008) Ince, N. F.; Goksu, F.; Tewfik, A. H.; Onaran, I.; Çetin, A. Enis; Pearson, T. C.
Due to low consumer acceptance and the possibility of immature kernels, closed-shell pistachio nuts should be separated from open-shell nuts before reaching the consumer. A system using impact acoustics as a means of classifying closed-shell nuts from open-shell nuts has already been shown to be feasible and have better discrimination performance than a mechanical system. The accuracy of an impact acoustics based system is determined by the signal processing and feature extraction procedures. In this article, a new time-frequency plain feature extraction and classification algorithm was developed to discriminate between open- and closed-shell pistachio nuts produced in the Gaziantep region of Turkey. The proposed approach relies on the analysis of the impact acoustics signal of pistachio nuts, which are emitted from their impact with a steel plate after dropping from a certain height. Features are extracted by decomposing the acoustic signals into time and frequency components, using double-tree undecimated wavelet packet transform. The most discriminative features from the dual tree nodes are selected by a wrapper strategy that includes the structural pruning of the double-tree feature dictionary. The proposed approach requires no prior knowledge of the relevant time or frequency content of the acoustic signals. The algorithm used a small number of features and achieved a classification accuracy of 91.7% on the validation data set, while separating the closed shells from the open ones. A previously implemented algorithm, which uses maximum signal amplitude, absolute integration, and gradient features, achieved 82% classification accuracy on the same dataset. The results show that the time-frequency features extracted from impact acoustics can be used successfully for classification of open- and closed-shell Turkish pistachios.
Open Access
Feasibility of impact-acoustic emissions for detection of damaged wheat kernels
(Elsevier BV, 2007-05) Pearson, T.; Çetin, A. Enis; Tewfik, A. H.; Haff, R. P.
A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.
Open Access
Identification of damaged wheat kernels and cracked-shell hazelnuts with impact acoustics time-frequency patterns
(American Society of Agricultural and Biological Engineers, 2008) Ince, N. F.; Onaran, I.; Pearson, T.; Tewfik, A. H.; Çetin, A. Enis; Kalkan, H.; Yardimci, Y.
A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with a PC-based data acquisition system. These signals were segmented with a flexible local discriminant bases (F-LDB) procedure in the time-frequency plane to extract discriminative patterns between damaged and undamaged food kernels. The F-LDB procedure requires no prior knowledge of the relevant time or frequency indices of the impact acoustics signals for classification. The method automatically finds all crucial time-frequency indices from the training data by combining local cosine packet analysis and a frequency axis clustering approach, which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post-processed by principal component analysis, and fed to a linear discriminant classifier. Experimental results establish the superior performance of the proposed approach when compared to prior techniques reported in the literature or used in the field. The new approach separated damaged wheat kernels (IDK, pupal, and scab) from undamaged wheat kernels with 96%, 82%, and 94% accuracy, respectively. It also separated cracked-shell hazelnuts from those with undamaged shells with 97.1% accuracy. The adaptation capability of the algorithm to the time-frequency patterns of signals makes it a universal method for food kernel inspection that can resist the impact acoustic variability between different kernel and damage types.
Open Access
An overview of signal processing for food inspection
(Institute of Electrical and Electronics Engineers, 2007-05) Pearson, T.; Çetin, A. Enis; Tewfik, A. H.; Gokmen, V.
The goal of this article is to introduce the signal processing community to the challenges that arise in food inspection. We briefly describe both traditional food-inspection technologies, which rely on sample collection and subsequent offline analysis in a laboratory, and newer approaches that use nondestructive methods to measure various quality parameters of food products in real time. We focus on four specific examples to illustrate the breadth of technologies currently in use in food inspection and the challenges that remain to be addressed. In each case, we describe the problem setting and its economic and health aspects; the techniques that are used, including the physical principles on which these techniques are based; and their performance and cost
Open Access
Subband domain coding of binary textual images for document archiving
(Institute of Electrical and Electronics Engineers, 1999-10) Gerek, Ö. N.; Çetin, A. Enis; Tewfik, A. H.; Atalay, V.
In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented.
Open Access
Subset selection with structured dictionaries in classification
(EURASIP, 2007) İnce, N. F.; Göksu, F.; Tewfik, A. H.; Onaran, İbrahim; Çetin, A. Enis
This paper describes a new approach for the selection of discriminant time-frequency features for classification. Unlike previous approaches that use the individual discrimination power of expansion coefficients, the proposed approach selects a subset of features by implementing a classifier directed pruning of an initial redundant set of candidate features. The candidate features are calculated from a structured redundant time-frequency analysis of the signal, such as an undecimated wavelet transform. We show that the proposed approach has a performance that is as good as or better than traditional classification approaches while using a much smaller number of features. In particular, we provide experimental results to demonstrate the superior performance of the algorithm in the area of impact acoustic classification for food kernel inspection. The proposed algorithm achieved 91.8% and 98.5% classification accuracies in separating open shell from closed shell pistachio nuts and discriminating between empty and full hazelnuts respectively. Traditional methods used in this area resulted in 82% and 97% classification accuracies respectively.
Open Access
The Topkapi Palace Museum
(Wiley-Blackwell Publishing Ltd., 2000) Çetin, A. Enis; Gerek, O. N.; Tewfik, A. H.
A city‐like ensemble overlooking the Marmara Sea and the Bosphorus, Istanbul’s legendary Topkapi Palace became a synonym for Ottoman history and the heart of a far‐flung empire for 400 years. Transformed into a museum, it houses a vast and disparate treasure. Presenting both the splendours of the palace and the richness of its collections on the Internet was the challenge described below. The authors, all with backgrounds in electrical engineering, are professionally involved in areas of advanced research in the new technologies. After having served as visiting associate professor at the University of Minnesota in the United States, A. Enis Cetin is a professor at Bilkent University in Ankara and chairman of the IEEE (Institute of Electrical and Electronics Engineers)‐EURASIP (European Association for Signal Processing) Nonlinear Signal and Image Processing Workshop, held in Ankara in June 1999. Omer N. Gerek is currently with the Signal Processing Laboratory of the Swiss Federal Institute of Technology‐EPFL in Lausanne (on leave from the Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir, Turkey). Ahmed H. Tewfik is a professor of electronic communications at the University of Minnesota and was the first Editor‐in‐Chief of the IEEE Signal Processing Letters in 1993.
Open Access
Wavelet domain textual coding of Ottoman script images
(SPIE, 1996-03) Gerek, Ömer. N.; Çetin, A. Enis; Tewfik, A. H.
Image coding using wavelet transform, DCT, and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters This method efficiently compresses document images and is suitable for database research, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that are described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.
Open Access
Wheat and hazelnut inspection with impact acoustics time-frequency patterns
(ASABE, 2007-06) İnce, N. F.; Onaran, İbrahim; Tewfik, A. H.; Kalkan, H.; Pearson, T.; Çetin, A. Enis; Yardimci, Y.
Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%.