Browsing by Author "Pearson, T."

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    Fast insect damage detection in wheat kernels using transmittance images
    (IEEE, 2004-07) Çataltepe, Z.; Pearson, T.; Cetin, A. Enis
    We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.12 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.90 ± 0.02. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93 ± 0.03.
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    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.
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    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.
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    Identification of insect damaged wheat kernels using transmittance images
    (The Institution of Engineering and Technology, 2005) Cataltepe, Z.; Çetin, A. Enis; Pearson, T.
    Transmittance images of wheat kernels are used to classify insect damaged and undamaged wheat kernels. The histogram of pixel intensities of the wheat images were used as the features. Combination of the linear model and a radial basis function network in a committee resulted in a false positive rate of 0.1 at the true positive rate of 0.8 and an area under the receiver operating characteristics curve of 0.92.
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    Identification of insect damaged wheat kernels using transmittance images
    (IEEE, 2004) Çataltepe, Z.; Çetin, A. Enis; Pearson, T.
    We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.2 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.86. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.1 at the TP Rate of 0.8 and an AUC of 0.92.
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    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
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    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%.