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Journal articles on the topic 'Linear Pattern Recognition'

To see the other types of publications on this topic, follow the link: Linear Pattern Recognition.

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Ordowski, Mark, and Gerard G. L. Meyer. "Geometric linear discriminant analysis for pattern recognition." Pattern Recognition 37, no. 3 (March 2004): 421–28. http://dx.doi.org/10.1016/j.patcog.2003.07.002.

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2

Eremin, I. I., V. D. Mazurov, and N. N. Astaf 'ev. "Linear inequalities in mathematical programming and pattern recognition." Ukrainian Mathematical Journal 40, no. 3 (1989): 243–51. http://dx.doi.org/10.1007/bf01061299.

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3

Li, Yang, Jian-Hui Jiang, Zeng-Ping Chen, Cheng-Jian Xu, and Ru-Qin Yu. "Robust linear discriminant analysis for chemical pattern recognition." Journal of Chemometrics 13, no. 1 (January 1999): 3–13. http://dx.doi.org/10.1002/(sici)1099-128x(199901/02)13:1<3::aid-cem524>3.0.co;2-r.

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4

Wu, Wei, Wei Qi Yuan, and Sen Lin. "An Instrument of Palm Vein Pattern Recognition." Applied Mechanics and Materials 333-335 (July 2013): 1092–95. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1092.

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Palm vein pattern recognition is one of the newest biometric techniques researched today. This paper presents a palm vein recognition instrument that uses blood vessel patterns as a personal identifying factor. The instrument uses the recognition algorithm of two dimensional Fisher linear discriminant for classification. The experiment has been done in a self-build palm vein database. Experimental results show that the designed instrument achieves an acceptable level of performance.

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5

Cooke, T. "Two variations on Fisher's linear discriminant for pattern recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 2 (2002): 268–73. http://dx.doi.org/10.1109/34.982904.

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6

Nieves, Juan L., Javier Hernández-Andrés, Eva Valero, and Javier Romero. "Spectral-reflectance linear models for optical color-pattern recognition." Applied Optics 43, no. 9 (March 19, 2004): 1880. http://dx.doi.org/10.1364/ao.43.001880.

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7

Reed, Stuart, and Jeremy Coupland. "Cascaded linear shift-invariant processors in optical pattern recognition." Applied Optics 40, no. 23 (August 10, 2001): 3843. http://dx.doi.org/10.1364/ao.40.003843.

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8

Hosgurmath, Sangamesh, Viswanatha Vanjre Mallappa, Nagaraj B. Patil, and Vishwanath Petli. "A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1468. http://dx.doi.org/10.11591/ijece.v12i2.pp1468-1476.

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Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

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9

Bobrowski, Leon. "Ranked linear models and sequential patterns recognition." Pattern Analysis and Applications 12, no. 1 (November 27, 2007): 1–7. http://dx.doi.org/10.1007/s10044-007-0092-8.

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10

Perez, C. A., G. D. Gonzalez, L. E. Medina, and F. J. Galdames. "Linear Versus Nonlinear Neural Modeling for 2-D Pattern Recognition." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 35, no. 6 (November 2005): 955–64. http://dx.doi.org/10.1109/tsmca.2005.851268.

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11

Wang, Jinfei, and Philip J. Howarth. "Structural Measures for Linear Feature Pattern Recognition from Satellite Imagery." Canadian Journal of Remote Sensing 17, no. 4 (October 1991): 294–303. http://dx.doi.org/10.1080/07038992.1991.10855298.

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12

Andriana, A. S., D. Prihatmanto, E. M. I. Hidaya, I. Supriana, and C. Machbub. "Contiguous Uniform Deviation for Multiple Linear Regression in Pattern Recognition." Journal of Physics: Conference Series 801 (January 2017): 012046. http://dx.doi.org/10.1088/1742-6596/801/1/012046.

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13

Moshou, Dimitrios, Bart De Keteiaere, Eis Vrindts, Patrik Kennes, Josse De Baerdemaeker, and Herman Ramon. "Local Linear Mapping Neural Networks for Pattern Recognition of Plants." IFAC Proceedings Volumes 31, no. 12 (June 1998): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)36042-1.

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14

Rahmati, Mohammad, and Laurence G. Hassebrook. "Intensity- and distortion-invariant pattern recognition with complex linear morphology." Pattern Recognition 27, no. 4 (April 1994): 549–68. http://dx.doi.org/10.1016/0031-3203(94)90036-1.

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15

Hu, H., P. Zhang, and F. De la Torre. "Face recognition using enhanced linear discriminant analysis." IET Computer Vision 4, no. 3 (2010): 195. http://dx.doi.org/10.1049/iet-cvi.2009.0024.

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16

LI, Z. C., Y. Y. TANG, T. D. BUI, and C. Y. SUEN. "SHAPE TRANSFORMATION MODELS AND THEIR APPLICATIONS IN PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 65–94. http://dx.doi.org/10.1142/s021800149000006x.

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This paper presents linear and bilinear shape transformations including basic transformations, analyzes their geometric properties, and provides computer algorithms. The shape transformations can be used to simplify the recognition of Roman letters, Chinese characters and other pictorial patterns by normalizing their shapes to the standard forms. Important theoretical analyses have been performed to illustrate that the linear and bilinear transformations are applicable to computer recognition of digitized patterns. A number of pictorial examples have been computed to confirm the analyses and conclusions made.

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17

Boulgouris, Nikolaos V., Konstantinos N. Plataniotis, and Dimitrios Hatzinakos. "Gait recognition using linear time normalization." Pattern Recognition 39, no. 5 (May 2006): 969–79. http://dx.doi.org/10.1016/j.patcog.2005.10.013.

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18

Cheng, Liang. "Pattern Recognition Controller Based on Fuzzy Neural Network." Advanced Materials Research 915-916 (April 2014): 1140–43. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1140.

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A class of fuzzy neural network design problem H controller. By TS fuzzy theory, a model of nonlinear complex systems. Then, based on Lyapunov-Krasovskii functional and LMI technique, gives the design an H controller. By using the Matlab LMI toolbox, we can get the corresponding feasible solution of linear matrix inequalities. Finally, a numerical simulation examples are given to prove the correctness of the H controller.

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19

Zang, Yiming, Yong Qian, Wei Liu, Yongpeng Xu, Gehao Sheng, and Xiuchen Jiang. "A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL." Energies 12, no. 21 (October 28, 2019): 4120. http://dx.doi.org/10.3390/en12214120.

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Optical detection and ultrahigh frequency (UHF) detection are two significant methods of partial discharge (PD) detection in the gas-insulated transmission lines (GIL), however, there is a phenomenon of signals loss when using two types of detections to monitor PD signals of different defects, such as needle defect and free particle defect. This makes the optical and UHF signals not correspond strictly to the actual PD signals, and therefore the characteristic information of optical PD patterns and UHF PD patterns is incomplete which reduces the accuracy of the pattern recognition. Therefore, an image fusion algorithm based on improved non-subsampled contourlet transform (NSCT) is proposed in this study. The optical pattern is fused with the UHF pattern to achieve the complementarity of the two detection methods, avoiding the PD signals loss of different defects. By constructing the experimental platform of optical-UHF integrated detection for GIL, phase-resolved partial discharge (PRPD) patterns of three defects were obtained. After that, the image fusion algorithm based on the local entropy and the phase congruency was used to produce the photoelectric fusion PD pattern. Before the pattern recognition, 28 characteristic parameters are extracted from the photoelectric fusion pattern, and then the dimension of the feature space is reduced to eight by the principal component analysis. Finally, three kinds of classifiers, including the linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN), are used for the pattern recognition. The results show that the recognition rate of all the photoelectric fusion pattern under different classifiers is higher than that of optical and UHF patterns, up to the maximum of 95%. Moreover, the photoelectric fusion pattern not only greatly improves the recognition rate of the needle defect and the free particle defect, but the recognition accuracy of the floating defect is also slightly improved.

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20

Yadav, Abhishek, and Sachin Mahajan. "Review on Face Detection and Recognition Technology Based on Neural Network." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 4, no. 2 (February 28, 2017): 01–07. http://dx.doi.org/10.53555/nncse.v4i2.400.

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In this paper, feature extraction and facial recognition are studied in order to resolve problems like highdimension problem, small size samples and no-linear separable problem that exist in facial recognition technology. In the part of feature extraction we use a HGPP algorithm, to extract the input features in building a face recognition system. The neural network, which represents brilliant performance on small training sets, non-linear separable and high-dimension pattern recognition problems in the recognition stage, is used for pattern classification. The proposed approach is validated with the ORL database. Experimental results demonstrate the effectiveness of this method in the performance of face recognition.

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21

Abbas Ahmed, Amjed. "Semantic Pattern Recognition Based on Linear Algebra and Latent Semanti Analysis." Diyala Journal For Pure Science 13, no. 1 (January 1, 2017): 154–65. http://dx.doi.org/10.24237/djps.1301.60a.

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22

Goudail, François, Vincent Laude, and Philippe Réfrégier. "Influence of nonoverlapping noise on regularized linear filters for pattern recognition." Optics Letters 20, no. 21 (November 1, 1995): 2237. http://dx.doi.org/10.1364/ol.20.002237.

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23

Ji, Nannan, Jiangshe Zhang, Chunxia Zhang, and Lei Wang. "Discriminative restricted Boltzmann machine for invariant pattern recognition with linear transformations." Pattern Recognition Letters 45 (August 2014): 172–80. http://dx.doi.org/10.1016/j.patrec.2014.03.022.

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24

González-Arjona, Domingo, and A. Gustavo González. "Adaptation of linear discriminant analysis to second level-pattern recognition classification." Analytica Chimica Acta 363, no. 1 (May 1998): 89–95. http://dx.doi.org/10.1016/s0003-2670(98)00075-0.

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25

de Moura, E. P., M. H. S. Siqueira, R. R. da Silva, J. M. A. Rebello, and L. P. Calôba. "Welding defect pattern recognition in TOFD signals Part 1. Linear classifiers." Insight - Non-Destructive Testing and Condition Monitoring 47, no. 12 (December 1, 2005): 777–82. http://dx.doi.org/10.1784/insi.2005.47.12.777.

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26

Aifanti, Niki, and Anastasios Delopoulos. "Linear subspaces for facial expression recognition." Signal Processing: Image Communication 29, no. 1 (January 2014): 177–88. http://dx.doi.org/10.1016/j.image.2013.10.004.

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27

Srivastava, Anuj, Xiuwen Liu, and Curt Hesher. "Face recognition using optimal linear components of range images." Image and Vision Computing 24, no. 3 (March 2006): 291–99. http://dx.doi.org/10.1016/j.imavis.2005.07.023.

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28

Li, Lu, Guo Qing Jiang, Tian Ye Niu, Yi Wang, Yong Lu, Qi Lan, Li Chang, Ya Lin Liu, and Chao Chen. "High Voltage Equipment PD Pattern Recognition Based on BP Classifier." Applied Mechanics and Materials 734 (February 2015): 99–103. http://dx.doi.org/10.4028/www.scientific.net/amm.734.99.

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The corresponding discharge waveforms were detected by ultrasonic sensor. The dimension of feature vectors extracted from discharge waveforms were reduced by local linear embedding algorithm. The processed vectors were used as input to train and test BP_Adaboost classifier. Recognition results show that, high voltage reactor insulating defects recognition with this method can reduce the calculation and maintain a high recognition rate at the same time. This shows its effectiveness in the application of partial discharge pattern recognition.

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29

SANCHEZ, JUAN R. "PATTERN RECOGNITION OF ONE-DIMENSIONAL CELLULAR AUTOMATA USING MARKOV CHAINS." International Journal of Modern Physics C 15, no. 04 (May 2004): 563–67. http://dx.doi.org/10.1142/s0129183104006029.

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A technique is presented for the identification of rule that generates a given complex pattern of linear one-dimensional cellular automata (LCA). The technique is based on the construction of a Markov transition matrix for the Markov chains that correspond to the evolution of the automaton. Such chain is generated by the evolution of a sequence of symbols representing the value of a string composed by small portion of the sites of the automaton. Excellent results are obtained for the identification of the rules that generate different complex patterns.

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30

Hannagan, Thomas, Frédéric Dandurand, and Jonathan Grainger. "Broken Symmetries in a Location-Invariant Word Recognition Network." Neural Computation 23, no. 1 (January 2011): 251–83. http://dx.doi.org/10.1162/neco_a_00064.

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We studied the feedforward network proposed by Dandurand et al. ( 2010 ), which maps location-specific letter inputs to location-invariant word outputs, probing the hidden layer to determine the nature of the code. Hidden patterns for words were densely distributed, and K-means clustering on single letter patterns produced evidence that the network had formed semi-location-invariant letter representations during training. The possible confound with superseding bigram representations was ruled out, and linear regressions showed that any word pattern was well approximated by a linear combination of its constituent letter patterns. Emulating this code using overlapping holographic representations (Plate, 1995 ) uncovered a surprisingly acute and useful correspondence with the network, stemming from a broken symmetry in the connection weight matrix and related to the group-invariance theorem (Minsky & Papert, 1969 ). These results also explain how the network can reproduce relative and transposition priming effects found in humans.

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31

Quyoom, Abdul. "A Novel Mechanism of Face Recognition Using Stepwise Linear Discriminant Analysis and Linear Vector Quantization Classifiers." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 29, 2017): 48. http://dx.doi.org/10.23956/ijarcsse.v7i7.96.

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Face recognition is a hard and special case of computer vision and pattern recognition. It is a challenging problem due to various kinds of variations of face images. This paper proposes a robust face recognition system. Here stepwise linear discriminant analysis (SWLDA) is used for the feature extraction and Linear Vector Quantization (LVQ) Classifier is used for face recognition. The main focus of SWLDA is to select localized features from the face. In order to increase the low-between-class variance and to reduce within-class-variance among different expression classes and use F-test value through which results are analyzed. In recognition, firstly face is detected using canny edge detection method, after face detection SWLDA is employed to extract the face features, and end linear vector quantization is applied for face recognition. To achieve optimum results and increase the robustness of the proposed system, experiments are performed on various different samples of face image, which consist of face image with the different pose and facial expression in order to validate the system, we use two famous datasets which include Yale and ORL face database.

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32

Wang, Haichuan, and Liming Zhang. "Linear generalization probe samples for face recognition." Pattern Recognition Letters 25, no. 8 (June 2004): 829–40. http://dx.doi.org/10.1016/j.patrec.2004.01.016.

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33

Song, Fengxi, Jingyu Yang, and Shuhai Liu. "Large margin linear projection and face recognition." Pattern Recognition 37, no. 9 (September 2004): 1953–55. http://dx.doi.org/10.1016/j.patcog.2004.01.016.

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34

Ullman, S., and R. Basri. "Recognition by linear combinations of models." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 10 (1991): 992–1006. http://dx.doi.org/10.1109/34.99234.

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35

Chowdhury, M., A. Alouani, and F. Hossain. "How much does inclusion of non-linearity and multi-point pattern recognition improve the spatial mapping of complex patterns of groundwater contamination?" Nonlinear Processes in Geophysics 16, no. 2 (April 15, 2009): 313–17. http://dx.doi.org/10.5194/npg-16-313-2009.

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Abstract. In this brief communication, we discuss the implication of the hypothesis that "non-linearity and multi-point pattern recognition can improve the spatial mapping of complex patterns of groundwater contamination". The discussion is based on our recently published work in Stochastic Environmental Research and Risk Assessment. Therein we have found that the use of a highly non-linear pattern learning technique in the form of an artificial neural network (ANN) can yield significantly superior results under the same set of constraints when compared to the more linear two-point ordinary kriging method.

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36

Elmannai, H., M. A. Loghmari, and M. S. Naceur. "TWO LEVELS FUSION DECISION FOR MULTISPECTRAL IMAGE PATTERN RECOGNITION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 69–74. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-69-2015.

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Major goal of multispectral data analysis is land cover classification and related applications. The dimension drawback leads to a small ratio of the remote sensing training data compared to the number of features. Therefore robust methods should be associated to overcome the dimensionality curse. The presented work proposed a pattern recognition approach. Source separation, feature extraction and decisional fusion are the main stages to establish an automatic pattern recognizer. <br><br> The first stage is pre-processing and is based on non linear source separation. The mixing process is considered non linear with gaussians distributions. The second stage performs feature extraction for Gabor, Wavelet and Curvelet transform. Feature information presentation provides an efficient information description for machine vision projects. <br><br> The third stage is a decisional fusion performed in two steps. The first step assign the best feature to each source/pattern using the accuracy matrix obtained from the learning data set. The second step is a source majority vote. Classification is performed by Support Vector Machine. Experimentation results show that the proposed fusion method enhances the classification accuracy and provide powerful tool for pattern recognition.

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37

Mi, Jian‐Xun, and Tao Liu. "Multi‐step linear representation‐based classification for face recognition." IET Computer Vision 10, no. 8 (June 16, 2016): 836–41. http://dx.doi.org/10.1049/iet-cvi.2015.0462.

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38

He, Ran, Wei-Shi Zheng, Bao-Gang Hu, and Xiang-Wei Kong. "A Regularized Correntropy Framework for Robust Pattern Recognition." Neural Computation 23, no. 8 (August 2011): 2074–100. http://dx.doi.org/10.1162/neco_a_00155.

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This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.

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39

Batur, A. U., and M. H. Hayes III. "Segmented Linear Subspaces for Illumination-Robust Face Recognition." International Journal of Computer Vision 57, no. 1 (April 2004): 49–66. http://dx.doi.org/10.1023/b:visi.0000013090.39095.d5.

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40

Lin, Chuang, Binghui Wang, Xin Fan, Yanchun Ma, and Huiyun Liu. "Orthogonal enhanced linear discriminant analysis for face recognition." IET Biometrics 5, no. 2 (June 1, 2016): 100–110. http://dx.doi.org/10.1049/iet-bmt.2014.0086.

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41

Niu, Guo, and Zhengming Ma. "Local non‐linear alignment for non‐linear dimensionality reduction." IET Computer Vision 11, no. 5 (July 6, 2017): 331–41. http://dx.doi.org/10.1049/iet-cvi.2015.0441.

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42

Wu, Xuegang, Bin Fang, Yuan Yan Tang, Xiaoping Zeng, and Changyuan Xing. "Reconstructed Error and Linear Representation Coefficients Restricted by l1-Minimization for Face Recognition under Different Illumination and Occlusion." Mathematical Problems in Engineering 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/1458412.

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The problem of recognizing human faces from frontal views with varying illumination, occlusion, and disguise is a great challenge to pattern recognition. A general knowledge is that face patterns from an objective set sit on a linear subspace. On the proof of the knowledge, some methods use the linear combination to represent a sample in face recognition. In this paper, in order to get the more discriminant information of reconstruction error, we constrain both the linear combination coefficients and the reconstruction error by l1-minimization which is not apt to be disturbed by outliners. Then, through an equivalent transformation of the model, it is convenient to compute the parameters in a new underdetermined linear system. Next, we use an optimization method to get the approximate solution. As a result, the minimum reconstruction error has contained much valuable discriminating information. The gradient of this variable is measured to decide the final recognition. The experiments show that the recognition protocol based on the reconstruction error achieves high performance on available databases (Extended Yale B and AR Face database).

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43

O, Nixon, and Arokia Nathan. "Magnetic pattern recognition sensor arrays using CCD readout." Canadian Journal of Physics 74, S1 (December 1, 1996): 143–46. http://dx.doi.org/10.1139/p96-848.

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A magnetic sensor array has been designed to overcome the limitations of technologies currently employed in the retrieval of magnetically encoded information. The array is reminiscent of a linear photodiode CCD, with the exception that the photodiodes have been replaced by buried-channel MOS magnetic sensors. The use of buried-channel MOS sensor elements results in lower noise and higher sensitivity. Noise is lower because of the absence of carrier-oxide interaction. Sensitivity, a parameter proportional to Hall mobility, is enhanced because of the absence of surface scattering. Sensitivity is further enhanced by employing multiple-gate structures, which exploit the dependence of Hall mobility on electric field to increase magnetic sensitivity. Characterization and numerical modelling results of the noise and sensitivity properties of the MOS sensor element are presented.

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44

Kim, Jaesoo, Alistair Mowat, Philip Poole, and Nikola Kasabov. "Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra." Chemometrics and Intelligent Laboratory Systems 51, no. 2 (July 2000): 201–16. http://dx.doi.org/10.1016/s0169-7439(00)00070-8.

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45

Velmurugan, S., and S. Selvarajan. "Linear Binary Pattern Based Biometric Recognition Using Hand Geometry And Iris Images." International Journal of Applied Engineering Research 10, no. 24 (December 30, 2015): 45675. http://dx.doi.org/10.37622/ijaer/10.24.2015.45675-45683.

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46

Rajput, Satyendra, and Joyti Bharti. "A Face Recognition Using Linear-Diagonal Binary Graph Pattern Feature Extraction Method." International Journal in Foundations of Computer Science & Technology 6, no. 2 (March 31, 2016): 55–65. http://dx.doi.org/10.5121/ijfcst.2016.6205.

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47

Vijaya Kumar, Bhagavatula. "Linear phase coefficient composite filter banks for distortion-invariant optical pattern recognition." Optical Engineering 29, no. 9 (1990): 1033. http://dx.doi.org/10.1117/12.55699.

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48

Karanikas, C., and G. Proios. "A non-linear discrete transform for pattern recognition of discrete chaotic systems☆." Chaos, Solitons & Fractals 17, no. 2-3 (July 2003): 195–201. http://dx.doi.org/10.1016/s0960-0779(02)00341-7.

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49

Millán, Marı́a S., Elisabet Pérez, and Katarzyna Chalasinska-Macukow. "Pattern recognition with variable discrimination capability by dual non-linear optical correlation." Optics Communications 161, no. 1-3 (March 1999): 115–22. http://dx.doi.org/10.1016/s0030-4018(99)00002-4.

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50

de Moura, E. P., M. H. S. Siqueira, R. R. da Silva, and J. M. A. Rebello. "Welding defect pattern recognition in TOFD signals Part 2. Non-linear classifiers." Insight - Non-Destructive Testing and Condition Monitoring 47, no. 12 (December 1, 2005): 783–87. http://dx.doi.org/10.1784/insi.2005.47.12.783.

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