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2016 | OriginalPaper | Chapter

Detection and Description of Image Features: An Introduction

Authors : M. Hassaballah, Ali Ismail Awad

Published in: Image Feature Detectors and Descriptors

Publisher: Springer International Publishing

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Abstract

Detection and description of image features play a vital role in various application domains such as image processing, computer vision, pattern recognition, and machine learning. There are two type of features that can be extracted from an image content; namely global and local features. Global features describe the image as a whole and can be interpreted as a particular property of the image involving all pixels; while, the local features aim to detect keypoints within the image and describe regions around these keypoints. After extracting the features and their descriptors from images, matching of common structures between images (i.e., features matching) is the next step for these applications. This chapter presents a general and brief introduction to topics of feature extraction for a variety of application domains. Its main aim is to provide short descriptions of the chapters included in this book volume.

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Literature
1.
go back to reference Klette, R.: Concise Computer Vision: An introduction into Theory and Algorithms. Springer, USA (2014)CrossRef Klette, R.: Concise Computer Vision: An introduction into Theory and Algorithms. Springer, USA (2014)CrossRef
2.
go back to reference Raxle, C.-C.: Automatic vehicle detection using local features-a statistical approach. IEEE Trans. Intell. Transp. Syst. 9(1), 83–96 (2008)CrossRef Raxle, C.-C.: Automatic vehicle detection using local features-a statistical approach. IEEE Trans. Intell. Transp. Syst. 9(1), 83–96 (2008)CrossRef
3.
go back to reference Mukhtar, A., Likun, X.: Vehicle detection techniques for collision avoidance systems: A review. IEEE Trans. Intell. Transp. Syst. 16(5), 2318–2338 (2015)CrossRef Mukhtar, A., Likun, X.: Vehicle detection techniques for collision avoidance systems: A review. IEEE Trans. Intell. Transp. Syst. 16(5), 2318–2338 (2015)CrossRef
4.
go back to reference Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)CrossRef Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)CrossRef
5.
go back to reference Da-Wen, S.: Computer Vision Technology for Food Quality Evaluation. Academic Press, Elsevier (2008) Da-Wen, S.: Computer Vision Technology for Food Quality Evaluation. Academic Press, Elsevier (2008)
6.
go back to reference Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A.: Medical computer vision: recognition techniques and applications in medical imaging. LNCS 7766 (2013) Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A.: Medical computer vision: recognition techniques and applications in medical imaging. LNCS 7766 (2013)
7.
go back to reference Koen, E., Gevers, T., Snoek, G.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRef Koen, E., Gevers, T., Snoek, G.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRef
8.
go back to reference Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, USA (2011)CrossRef Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, USA (2011)CrossRef
9.
go back to reference Chen, Z., Sun, S.K.: A Zernike moment phase-based descriptor for local image representation and matching. IEEE Trans. Image Process. 19(1), 205–219 (2010)CrossRefMathSciNet Chen, Z., Sun, S.K.: A Zernike moment phase-based descriptor for local image representation and matching. IEEE Trans. Image Process. 19(1), 205–219 (2010)CrossRefMathSciNet
10.
go back to reference Andreopoulos, A., Tsotsos, J.: 50 years of object recognition: directions forward. Comput. Vis. Image Underst. 117(8), 827–891 (2013)CrossRef Andreopoulos, A., Tsotsos, J.: 50 years of object recognition: directions forward. Comput. Vis. Image Underst. 117(8), 827–891 (2013)CrossRef
11.
go back to reference Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)CrossRef Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)CrossRef
12.
go back to reference Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. Int. J. Comput. Vis. 73(3), 263–284 (2007)CrossRef Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. Int. J. Comput. Vis. 73(3), 263–284 (2007)CrossRef
13.
go back to reference Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, USA (2007) Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, USA (2007)
14.
go back to reference John, C.R.: The Image Processing Handbook, 6 edn. CRC Press, Taylor & Francis Group, USA (2011) John, C.R.: The Image Processing Handbook, 6 edn. CRC Press, Taylor & Francis Group, USA (2011)
15.
go back to reference Li, J., Allinson, N.: A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12), 1771–1787 (2008)CrossRef Li, J., Allinson, N.: A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12), 1771–1787 (2008)CrossRef
16.
go back to reference Liu, S., Bai, X.: Discriminative features for image classification and retrieval. Pattern Recogn. Lett. 33(6), 744–751 (2012)CrossRef Liu, S., Bai, X.: Discriminative features for image classification and retrieval. Pattern Recogn. Lett. 33(6), 744–751 (2012)CrossRef
17.
go back to reference Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2007)CrossRef Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2007)CrossRef
18.
go back to reference Mainali, P., Lafruit, G., Yang, Q., Geelen, B., Gool, L.V., Lauwereins, R.: SIFER: Scale-invariant feature detector with error resilience. Int. J. Comput. Vis. 104(2), 172–197 (2013)CrossRefMATH Mainali, P., Lafruit, G., Yang, Q., Geelen, B., Gool, L.V., Lauwereins, R.: SIFER: Scale-invariant feature detector with error resilience. Int. J. Comput. Vis. 104(2), 172–197 (2013)CrossRefMATH
19.
go back to reference Zhang, Y., Tian, T., Tian, J., Gong, J., Ming, D.: A novel biologically inspired local feature descriptor. Biol. Cybern. 108(3), 275–290 (2014)CrossRef Zhang, Y., Tian, T., Tian, J., Gong, J., Ming, D.: A novel biologically inspired local feature descriptor. Biol. Cybern. 108(3), 275–290 (2014)CrossRef
20.
go back to reference Hugo, P.: Performance evaluation of keypoint detection and matching techniques on grayscale data. SIViP 9(5), 1009–1019 (2015)CrossRef Hugo, P.: Performance evaluation of keypoint detection and matching techniques on grayscale data. SIViP 9(5), 1009–1019 (2015)CrossRef
21.
go back to reference Bouchiha, R., Besbes, K.: Comparison of local descriptors for automatic remote sensing image registration. SIViP 9(2), 463–469 (2015)CrossRef Bouchiha, R., Besbes, K.: Comparison of local descriptors for automatic remote sensing image registration. SIViP 9(2), 463–469 (2015)CrossRef
22.
go back to reference Bianco, S., Mazzini, D., Pau, D., Schettini, R.: Local detectors and compact descriptors for visual search: a quantitative comparison. Digital Sig. Process 44, 1–13 (2015)CrossRef Bianco, S., Mazzini, D., Pau, D., Schettini, R.: Local detectors and compact descriptors for visual search: a quantitative comparison. Digital Sig. Process 44, 1–13 (2015)CrossRef
23.
go back to reference Takacs, G., Chandrasekhar, V., Tsai, S., Chen, D., Grzeszczuk, R., Girod, B.: Rotation-invariant fast features for large-scale recognition and real-time tracking. Sig. Process: Image Commun. 28(4), 334–344 (2013) Takacs, G., Chandrasekhar, V., Tsai, S., Chen, D., Grzeszczuk, R., Girod, B.: Rotation-invariant fast features for large-scale recognition and real-time tracking. Sig. Process: Image Commun. 28(4), 334–344 (2013)
24.
go back to reference Seidenari, L., Serra, G., Bagdanov, A., Del Bimbo, A.: Local pyramidal descriptors for image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1033–1040 (2014)CrossRef Seidenari, L., Serra, G., Bagdanov, A., Del Bimbo, A.: Local pyramidal descriptors for image recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1033–1040 (2014)CrossRef
25.
go back to reference Morevec, H.P.: Towards automatic visual obstacle avoidance. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, vol. 2, pp. 584–584. IJCAI’77. Morgan Kaufmann Publishers Inc., San Francisco (1977) Morevec, H.P.: Towards automatic visual obstacle avoidance. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, vol. 2, pp. 584–584. IJCAI’77. Morgan Kaufmann Publishers Inc., San Francisco (1977)
26.
go back to reference Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of the Fourth Alvey Vision Conference, pp. 147–151 (1988) Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of the Fourth Alvey Vision Conference, pp. 147–151 (1988)
27.
go back to reference Smith, S., Brady, J.: Susan-a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)CrossRef Smith, S., Brady, J.: Susan-a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)CrossRef
28.
go back to reference Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1515. ICCV’05, IEEE Computer Society, Washington, DC (2005) Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1515. ICCV’05, IEEE Computer Society, Washington, DC (2005)
29.
go back to reference Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef
30.
go back to reference Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef
31.
go back to reference Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000)CrossRefMATH Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000)CrossRefMATH
32.
go back to reference Possa, P., Mahmoudi, S., Harb, N., Valderrama, C., Manneback, P.: A multi-resolution FPGA-Based architecture for real-time edge and corner detection. IEEE Trans. Comput. 63(10), 2376–2388 (2014)CrossRefMathSciNet Possa, P., Mahmoudi, S., Harb, N., Valderrama, C., Manneback, P.: A multi-resolution FPGA-Based architecture for real-time edge and corner detection. IEEE Trans. Comput. 63(10), 2376–2388 (2014)CrossRefMathSciNet
33.
go back to reference Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics, 1st edn.Springer (2011) Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics, 1st edn.Springer (2011)
34.
go back to reference Egawa, S., Awad, A.I., Baba, K.: Evaluation of acceleration algorithm for biometric identification. In: Benlamri, R. (ed.) Networked Digital Technologies, Communications in Computer and Information Science, vol. 294, pp. 231–242. Springer, Heidelberg (2012) Egawa, S., Awad, A.I., Baba, K.: Evaluation of acceleration algorithm for biometric identification. In: Benlamri, R. (ed.) Networked Digital Technologies, Communications in Computer and Information Science, vol. 294, pp. 231–242. Springer, Heidelberg (2012)
35.
go back to reference Awad, A.I.: Fingerprint local invariant feature extraction on GPU with CUDA. Informatica (Slovenia) 37(3), 279–284 (2013) Awad, A.I.: Fingerprint local invariant feature extraction on GPU with CUDA. Informatica (Slovenia) 37(3), 279–284 (2013)
36.
go back to reference Awad, A.I.: Fast fingerprint orientation field estimation incorporating general purpose GPU. In: Balas, V.E., Jain, L.C., Kovaevi, B. (eds.) Soft Computing Applications, Advances in Intelligent Systems and Computing, vol. 357, pp. 891–902. Springer International Publishing (2016) Awad, A.I.: Fast fingerprint orientation field estimation incorporating general purpose GPU. In: Balas, V.E., Jain, L.C., Kovaevi, B. (eds.) Soft Computing Applications, Advances in Intelligent Systems and Computing, vol. 357, pp. 891–902. Springer International Publishing (2016)
37.
go back to reference Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. 511–518 (2001) Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. 511–518 (2001)
38.
go back to reference Awad, A.I., Baba, K.: Evaluation of a fingerprint identification algorithm with SIFT features. In: Proceedings of the 3rd 2012 IIAI International Conference on Advanced Applied Informatics, pp. 129–132. IEEE, Fukuoka, Japan (2012) Awad, A.I., Baba, K.: Evaluation of a fingerprint identification algorithm with SIFT features. In: Proceedings of the 3rd 2012 IIAI International Conference on Advanced Applied Informatics, pp. 129–132. IEEE, Fukuoka, Japan (2012)
39.
go back to reference Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011) Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)
40.
go back to reference Donoser, M., Bischof, H.: Efficient maximally stable extremal region (MSER) tracking. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 1, 553–560 (2006) Donoser, M., Bischof, H.: Efficient maximally stable extremal region (MSER) tracking. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 1, 553–560 (2006)
41.
go back to reference Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’94), pp. 593–600 (1994) Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’94), pp. 593–600 (1994)
42.
go back to reference Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Proceedings of the 11th European Conference on Computer Vision: Part IV, pp. 778–792. ECCV’10. Springer, Heidelberg (2010) Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Proceedings of the 11th European Conference on Computer Vision: Part IV, pp. 778–792. ECCV’10. Springer, Heidelberg (2010)
43.
go back to reference Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 2564–2571. ICCV ’11, IEEE Computer Society, Washington, DC (2011) Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 2564–2571. ICCV ’11, IEEE Computer Society, Washington, DC (2011)
44.
go back to reference Pfau, R., Steinbach, M., Woll, B. (eds.): Sign Language: An International Handbook. Series of Handbooks of Linguistics and Communication Science (HSK), De Gruyter, Berlin (2012) Pfau, R., Steinbach, M., Woll, B. (eds.): Sign Language: An International Handbook. Series of Handbooks of Linguistics and Communication Science (HSK), De Gruyter, Berlin (2012)
45.
go back to reference Trémeau, A., Tominaga, S., Plataniotis, K.N.: Color in image and video processing: Most recent trends and future research directions. J. Image Video Process. Color Image Video Process. 7:1–7:26 (2008) Trémeau, A., Tominaga, S., Plataniotis, K.N.: Color in image and video processing: Most recent trends and future research directions. J. Image Video Process. Color Image Video Process. 7:1–7:26 (2008)
46.
go back to reference Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)CrossRef Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)CrossRef
47.
go back to reference Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2211–2226 (2009)CrossRef Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2211–2226 (2009)CrossRef
48.
go back to reference Awad, A.I., Hassanien, A.E.: Impact of some biometric modalities on forensic science. In: Muda, A.K., Choo, Y.H., Abraham, A., N. Srihari, S. (eds.) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, Studies in Computational Intelligence, vol. 555, pp. 47–62. Springer International Publishing (2014) Awad, A.I., Hassanien, A.E.: Impact of some biometric modalities on forensic science. In: Muda, A.K., Choo, Y.H., Abraham, A., N. Srihari, S. (eds.) Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, Studies in Computational Intelligence, vol. 555, pp. 47–62. Springer International Publishing (2014)
49.
go back to reference Gutierrez, J., Epifanio, I., de Ves, E., Ferri, F.: An active contour model for the automatic detection of the fovea in fluorescein angiographies. In: Proceedings of the 15th International Conference on Pattern Recognition. vol. 4, pp. 312–315. (2000) Gutierrez, J., Epifanio, I., de Ves, E., Ferri, F.: An active contour model for the automatic detection of the fovea in fluorescein angiographies. In: Proceedings of the 15th International Conference on Pattern Recognition. vol. 4, pp. 312–315. (2000)
50.
go back to reference Fomenko, A., Kunii, T.: Topological Modeling for Visualization. Springer, Japan (2013) Fomenko, A., Kunii, T.: Topological Modeling for Visualization. Springer, Japan (2013)
51.
go back to reference Hero, A., Ma, B., Michel, O., Gorman, J.: Applications of entropic spanning graphs. IEEE Signal Process. Mag. 19(5), 85–95 (2002)CrossRef Hero, A., Ma, B., Michel, O., Gorman, J.: Applications of entropic spanning graphs. IEEE Signal Process. Mag. 19(5), 85–95 (2002)CrossRef
52.
go back to reference Al-Shaher, A.A., Hancock, E.R.: Learning mixtures of point distribution models with the EM algorithm. Pattern Recogn. 36(12), 2805–2818 (2003)CrossRefMATH Al-Shaher, A.A., Hancock, E.R.: Learning mixtures of point distribution models with the EM algorithm. Pattern Recogn. 36(12), 2805–2818 (2003)CrossRefMATH
53.
go back to reference Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRef Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRef
54.
go back to reference Lee, C.C., Chen, S.H.: Gabor wavelets and SVM classifier for liver diseases classiflcation from CT images. In: IEEE International Conference on Systems, Man and Cybernetics (SMC’06), vol. 1, pp. 548–552 (2006) Lee, C.C., Chen, S.H.: Gabor wavelets and SVM classifier for liver diseases classiflcation from CT images. In: IEEE International Conference on Systems, Man and Cybernetics (SMC’06), vol. 1, pp. 548–552 (2006)
55.
go back to reference Christmann, A., Steinwart, I.: Support vector machines for classification. In: Support Vector Machines. Information Science and Statistics, pp. 285–329, Springer, New York (2008) Christmann, A., Steinwart, I.: Support vector machines for classification. In: Support Vector Machines. Information Science and Statistics, pp. 285–329, Springer, New York (2008)
56.
57.
go back to reference Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (2006)CrossRef Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (2006)CrossRef
58.
go back to reference Wu, Y., Ianakiev, K., Govindaraju, V.: Improved k-nearest neighbor classification. Pattern Recogn. 35(10), 2311–2318 (2002)CrossRefMATH Wu, Y., Ianakiev, K., Govindaraju, V.: Improved k-nearest neighbor classification. Pattern Recogn. 35(10), 2311–2318 (2002)CrossRefMATH
59.
go back to reference Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)CrossRef Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)CrossRef
Metadata
Title
Detection and Description of Image Features: An Introduction
Authors
M. Hassaballah
Ali Ismail Awad
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-28854-3_1

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