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Tytuł artykułu

Development of an acousto-optic system for hyperspectral image segmentation

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Image segmentation is a typical operation in many image analysis and computer vision applications. However, hyperspectral image segmentation is a field which have not been fully investigated. In this study an analogue-digital image segmentation technique is presented. The system uses an acousto-optic tuneable filter, and a CCD camera to capture hyperspectral images that are stored in a digital grey scale format. The dataset was built considering several objects with remarkable differences in the reflectance and brightness components. In addition, the work presents a semi-supervised segmentation technique to deal with the complex problem of hyperspectral image segmentation, with its corresponding quantitative and qualitative evaluation. Particularly, the developed acousto-optic system is capable to acquire 120 frames through the whole visible light spectrum. Moreover, the analysis of the spectral images of a given object enables its segmentation using a simple subtraction operation. Experimental results showed that it is possible to segment any region of interest with a good performance rate by using the proposed analogue-digital segmentation technique.
Rocznik
Strony
517--530
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr.
Twórcy
autor
  • Universidad Politécnica de Querétaro, Carretera Estatal 420 S/N, C.P. 76240, El Marqués, Querétaro, México
  • Universidad del Quindío, Facultad de Física, Armenia, Colombia
  • Universidad del Quindío, Facultad de Física, Armenia, Colombia
  • Universidad Politécnica de Querétaro, Carretera Estatal 420 S/N, C.P. 76240, El Marqués, Querétaro, México
  • Universidad Politécnica de Querétaro, Carretera Estatal 420 S/N, C.P. 76240, El Marqués, Querétaro, México
  • Universidad Politécnica de Querétaro, Carretera Estatal 420 S/N, C.P. 76240, El Marqués, Querétaro, México
  • Universidad Autónoma del Estado de Morelos, Center for Research in Engineering and Applied Science, México
Bibliografia
  • [1] Lee, J., Shinozuka, M. (2006). Real-time displacement measurement of a flexible bridge using digital image processing techniques. Experimental Mechanics, 46(1), 105-114.
  • [2] Meer, P., Mintz, D., Rosenfeld, A., Yoon, D. (1991). Robust regression methods for computer vision: A review. International Journal of Computer Vision, 6(1), 59-70.
  • [3] Cheng, H., Jiang, X, Sun Y., Wang, J. (2001). Color image segmentation: advances and prospects. Pattern recognition, 34(12), 2259-2281.
  • [4] Burnett, C., Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3), 233249.
  • [5] Tang, L., Tian, L., Steward, F. (2000). Color image segmentation with genetic algorithm for in-field weed sensing. Transactions of the ASAE, 43(4), 1019-1027.
  • [6] Ryherd, S., Woodcock, C. (1996). Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric engineering and remote sensing, 62(2), 181-194.
  • [7] Mosquera, J., Isaza, C., Gómez, G. (2012). Technical analog-digital for segmentation of spectral images acquired with an acousto-optic system. XVII Symposium of Image, Signal Processing and Artificial Vision (STSIVA).
  • [8] Voloshinov, V., Molchanov, V., Mosquera, J. (1996). Spectral and polarization analysis of optical images by means of acousto-optics. Optics & Laser Technology, 28(2), 119-127.
  • [9] Narasimha, S., Feng, J., Emir, Y., Susan, K., Sudhir, B., Jolanta, S. (2018). Acousto-optic tunable filter based spectrapolarimeter for extraction of Stokes and Mueller matrices. Proc. SPIE 10655, Polarization: Measurement, Analysis, and Remote Sensing XIII.
  • [10] Guillaume, N., Angulo, J., Dominique, J. (2007). Morphological segmentation of hyperspectral images. Image. Anal. Stereol., 26, 101-109.
  • [11] Turpin, T. (1981). Spectrum analysis using optical processing. Proc. of the IEEE, 69(1), 79-92.
  • [12] Antonio, P., Atli, J., Boardman, J., Brazile, J., Bruzzone, L., Camps, G., Chanussot, J., Fauvel, M., Gamba, P. (2009). Recent advances in techniques for hyperspectral image processing. Remote sensing of environment, 113(1), S110-S122.
  • [13] Li, J., Bioucas, J., Plaza, A. (2010). Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing, 48(11), 4085-4098.
  • [14] Liu, Z., Yan, J., Zhang, D., Li, Q. (2007). Automated tongue segmentation in hyperspectral images for medicine. Journal of Applied optics, 46(34), 8328-8334.
  • [15] Chaudhari, A., Darvas, F., Bading, J., Moats, R., Conti, P., Smith, D., Cherry, S., Leahy, R. (2005). Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging. Physics in medicine and biology, 50(23), 5421-5441.
  • [16] Gowen, A., O’Donnell, C., Cullen, P., Downey, G., Frias, J. (2007). Hyperspectral imaging an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology, 18(12), 590-598.
  • [17] Nikhil, P., Sankar, P. (1993). A review on image segmentation techniques. Pattern recognition, 26(9), 1277-1294.
  • [18] Yu Z. (2001). A review of recent evaluation methods for image segmentation. Signal Processing and its Applications, Sixth International Symposium IEEE, 1, 148-151.
  • [19] Baranda, A. (2015). A hyperspectral imaging system using an acousto-optic tunable filter. Master Thesis. Norwegian University of Science and Technology.
  • [20] Ali, M., Ramy, A., Lilian, D., Joseph, H., Mark, D. (2017). Hyperespectral image processing for detection and grading of skin erythema. Proc. SPIE Medical Imaging 2017, Image Processing.
  • [21] Peng, F., Xin, S., Quansen, S. (2017). Hyperespectral image segmentation via frequency-based similarity for mixed noise estimation. Remote sensing, 9(12), 1237.
  • [22] Konstantin, B., Vladimir, Y. (2017). Hyperespectral imaging acousto-optic system with spatial filtering for optical phase visualization. J. Biomedical Optics, 22(6), 066017.
  • [23] Dey, V., Zhang, Y., Zhong, M. (2010). A review on image segmentation techniques with remote sensing perspective. ISPRS TC VII Symposium.
  • [24] Devarshi, N., Pinal, S. (2014). A review on image segmentation clustering algorithms. International Journal of Computer Science Information Technology, 5(3), 3289-3293.
  • [25] Lorente, D., Aleixos, N., Gomez, J., Cubero, S., Navarrete, O., Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121-1142.
  • [26] Voloshinov, V., Gupta, N. (2004). Ultraviolet-visible imaging acousto-optic tunable filters in KDP. Applied optics, 43(19), 3901-3909.
  • [27] Tsai, C. (1990). Guided-wave acousto-optics: interactions, devices, and applications. Springer-Verlag, 23.
  • [28] Voloshinov, V., Tatyana, M., Vladimir, Y. (2002). Two-dimensional selection of optical spatial frequencies by acousto-optic methods. Optical Engineering, 41(6), 1273-1280.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5a1e236b-9f0e-4857-8477-fb18d6f9f106
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