Análisis de motivos decorativos de tejidos y revestimientos cerámicos en el entorno de la visión artificial. Aplicación a la reconstrucción de motivos históricos y al diseño

El objetivo de esta tesis es la contribución a la creación, e implementación en herramientas informáticas, de una metodología aplicable para el análisis y edición de imágenes procedentes del campo de los diseños cerámicos y textiles, y por extensión, de todas aquellas imágenes que siguen un patrón repetitivo y que, por tanto, se ajustan a la Teoría de Grupos de Simetría. Para ello, se ha definido una metodología de análisis dividida en etapas, en la que se va aumentando gradualmente el nivel de la información manejada, desde los píxeles de la imagen inicial, pasando por los objetos (formas o unidades básicas perceptúales) y los motivos (agrupaciones de objetos realizadas con criterios perceptúales) hasta llegar a la estructura del patrón, es decir, las distintas transformaciones geométricas que relacionan los elementos (objetos y motivos) que lo forman. La información estructural obtenida es utilizada con fines diversos: la clasificación de las imágenes según el Grupo de Simetría del Plano del patrón, la reconstrucción de las imágenes aprovechando el conocimiento de qué partes están relacionadas por la estructura, y por último, la edición de patrones, tanto a nivel de formas y motivos, como de estructura, permitiendo realizar cambios estructurales con facilidad, con lo que se generan familias de patrones a partir de uno analizado. Las herramientas desarrolladas han sido probadas con un amplio conjunto de imágenes de patrones de procedencias muy diversas, destacando el estudio de los alicatados de la Alhambra de Granada y del Alcázar de Sevilla, así como de textiles y, ampliando los objetivos iniciales, a diversos elementos del entorno urbano.Albert Gil, FE. (2006). Análisis de motivos decorativos de tejidos y revestimientos cerámicos en el entorno de la visión artificial. Aplicación a la reconstrucción de motivos históricos y al diseño [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1936Palanci

Complete and Automated Generation of Configurable Virtual Prototypes of Products Based on Parameterization Tools and Rules. Application to a Case Study

[EN] In engineering, the 3D model of a product is essential. A 3D model allows making modifications by editing the characteristic modeling functions, which implies knowing in detail the process followed in the modeling. In addition, the flexibility in the configuration is limited, since the modifications are made on geometry and parameters that are dependent on each other. In this work, a parametric modeling approach allows the generation of 3D models with different specifications by modifying a reduced number of parameters. To demonstrate the functionality, an application has been developed, using Autodesk Inventor iLogic, for the modeling of an engine with V-cylinder arrangement. Taking as input key parameters (number of cylinders,¿), it can generate virtual prototypes with different configurations, facilitating the selection of the best product configuration by allowing to evaluate different alternatives.Veliz Vega, V.; Albert Gil, FE.; Aleixos Borrás, MN. (2022). Complete and Automated Generation of Configurable Virtual Prototypes of Products Based on Parameterization Tools and Rules. Application to a Case Study. Lecture Notes in Mechanical Engineering (Online). 294-301. https://doi.org/10.1007/978-3-030-92426-3_3429430

Development of an Application for the Automatic Evaluation of the Quality of 3D CAD Models

[EN] In the 3D modeling of products, the use of an adequate methodology that ensures the capture of the design intention is very important. The sequence of operations is key, like for instance, the sketches have to be completely restricted and the references of the modelling functions have to be correctly chosen without generating unwanted dependency relationships, among others. In the best of cases, the team leader dictates best practice manuals and then supervises the design work, ensuring that quality, which will facilitate future modifications or new designs based on existing models. However, this is not an established process, causing multiple failures in cascade when modifying or reusing the models is approached. This work has consisted of the development of an application that allows the automation of the quality analysis process in the models and has been developed for the Autodesk Inventor application using its iLogic tool. This work is the result of a Master¿s Thesis, where for the evaluation of the developed application, the examination models of the students of the subject of Graphic Engineering of the 4th year of the Degree in Engineering in Industrial Technologies of the Universitat Politècnica de València have been used.Pou Schmidt, I.; Rodriguez Ortega, A.; Albert Gil, FE.; Aleixos Borrás, MN. (2022). Development of an Application for the Automatic Evaluation of the Quality of 3D CAD Models. Lecture Notes in Mechanical Engineering (Online). 337-344. https://doi.org/10.1007/978-3-030-92426-3_3933734

Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review

[EN] Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.Cubero García, S.; Lee, WS.; Aleixos Borrás, MN.; Albert Gil, FE.; Blasco Ivars, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review. Food and Bioprocess Technology. 9(10):1623-1639. https://doi.org/10.1007/s11947-016-1767-1S16231639910Adebayo, S. E., Hashim, N., Abdan, K., & Hanafi, M. (2016). Application and potential of backscattering imaging techniques in agricultural and food processing—a review. Journal of Food Engineering, 169, 155–164.Aleixos, N., Blasco, J., Navarrón, F., & Moltó, E. (2002). Multispectral inspection of citrus in real time using machine vision and digital signal processors. Computers and Electronics in Agriculture, 33(2), 121–137.Annamalai, P., & Lee, W. S. (2003). Citrus yield mapping system using machine vision. ASAE Paper No. 031002. St. Joseph: ASAE.Annamalai, P., & Lee, W. S. (2004). Identification of green citrus fruits using spectral characteristics. ASAE Paper No. FL04–1001. St. Joseph: ASAE.Balasundaram, D., Burks, T. F., Bulanon, D. M., Schubert, T., & Lee, W. S. (2009). Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biology and Technology, 51, 220–226.Bansal, R., Lee, W. S., & Satish, S. (2013). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.Barreiro, P., Zheng, C., Sun, D.-W., Hernández-Sánchez, N., Pérez-Sánchez, J. M., & Ruiz-Cabello, J. (2008). Non-destructive seed detection in mandarins: comparison of automatic threshold methods in FLASH and COMSPIRA MRIs. Postharvest Biology and Technology, 47, 189–198.Basavaprasad, B., & Ravi, M. (2014). A comparative study on classification of image segmentation methods with a focus on graph based techniques. International Journal of Research in Engineering and Technology, 3, 310–315.Birth, G. S. (1976). How light interacts with foods. In: Gafney J.Jr.(Ed.), Quality detection in foods (pp. 6–11). St. Joseph: ASAE.Blanc, P.G.R., Blasco, J., Moltó, E., Gómez-Sanchis, J., & Cubero, S. (2010) System for the automatic selective separation of rotten citrus fruits. Patent number EP2133157 A1 CN101678405A, EP2133157A4, EP2133157B1, US20100121484Blasco, J., Aleixos, N., & Moltó, E. (2007a). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, 81(3), 535–543.Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007b). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damages in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145.Blasco, J., Cubero, S., & Moltó, E. (2016). Quality evaluation of citrus fruits. In D.-W. Sun (Ed.), Computer vision technology for food quality evaluation (2nd ed.). San Diego: Academic Press.Bulanon, D. M., Burks, T. F., & Alchanatis, V. (2009). Image fusion of visible and thermal images for fruit detection. Biosystems Engineering, 103, 12–22.Bulanon, D.M., Burks, T.F., Kim, D.G., & Ritenour, M.A. (2013). Citrus black spot detection using hyperspectral image analysis. Agricultural Engineering International: CIGR Journal, 15,(3)171.Burks, T. F., Villegas, F., Hannan, M. W., & Flood, S. (2003). Engineering and horticultural aspects of robotic fruit harvesting: opportunities and constraints. HortTechnology, 15(1), 79–87.Campbell, B. L., Nelson, R. G., Ebel, R. C., Dozier, W. A., Adrian, J. L., & Hockema, B. R. (2004). Fruit quality characteristics that affect consumer preferences for Satsuma mandarins. Hortscience, 39(7), 1664–1669.Chinchuluun, R., Lee, W. S., & Ehsani, R. (2009). Machine vision system for determining citrus count and size on a canopy shake and catch harvester. Applied Engineering in Agriculture, 25(4), 451–458.Choi, D., Lee, W. S., Ehsani, R., & Roka, F. M. (2015). A machine vision system for quantification of citrus fruit dropped on the ground under the canopy. Transactions of the ASABE, 58(4), 933–946.Codex Alimentarius, (2011). Codex standard for oranges. Available at: http://www.codexalimentarius.org/download/standards/10372/CXS_245e.pdf . Accessed March 2016Cubero, S., Aleixos, N., Albert, A., Torregrosa, A., Ortiz, C., García-Navarrete, O., & Blasco, J. (2014a). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture, 15(1), 80–94.Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.Cubero, S., Diago, M. P., Blasco, J., Tardáguila, J., Millán, B., & Aleixos, N. (2014b). A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis. Biosystems Engineering, 117, 62–72.Dong, C.-W., Ye, Y., Zhang, J.-Q., Zhu, H.-K., & Liu, F. (2014). Detection of thrips defect on green-peel citrus using hyperspectral imaging technology combining PCA and B-Spline lighting correction method. Journal of Integrative Agriculture, 13(10), 2229–2235.FAOSTAT (2012). URL: http://faostat.fao.org http://www.fao.org/fileadmin/templates/est/COMM_MARKETS_MONITORING/Citrus/Documents/CITRUS_BULLETIN_2012.pdf . Accessed March 2016.Farrell, T. J., Patterson, M. S., & Wilson, B. (1992). A diffusion-theory model of spatially resolved steady-state diffuse reflectance for the noninvasive determination of tissue optical-properties in vivo. Medical Physics, 19, 879–888.Flood, S. J., Burks, T. F., & Teixeira, A. A. (2006). Physical properties of oranges in response to applied gripping forces for robotic harvesting. Transactions of ASAE, 49(2), 341–346.Gaffney, J. J. (1973). Reflectance properties of citrus fruit. Transactions of ASAE, 16(2), 310–314.Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture, 91, 106–115.Gómez, J., Blasco, J., Moltó, E., & Camps-Valls, G. (2007). Hyperspectral detection of citrus damage with a Mahalanobis kernel classifier. Electronics Letters, 43(20), 1082–1084.Gómez-Sanchis, J., Blasco, J., Soria-Olivas, E., Lorente, D., Escandell-Montero, P., Martínez-Martínez, J. M., Martínez-Sober, M., & Aleixos, N. (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology, 82, 76–86.Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., & Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.Gómez-Sanchis, J., Lorente, D., Soria-Olivas, E., Aleixos, N., Cubero, S., & Blasco, J. (2014). Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food and Bioprocess Technology, 7, 1047–1056.Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785.Gong, A., Yu, J., He, Y., & Qiu, Z. (2013). Citrus yield estimation based on images processed by an android mobile phone. Biosystems Engineering, 115, 162–170.Gottwald, T. R., Graham, J. H., & Schubert, T. S. (2002). Citrus canker: the pathogen and its impact. Plant Health Progress. doi: 10.1094/PHP-2002-0812-01-RV.Hannan, M., Burks, T. F., & Bulanon, D.M. (2009). A machine vision algorithm for orange fruit detection. The CIGR Ejournal. Manuscript 1281. Vol XI. December 2009.Harrell, R. C., Adsit, P. D., & Slaughter, D. C. (1985). Real-time vision-servoing of a robotic tree-fruit harvester. ASAE Paper No (pp. 85–3550). St. Joseph: ASAE.Hernández-Sánchez, N., Barreiro, P., & Ruiz-Cabello, J. (2006). On-line identification of seeds in mandarins with magnetic resonance imaging. Biosystems Engineering, 95, 529–536.Holmes, G. J., & Eckert, J. W. (1999). Sensitivity of Penicillium digitatum and P. italicum to postharvest citrus fungicides in California. Phytopathology, 89(9), 716–721.Iqbal, S. M., Gopal, A., Sankaranarayanan, P. E., & Nair, A. B. (2016). Classification of selected citrus fruits based on color using machine vision system. International Journal of Food Properties, 19, 272–288.Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley.Jafari, A., Fazayeli, A., & Zarezadeh, M. R. (2014). Estimation of orange skin thickness based on visual texture coarseness. Biosystems Engineering, 117, 73–82.Jiménez-Cuesta, M. J., Cuquerella, J., & Martínez-Jávega, J. M. (1981). Determination of a color index for citrus fruit degreening. In Proceedings of the International Society of Citriculture, 2, 750–753.Kim, D. G., Burks, T. F., Qin, J., & Bulanon, D. M. (2009). Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering, 2, 41–50.Kim, D. G., Burks, T. F., Ritenour, M. A., & Qin, J. (2014). Citrus black spot detection using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 7, 20–27.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., Kaichi, T., & Okamoto, S. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4, 7–11.Kondo, N., Kuramoto, M., Shimizu, H., Ogawa, Y., Kurita, M., Nishizu, T., Chong, V. K., & Yamamoto, K. (2009). Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Engineering in Agriculture, Environment and Food, 2, 54–59.Kurita, M., Kondo, N., Shimizu, H., Ling, P. P., Falzea, P. D., Shiigi, T., Ninomiya, K., Nishizu, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21, 533–540.Kurtulmus, F., Lee, W. S., & Vardar, A. (2011). Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 78(2), 140–149.Ladaniya, M. S. (2010). Citrus fruit: biology, technology and evaluation. San Diego: Academic Press.Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture. doi: 10.1007/s11119-016-9443-z.Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014). Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15, 162–183.Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78, 38–48.Li, J., Rao, X., & Ying, Y. (2012a). Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. Journal of the Science of Food and Agriculture, 92, 125–134.Li, J., Rao, X., Wang, F., Wu, W., & Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69.Li, J., Rao, X., Ying, Y., & Wang, D. (2010). Detection of navel oranges canker based on hyperspectral imaging technology. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 26, 222–228.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A., Yang, C., & Mangan, R. (2012b). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 83, 32–46.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2015). Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering, 132, 28–38.Lopes, L. B., VanDeWall, H., Li, H. T., Venugopal, V., Li, H. K., Naydin, S., Hosmer, J., Levendusky, M., Zheng, H., Bentley, M. V., Levin, R., & Hass, M. A. (2010). Topical delivery of lycopene using microemulsions: enhanced skin penetration and tissue antioxidant activity. Journal of Pharmaceutical Sciences, 99, 1346–1357.López, J. J., Cobos, M., & Aguilera, E. (2011). Computer-based detection and classification of flaws in citrus fruits. Neural Computing and Applications, 20, 975–981.López-García, F., Andreu, G., Blasco, J., Aleixos, N., & Valiente, J. M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71, 189–197.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2013a). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology, 6(2), 530–541.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.Lorente, D., Blasco, J., Serrano, A. J., Soria-Olivas, E., Aleixos, N., & Gómez-Sanchis, J. (2013b). Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food and Bioprocess Technology, 6(12), 3613–3619.Lorente, D., Zude, M., Regen, C., Palou, L., Gómez-Sanchis, J., & Blasco, J. (2013c). Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology, 86, 424–430.Lorente, D., Zude, M., Idler, C., Gómez-Sanchis, J., & Blasco, J. (2015). Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. Journal of Food Engineering, 154, 76–85.Maf Industries. (2016). VIOTEC brochure. http://mafindustries.com/wp-content/uploads/2015/02/viotec3.pdf . Accessed March 2016.Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5(2), 425–444.Mehta, S. S., & Burks, T. F. (2014). Vision-based control of robotic manipulator for citrus harvesting. Computers and Electronics in Agriculture, 102, 146–158.Moltó, E., Blasco, J., & Gómez-Sanchis, J. (2010). Analysis of hyperspectral images of citrus fruits. In D.-W. Sun (Ed.), Hyperspectral imaging for food quality analysis and control (pp. 321–348). California: Academic Press.Moltó, E., Plá, F., & Juste, F. (1992). Vision systems for the location of citrus fruit in a tree canopy. Journal of Agricultural Engineering Research, 52, 101–110.Momin, A., Kondo, N., Kuramoto, M., Ogawa, Y., Yamamoto, K., & Shiigi, T. (2012). Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV-VIS spectra. Engineering in Agriculture, Environment and Food, 5, 126–132.Momin, A., Kondo, N., Ogawa, Y., Ido, K., & Ninomiya, K. (2013b). Patterns of fluorescence associated with citrus peel defects. Engineering in Agriculture, Environment and Food, 6, 54–60.Momin, A., Kuramoto, M., Kondo, N., Ido, K., Ogawa, Y., Shiigi, T., & Ahmad, U. (2013a). Identification of UV-fluorescence components for detecting peel defects of lemon and yuzu using machine vision. Engineering in Agriculture, Environment and Food, 6, 165–171.Morgan, S. P., & Stockford, I. M. (2003). Surface-reflection elimination in polarization imaging of superficial tissue. Optics Letters, 28, 114–116.Niphadkar, N. P., Burks, T. F., Qin, J., & Ritenour, M. (2013b). Edge effect compensation for citrus canker lesion detection due to light source variation—a hyperspectral imaging application. Agricultural Engineering International: CIGR Journal, 15, 314–327.Niphadkar, N. P., Burks, T. F., Qin, J. W., & Ritenour, M. A. (2013a). Estimation of citrus canker lesion size using hyperspectral reflectance imaging. International Journal of Agricultural and Biological Engineering, 6, 41–51.Obenland, D., Margosan, D., Smilanick, J. L., & Mackey, B. (2010). Ultraviolet fluorescence to identify navel oranges with poor peel quality and decay. HortTechnology, 20, 991–995.Ogawa, Y., Abdul, M. M., Kuramoto, M., Kohno, Y., Shiigi, T., Yamamoto, K., & Kondo, K. (2011). Rotten part detection on citrus fruit surfaces by use of fluorescent images. The Review of Laser Engineering, 394, 255–261.Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66(2), 201–208.Omid, M., Khojastehnazhand, M., & Tabatabaeefar, A. (2010). Estimating volume and mass of citrus fruits by image processing technique. Journal of Food Engineering, 100, 315–321.Ottavian, M., Barolo, M., & García-Muñoz, S. (2013). Maintenance of machine vision systems for product quality assessment. Part I. Addressing changes in lighting conditions. Industrial & Engineering Chemistry Research, 52, 12309–12318.Ottavian, M., Barolo, M., & García-Muñoz, S. (2014). Maintenance of machine vision systems for product quality assessment. Part II. Addressing camera replacement. Industrial & Engineering Chemistry Research, 53, 1529–1536.Palou, L. (2014). Penicillium digitatum, Penicillium italicum (green mold, blue mold). In S. Bautista-Baños (Ed.), Postharvest decay. Control strategies. London: Elsevier.Palou, L., Smilanick, J. L., Montesinos-Herrero, C., Valencia-Chamorro, S., & Pérez-Gago, M. B. (2011). Novel approaches for postharvest preservation of fresh citrus fruits. In Slaker (Ed.), Citrus fruits: properties, consumption and nutrition. New York: Nova Science Publishers, Inc..Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: a systematic review of research. Journal of Sensors, Open Access Article ID 195308.Pourreza, A., Lee, W. S., Ehsani, R., Schueller, J. K., & Raveh, E. (2015a). An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture, 110, 221–232.Pourreza, A., Lee, W. S., Etxeberria, E., & Banerjee, A. (2015b). An evaluation of a vision based sensor performance in Huanglongbing disease identification. Biosystems Engineering, 130, 13–22.Qin, J., Burks, T. F., Kim, M. S., Chao, K., & Ritenour, M. A. (2008). Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety, 2(3), 168–177.Qin, J., Burks, T. F., Ritenour, M. A., & Gordon Bonn, W. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93, 183–191.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2011). Multispectral detection of citrus canker using hyperspectral band selection. Transactions of the ASABE, 54, 2331–2341.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2012). Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 108, 87–93.Sengupta, S., & Lee, W. S. (2014). Identification and determination of the number of immature green citrus fruit under different ambient light conditions.

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

[EN] The collection of oranges normally begins before they have reached the typical orange colour. Moreover, citrus fruits are subjected to certain degreening treatments that depend on the standard citrus colour index (CCI) at harvest. In order to facilitate the measure of this index, a free application that uses image processing techniques has been developed for Android-based mobile devices using the built-in camera of the device. The image analysis process is performed on all the images from the live input of the camera to obtain the CCI of such fruit using the open source OpenCV library. For this purpose, the RGB (red, green and blue colour coordinates) average value of a pre-selected area of the input image is calculated and then converted to HunterLab colour space to finally calculate the CCI. Several tests were carried out in the field with the fruit on the trees and under laboratory conditions with different varieties of oranges (Navel, Bonanza, Cram and Navelina) at different stages of maturity, and using different Android devices. The results were obtained for each device and condition in relation to the colour measured by a camera and compared with the performance of a panel of workers who evaluated the colour using the traditional methods. Best R-2 values obtained were 0.854 for outdoors conditions and 0.881 when measurements were done indoors.This work was partially funded by INIA and FEDER funds through research project RTA2015-00078-00-00.Cubero-García, S.; Albert Gil, FE.; Prats-Montalbán, JM.; Fernandez-Pacheco, DG.; Blasco Ivars, J.; Aleixos Borrás, MN. (2018). Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices. Biosystems Engineering. 167:63-74. doi:10.1016/j.biosystemseng.2017.12.012S637416

Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform

The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R-2 coefficient of 0.993 for size estimation and an R-2 coefficient of 0.918 for the colour index.This research work has been funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) and the European FEDER funds (projects RTA2009-00118-C02-01 and RTA2009-00118-C02-02). The authors wish to thank the collaboration of the company Argiles Diseny i Fabricacio, S.L.Cubero García, S.; Aleixos Borrás, MN.; Albert Gil, FE.; Torregrosa, A.; Ortiz Sánchez, MC.; García Navarrete, OL.; Blasco Ivars, J. (2014). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture. 15(1):80-94. doi:10.1007/s11119-013-9324-7S8094151Baeten, J., Donné, K., Boedrij, S., Beckers, W., & Claesen, E. (2008). Autonomous fruit picking machine: A robotic apple harvester. Springer Tracts in Advanced Robotics, 42, 531–539.Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009a). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103, 137–145.Blasco, J., Aleixos, N., Roger, J. M., Rabatel, G., & Moltó, E. (2002). Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149–157.Blasco, J., Cubero, S., Gómez-Sanchis, J., Mira, P., & Moltó, E. (2009b). Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering, 90, 27–34.Chong, V. K., Monta, M., Ninomiya, K., Kondo, N., Namba, K., Terasaki, E., et al. (2008). Development of mobile eggplant grading robot for dynamic in-field variability sensing––manufacture of robot and performance test. Engineering in Agriculture, Environment and Food, 1(2), 68–76.Coppock, G. E., & Jutras, P. J. (1960). Mechanizing citrus fruit harvesting. Transactions of the ASAE, 3(2), 130–132.Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.Cubero, S., Moltó, E., Gutiérrez, A., Aleixos, N., García-Navarrete, O. L., Juste, F., et al. (2010). Real-time inspection of fruit on a mobile harvesting platform in field conditions using computer vision. Progress in Agricultural Engineering Science, 6, 1–16.DOGV. (2006). Diari Oficial de la Comunitat Valenciana, 5346, 30321–30328.Edan, Y., Rogozin, D., Flash, T., & Miles, G. E. (2000). Robotic melon harvesting. IEEE Transactions on Robotics and Automation, 16(6), 831–834.Ehsani, M. R., Grift, T. E., Maja, J. M., & Zhong, D. (2009). Two fruit counting techniques for citrus mechanical harvesting machinery. Computers and Electronics in Agriculture, 65(2), 186–191.Feng, G., Qixin, C., & Masateru, N. (2008). Fruit detachment and classification method for strawberry harvesting robot. International Journal of Advanced Robotic Systems, 5(1), 41–48.Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., et al. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.HunterLab. (2008). Applications note, 8(9) http://www.hunterlab.com/appnotes/an08_96a.pdf . Accessed Nov 2012.Jiménez-Cuesta, M.J., Cuquerella, J., & Martínez-Jávega, J.M. (1981). Determination of a color index for citrus fruit degreening. In: Proceedings of the International Society of Citriculture, Tokyo (Japan), vol. 2 (pp. 750–753).Jutras, P.J., & Coppock, G.E. (1958). Mechanization of citrus fruit picking. Florida State Horticultural Society, 71, 201,204.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., et al. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4(1), 7–11.Kondo, N. (2009). Robotization in fruit grading system. Sensors and Instrumentation for Food Quality, 3, 81–87.Lee, W. S., & Slaughter, D. C. (2004). Recognition of partially occluded plant leaves using a modified Watershed algorithm. Transactions of the ASAE, 47, 1269–1280.Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1(1), 95–113.Li, Z., Li, P., & Liu, J. (2011). Physical and mechanical properties of tomato fruits as related to robot harvesting. Journal of Food Engineering, 103(2), 170–178.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.Mazzetto, F., Calcante, A., Mena, A., & Vercesi, A. (2010). Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture. Precision Agriculture, 11(6), 636–649.McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7–23.Mizushima, A., & Lu, R. (2011). Cost benefits analysis of in-field presorting for the apple industry. Applied Engineering in Agriculture, 27(1), 33–40.Muscato, G., Prestifilippo, M., Abbate, N., & Rizzuto, I. (2005). A prototype of an orange picking robot: Past history and experimental results. Industrial Robot, 32(2), 128–138.Nieuwenhuizen, A. T., Hofstee, J. W., & van Henten, E. J. (2010). Adaptive detection of volunteer potato plants in sugar beet fields. Precision Agriculture, 11, 433–447.Official Journal of European Communities. (2001). 14.09.2001. pp. L244/12–L244/18. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2001:244:0012:0018:EN:PDF . Accessed May 2013.Ortiz, C., Blasco, J., Balasch, S., & Torregrosa, A. (2011). Shock absorbing surfaces for collecting fruit during the mechanical harvesting of citrus. Biosystems Engineering, 110, 2–9.Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., & Morimoto, E. (2004). Mobile fruit grading robot (part1)––Development of a robotic system for grading sweet peppers. Journal of the Japanese Society of Agricultural Machinery (JSAM), 66(2), 113–122.Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., & Morimoto, E. (2005). Mapping yield and quality using the mobile fruit grading robot. Biosystems Engineering, 90(2), 135–142.Ruiz-Altisent, M., Ortiz-Cañavate, J., & Valero, C. (2004). Fruit and vegetables harvesting systems. In: R. Dris and S. M. Jain (Eds.), Production practices and quality assessment of food crops, vol. 1: Preharvest practice (pp. 261–285). Dordrecht: Kluwer.Torregrosa, A., Gil, J., Ortiz, C., Ortí, E., & Martín, B. (2009). Mechanical harvesting of oranges and mandarins in Spain. Biosystems Engineering, 104(1), 18–24.Vidal, A., Talens, P., Prats-Montalbán, J. M., Cubero, S., Albert, F., & Blasco, J. (2012). In-line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food and Bioprocess Technology,. doi: 10.1007/s11947-012-1015-2

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Análisis de motivos decorativos de tejidos y revestimientos cerámicos en el entorno de la visión artificial. Aplicación a la reconstrucción de motivos históricos y al diseño

El objetivo de esta tesis es la contribución a la creación, e implementación en herramientas informáticas, de una metodología aplicable para el análisis y edición de imágenes procedentes del campo de los diseños cerámicos y textiles, y por extensión, de todas aquellas imágenes que siguen un patrón repetitivo y que, por tanto, se ajustan a la Teoría de Grupos de Simetría. Para ello, se ha definido una metodología de análisis dividida en etapas, en la que se va aumentando gradualmente el nivel de la información manejada, desde los píxeles de la imagen inicial, pasando por los objetos (formas o unidades básicas perceptúales) y los motivos (agrupaciones de objetos realizadas con criterios perceptúales) hasta llegar a la estructura del patrón, es decir, las distintas transformaciones geométricas que relacionan los elementos (objetos y motivos) que lo forman. La información estructural obtenida es utilizada con fines diversos: la clasificación de las imágenes según el Grupo de Simetría del Plano del patrón, la reconstrucción de las imágenes aprovechando el conocimiento de qué partes están relacionadas por la estructura, y por último, la edición de patrones, tanto a nivel de formas y motivos, como de estructura, permitiendo realizar cambios estructurales con facilidad, con lo que se generan familias de patrones a partir de uno analizado. Las herramientas desarrolladas han sido probadas con un amplio conjunto de imágenes de patrones de procedencias muy diversas, destacando el estudio de los alicatados de la Alhambra de Granada y del Alcázar de Sevilla, así como de textiles y, ampliando los objetivos iniciales, a diversos elementos del entorno urbano

Evolution over Time of Ventilatory Management and Outcome of Patients with Neurologic Disease∗

OBJECTIVES: To describe the changes in ventilator management over time in patients with neurologic disease at ICU admission and to estimate factors associated with 28-day hospital mortality. DESIGN: Secondary analysis of three prospective, observational, multicenter studies. SETTING: Cohort studies conducted in 2004, 2010, and 2016. PATIENTS: Adult patients who received mechanical ventilation for more than 12 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among the 20,929 patients enrolled, we included 4,152 (20%) mechanically ventilated patients due to different neurologic diseases. Hemorrhagic stroke and brain trauma were the most common pathologies associated with the need for mechanical ventilation. Although volume-cycled ventilation remained the preferred ventilation mode, there was a significant (p < 0.001) increment in the use of pressure support ventilation. The proportion of patients receiving a protective lung ventilation strategy was increased over time: 47% in 2004, 63% in 2010, and 65% in 2016 (p < 0.001), as well as the duration of protective ventilation strategies: 406 days per 1,000 mechanical ventilation days in 2004, 523 days per 1,000 mechanical ventilation days in 2010, and 585 days per 1,000 mechanical ventilation days in 2016 (p < 0.001). There were no differences in the length of stay in the ICU, mortality in the ICU, and mortality in hospital from 2004 to 2016. Independent risk factors for 28-day mortality were age greater than 75 years, Simplified Acute Physiology Score II greater than 50, the occurrence of organ dysfunction within first 48 hours after brain injury, and specific neurologic diseases such as hemorrhagic stroke, ischemic stroke, and brain trauma. CONCLUSIONS: More lung-protective ventilatory strategies have been implemented over years in neurologic patients with no effect on pulmonary complications or on survival. We found several prognostic factors on mortality such as advanced age, the severity of the disease, organ dysfunctions, and the etiology of neurologic disease