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2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

Journal of Physics: Photonics 2025年第3期7卷 032501-032501页

作者： Lien Smeesters Francesca Venturini Stefan Paulus Anne-Katrin Mahlein David Perpetuini Daniela Cardone Arcangelo Merla Benjamin Gac Stephane Perrin Denis Trégoat Luiz Poffo Antoine Fournier Giuseppe Bonifazi Silvia Serranti Wenzhi Liao Noureddin Osseiran Mathias Vanwolleghem Romain Peretti Aparajita Bandyopadhyay Karl Bertling Krishnan Parameswaran Aleksandar D Rákic Amartya Sengupta Sune Svanberg Małgorzata Kujawinska Daniel Cozzolino Dolores Pérez Marín José A Entrenas de León Irina Torres Rodríguez Marietta Fodor Eszter Benes Anna Matkovits Zsuzsa Jókai Simona M Cristescu Amir Khodabakhsh Khalil Jahromi Mohammadreza Nematollahi Frans H Harren Eugene Rokx Ewa Sikorska Kay Sowoidnich Martin Maiwald Bernd Sumpf Matthieu Roussey Kai-Erik Peiponen Antoni Femenias Polina Fomina Vjekoslav Kokoric Robert Stach Boris Mizaikoff Maria Konstantaki Stavros Pissadakis Umberto Michelucci Sławomir Paśko Anna Pakuła Richard A Crocombe Ellen V Miseo Andreas C Liapis Faisal Ahmed Zhipei Sun Chrysoula Chandrinou Marianneza Chatzipetrou Ioanna Zergioti Ulrich Trog Anja Haase Martin Smolka Lex Oosterveld Chris Van Hoof André Müller Michael Hlavatsch Diellza Bajrami Ernesto Olvera-Gonzalez Nivia Escalante-Garcia Arturo B Soro Koenraad Van Hoorde Brijesh K Tiwari Thomas Westerhoff Michael Kneissl Anna Grazia Mignani Department of Applied Physics and Photonics Vrije Universiteit Brussel and Flanders Make Brussels Photonics (B-PHOT) Pleinlaan 2 B-1050 Brussels Belgium School of Engineering ZHAW Zurich University of Applied Sciences 8401 Winterthur Switzerland TOELT LLC Research and Development 8600 Duebendorf Switzerland Institute of Sugar Beet Research Holtenser Landstr. 77 37079 Göttingen Germany Department of Engineering and Geology University ‘D’Annunzio’ of Chieti-Pescara Pescara 65127 Italy Arvalis 45 Voie Romaine 41240 Beauce la Romaine France Université de Rennes CNRS Institut FOTON—UMR 6082 F-22305 Lannion France Photonics Bretagne 4 Rue Louis de Broglie 22300 Lannion France University of Limoges XLIM UMR CNRS 7252 Limoges France Department of Chemical Engineering Materials and Environment Sapienza—University of Rome via Eudossiana 18 00184 Rome Italy Flanders Make 8500 Kortrijk Belgium Department of Telecommunications and Information Processing Ghent University 9000 Ghent Belgium Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN) CNRS–UMR 8520 Université de Lille 59652 Villeneuve d’Ascq France DRDO-Industry-Academia Centre of Excellence (DIA-CoE) Indian Institute of Technology Delhi New Delhi 110016 India School of Electrical Engineering and Computer Science University of Queensland Brisbane QLD 4072 Australia Cambridge Consultants Boston MA United States of America Department of Physics Indian Institute of Technology Delhi New Delhi 110016 India Physics Department Lund University SE-221 00 Lund Sweden Institute of Micromechanics and Photonics Warsaw University of Technology 8 Sw. Andrzeja Boboli St 02-525 Warsaw Poland Central Office of Measures 2 Elektoralna St. 00-139 Warsaw Poland Centre for Nutrition and Food Sciences Queensland Alliance for Agriculture and Food Innovation (QAAFI) The University of Queensland St.Lucia Brisbane QLD 4072 Australia Non-Destructive Spectral Sensors Unit Department of Animal Production Fa

Photonics technologies play a crucial role in driving technological advancements within the agrifood industry, aiming to deliver a sustainable food and agriculture, and offering healthy, nutritious and safe food for all of us. Particularly, optical sensors and imaging systems, together with machine-learning processing and advanced lighting, play a pivotal role in monitoring crop and soil health with unprecedented precision, while safeguarding the food supply chain. This roadmap aims to provide an overview of the state-of-the-art photonics technologies benefitting agrifood applications, including a view on their current limitations, challenges and future potential, while addressing practical case studies. Future trends towards multimodal sensors and sensor fusion, artificial intelligence and digital twins, miniaturization and controlled farming are highlighted. The revolutionizing capabilities of the photonics technologies are indicated, inspiring future applications and developments, and paving the way towards optimized resource utilization, increased crop yields, stopping land degradation and reduction of food waste.

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Convex cone volume analysis for finding endmembers in hyperspectral imagery

Convex cone volume analysis for finding endmembers in hypers...

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作者： Chang, Chein-I Xiong, Wei Chen, Shih-Yu Information and Technology College Dalian Maritime University Dalian China School of Physics and Optoelectronic Engineering Xidian University Xian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

This paper presents a new approach, called convex cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be shown that finding the maximal volume of a convex cone in the original data space is equivalent to finding the maximal volume of a simplex in a hyperplane. As a result, the CCVA can take advantage of many recently developed fast computational algorithms developed for N-FINDR to derive their counterparts for CCVA. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Spectroscopy

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[P244] Accuracy of ct numbers obtained by dira and monoenergetic plus algorithms in quantitative dual-energy computed tomography

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Physica Medica 2018年 52卷 169-169页

作者： Maria Magnusson Alexandr Malusek Michael Sandborg Gudrun Alm Carlsson Lilian Henriksson Åsa Carlsson Tedgren Computer Vision Laboratory Dept. of Electrical Engineering Center for Medical Image Science and Visualization (Cmiv) Linköping University Linköping Sweden Radiation Physics Dept. of Medical and Health Sciences Center for Medical Image Science and Visualization (Cmiv) Linköping University Linköping Sweden Dept. of Radiology and Dept. of Medical and Health Sciences Center for Medical Image Science and Visualization (Cmiv) Linköping University Linköping Sweden Dept. of Medical Radiation Physics and Nuclear Medicine Karolinska University Hospital Stockholm Sweden

Purpose The aim of this work is to compare the accuracy of the DIRA [1] and Monoenergetic Plus [2] image reconstruction algorithms in dual-energy CT . Accurate CT numbers are needed for instance in radiotherapy planning. Methods A Siemens SOMATOM Definition Force scanner was used to scan a cylindrical PMMA phantom of diameter of 160 mm with four rod inserts of diameter 20 mm made of aluminum, teflon and low-density polyethylene. For the Monoenergetic Plus algorithm, a helical, dual-energy protocol with 80 and Sn150 kV was used. images reconstructed with the ADMIRE algorithm were processed in the *** Frontier software (Siemens) and virtual monoenergetic images at energies from 40 to 180 keV were obtained. For DIRA, axial, dual-energy scan was performed, and two sets of projection data were recorded. The projection data were then processed by DIRA, which used a two-material decomposition to the bone and bone marrow doublet and a three-material decomposition to the lipid, protein and water triplet. Virtual monoenergetic images were obtained from the computed material mass fractions. The reconstruction with DIRA was also performed for computer simulated projection data. CT numbers provided by both algorithms were compared to tabulated values. Results The Monoenergetic Plus algorithm performed best for PMMA with the maximum difference of 9 HU at 40 keV. For Aluminum, the difference of about 43 HU was observed at 60 and 180 keV. In case of DIRA, computer simulations predicted good agreement in CT numbers, differences of 13 HU and 2 HU at 50 and 88keV, respectively, were observed for aluminum. Preliminary results indicate that accuracy of CT values obtained from measured projections approaches those from computer simulations. Conclusions The Monoenergetic Plus algorithm performs two-material decomposition to iodine and water and then uses the data on concentrations for the prediction of CT numbers. This approach leads to errors especially for aluminum, nevertheless t

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Nuclei segmentation of fluorescence microscopy images based on midpoint analysis and marked point process

Nuclei segmentation of fluorescence microscopy images based ...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Neeraj J. Gadgil Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory Purdue University West Lafayette Indiana Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indiana Division of Nephrology Indiana University Indianapolis Indiana

Microscope image analysis is challenging because of non-uniform brightness, decreasing image contrast with tissue depth, poor edge details and irregular and unknown structures. In this paper, we present a nuclei segmentation and counting method using midpoint analysis, shape-based function optimization and a MPP simulation with a spatial birth-death process. Preliminary results demonstrate efficacy of the proposed method.

关键词： image segmentation Microscopy Shape Three-dimensional displays Brightness Biology Stochastic processes

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Jelly filling segmentation of fluorescence microscopy images containing incomplete labeling

Jelly filling segmentation of fluorescence microscopy images...

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IEEE International Symposium on Biomedical Imaging

ISBN: (纸本)9781479923519

Biological images acquired using fluorescence microscopy typically suffer from poor edge details, non-uniform brightness, decreasing image contrast with tissue depth and irregular/unknown structure. Hence they are consequently challenging to segment. In this paper, we present a segmentation method we call "Jelly filling" for analyzing biological volumes that also contain incomplete labeling. Our method uses adaptive thresholding, component analysis, and three-dimensional structural consistency to iteratively refine segments that represent physical quantities. Preliminary results obtained using fluorescence microscopy images of rat kidneys demonstrate the efficacy of the proposed method.

关键词： image segmentation Biology Three-dimensional displays Microscopy Labeling image edge detection Filling

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Blind deconvolution for single noisy and blurry image using alternating maximum a posteriori estimation with low rank prior

Blind deconvolution for single noisy and blurry image using ...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Shijie Sun Huaici Zhao Jinfeng Lv Mingguo Hao Bo Li Key Laboratory of Image Understanding and Computer Vision Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang China College of Information Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9781509041008

The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and even more challenging especially in the condition that the noise in the input image cannot be negligible. In this paper, the main problem we focus on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating maximum a posteriori (MAP) estimation combined with low rank prior. When estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. The denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Extensive experiments show the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.

关键词： C# languages

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Counterattack detection in broadcast soccer videos using camera motion estimation

Counterattack detection in broadcast soccer videos using cam...

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2015 International Symposium on Artificial Intelligence and Signal processing, AISP 2015

作者： Sigari, Mohamad-Hoseyn Soltanian-Zadeh, Hamid Kiani, Vahid Pourreza, Amid-Reza Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Machine Vision Research Laboratory Computer Engineering Department Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (纸本)9781479988174

This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and medium-view shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results. © 2015 IEEE.

关键词： Motion estimation

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Offline Handwritten Arabic Character Recognition Using Features Extracted from Curvelet and Spatial Domains

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Research Journal of Applied Sciences, engineering and Technology 2015年第2期11卷 158-164页

作者： Mazen Abdullah Bahashwan Syed Abd Rahman Abu-Bakar Computer Vision Video and Image Processing Research Lab (CvviP) Department of Electronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia

Arabic character recognition is a challenging problem in several artificial intelligence applications, especially when recognizing connected cursive letters. Another dimension of complexity is that Arabic characters may form various shapes depending on their positions in the word. As a result, unconstrained handwritten Arabic character recognition has not been well explored. In this study, we propose an efficient algorithm for Arabic character recognition. The new algorithm combines features extracted from curvelet and spatial domains. The curvelet domain is multiscale and multidirectional. Therefore, curvelet domain is efficient in representing edges and curves. Meanwhile, the spatial domain preserves original aspects of the characters. This feature vector is then trained using the back propagation neural network for the recognition task. The proposed algorithm is evaluated using a database containing 5,600 handwritten characters from 50 different writers. A promising average success rate of 90.3% has been achieved. Therefore, the proposed algorithm is suitable for the unconstrained handwritten Arabic character recognition applications.

关键词： Character database curvelet transform neural network optical character recognition

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