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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Adrian Carrio Carlos Sampedro Changhong Fu Jean-François Collumeau Pascual Campoy Computer Vision Group Center for Automation and Robotics (UPM-CSIC) Madrid Spain School of Mechanical and Aerospace Engineering and ST Engineering-NTU Corp Laboratory Nanyang Technological University Singapore

vision-based sky segmentation and horizon line detection can be extremely useful to perform important tasks onboard Unmanned Aerial Vehicles (UAVs), such as pose estimation and collision avoidance. Most of the existing vision-based solutions use traditional image processing methods to identify the horizon line. This results in good overall accuracy and fast computation times. However, difficult environmental conditions such as a foggy or cloudy skies hinder correct sky segmentation. This paper proposes a solution for sky segmentation in RGB images using a supervised Machine Learning approach by first splitting the image into fixed-size patches, extracting and classifying color descriptors for each patch and performing a final post-processing stage to improve segmentation quality. A method for automatic horizon line detection is also proposed. The performance of our approach was evaluated on flight images captured onboard UAVs, achieving performance accuracies above 93% at real-time frame rates.

关键词： Image color analysis Image segmentation Aircraft Image edge detection Histograms Real-time systems Unmanned aerial vehicles

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A vision based aerial robot solution for the Mission 7 of the International Aerial robotics Competition

A vision based aerial robot solution for the Mission 7 of th...

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2015 International Conference on Unmanned Aircraft Systems, ICUAS 2015

作者： Sanchez-Lopez, Jose Luis Pestana, Jesus Collumeau, Jean-Franois Suarez-Fernandez, Ramon Campoy, Pascual Molina, Martin Computer Vision Group Centre for Automation and Robotics CSICUPM Spain Aerial Vision Group Graz University of Technology - TUGraz Austria Mekelweg Netherlands Spain

ISBN: (纸本)9781479960101

The International Aerial robotics Competition (IARC) aims at pulling forward the state of the art in UAV. The Mission's 7 challenge deals mainly with GPS/Laser denied navigation, Robot-Robot interaction and obstacle avoidance in the setting of a ground robot herding problem. We present in this paper our UAV which took part in the 2014 competition, in the China venue. This year, the mission was not completed by any participant but our team at Technical University of Madrid (UPM) were awarded with two special prizes: Best Target Detection and Best System Control. The platform, hardware and the developed software used in this top leading competition are presented in this paper. This software has three main components: the visual localization and mapping algorithm;the control algorithms;and the mission planner. A statement of the safety measures integrated in the drone and of our efforts to ensure field testing in conditions as close as possible to the challenger's are also included. © 2015 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Rapid quantification of total polyphenol content in EVOO using NIR sensor with wavelength selection and FS-MLR 12

Rapid quantification of total polyphenol content in EVOO usi...

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12th IEEE International Conference on Imaging Systems and Techniques, IST 2015

作者： Martínez Gila, D.M. Beltrán Ortega, J. Gámez García, J.G. Gómez Ortega, J. Robotics Automation and Computer Vision Group University of Jaen Campus Las Lagunillas s/n JaenES-23071 Spain

ISBN: (纸本)9781479986330

Olive oil quality is regulated by an European normative, and according to different chemical and organoleptic markers the olive oil can be labelled as lampante, virgin or extra virgin olive oil (EVOO), being the last the top-quality one. Since these parameters determine mainly the olive oil chemical stability, in the last years different works have studied the favourable influence of others olive oil minor components on the human health. Polyphenols are one of these minor components. This research has been carried out to study the correlation between the polyphenols contained into EVOO and its spectral response in the near infrared. The selection of the best correlated spectral wavelengths could be the first step to build a rapid and low-cost NIR sensor to be set up into the elaboration process in order to control the presence of these components. During the process, several olive oil variables such as temperature, humidity, turbidity.. could affect to the NIR response. In this sense, this work has studied the influence of the olive oil turbidity on the error of polyphenol content prediction. For this study 63 olive oil samples obtained from an olive oil mill have been employed and the reference values were obtained by an external laboratory with chemical analysis. Spectra were filtered with an ANOVA analysis and the best correlated band was identified between 1690 and 1870 nm. The regression algorithm FSMLR was used and it got a RMSECV of 75 ppm, the R2 coefficient was 0.94 and the RPD value was 4.76. The results get worse when samples with different turbidity indexes were used. © 2015 IEEE.

关键词： Olive oil

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Automatic determination of peroxides and acidity of olive oil using machine vision in olive fruits before milling process 12

Automatic determination of peroxides and acidity of olive oi...

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12th IEEE International Conference on Imaging Systems and Techniques, IST 2015

作者： Morene, O. Cáceres Gila, D. M. Martínez Puerto, D. Aguilera García, J. Gámez Ortega, J. Gómez ES-23600 Spain Robotics Automation and Computer Vision Group University of Jaén Campus Las Lagunillas S/n JaénES-23071 Spain

ISBN: (纸本)9781479986330

Ones of the most important parameters to measure of olive oil quality are acidity and peroxide index and, currently, they are measured in a laboratory using samples of olives extracted from a batch in the reception process. The aim of this work is to provide an automatic inspection system, based on computer vision-visible and infrared (IR) channels-, to infer automatically these parameters. The proposal uses the differences in superficial textures, the defects in IR pictures and a color estimation in CIELab. Furthermore, a different image preprocessing has been employed and an Artificial Neural Networks have been used as estimation technique. The system has reached good estimation results with R=96.3 in acidity and R=93.9 in peroxide index. © 2015 IEEE.

关键词： computer vision

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Olive batches automatic classification in mill reception using computer vision 12

Olive batches automatic classification in mill reception usi...

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12th IEEE International Conference on Imaging Systems and Techniques, IST 2015

作者： Puerto, D. Aguilera Moreno, O. Cáceres Gila, D.M. Martínez García, J. Gámez Ortega, J. Gómez ES-23600 Spain Robotics Automation and Computer Vision Group University of Jaén Campus Las Lagunillas s/n JaénES-23071 Spain

ISBN: (纸本)9781479986330

Olive batches classification is crucial to obtain the best possible oil in milling extracting process. Nowadays, this selection is done manually and it takes place before starting the milling process. This work proposes an automatic classification system of olives batch, based on computer vision, whose goal is the online differentiation of the olives batch according to their quality level. The proposed system has been installed and tested in production condition, working with real lots of olives brought to the mill for the famer. Two full setups have been installed in the factory: before and after the olive washing process. The proposed methodology uses a feature vector of the samples that concatenates the olive image histograms from different colour spaces and the result values of two algorithms used to determine the texture (image entropy and grey level co-occurrence matrix). As classifier, an artificial neural network (ANN) was used. For the experimental validation, 6325 images from 100 batches were analysed showing good classification results (success ratios of 97.1% before the washing stage and 96.4% after). © 2015 IEEE.

关键词： computer vision

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Rapid quantification of total polyphenol content in EVOO using NIR sensor with wavelength selection and FS-MLR

Rapid quantification of total polyphenol content in EVOO usi...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： D.M. Martínez Gila J. Beltrán Ortega J. Gámez García J. Gómez Ortega Robotics Automation and Computer Vision Group Universidad de Jaen Jaen JaÃ©n ES Robotics Automation and Computer Vision Group University of Jaen Jaen (Spain)

Olive oil quality is regulated by an European normative, and according to different chemical and organoleptic markers the olive oil can be labelled as lampante, virgin or extra virgin olive oil (EVOO), being the last the top-quality one. Since these parameters determine mainly the olive oil chemical stability, in the last years different works have studied the favourable influence of others olive oil minor components on the human health. Polyphenols are one of these minor components. This research has been carried out to study the correlation between the polyphenols contained into EVOO and its spectral response in the near infrared. The selection of the best correlated spectral wavelengths could be the first step to build a rapid and low-cost NIR sensor to be set up into the elaboration process in order to control the presence of these components. During the process, several olive oil variables such as temperature, humidity, turbidity... could affect to the NIR response. In this sense, this work has studied the influence of the olive oil turbidity on the error of polyphenol content prediction. For this study 63 olive oil samples obtained from an olive oil mill have been employed and the reference values were obtained by an external laboratory with chemical analysis. Spectra were filtered with an ANOVA analysis and the best correlated band was identified between 1690 and 1870 nm. The regression algorithm FSMLR was used and it got a RMSECV of 75 ppm, the R 2 coefficient was 0.94 and the RPD value was 4.76. The results get worse when samples with different turbidity indexes were used.

关键词： Analysis of variance Indexes Chemicals Spectroscopy Absorption Compounds Calibration

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Automatic determination of peroxides and acidity of olive oil using machine vision in olive fruits before milling process

Automatic determination of peroxides and acidity of olive oi...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： O. Cáceres Morene D.M. Martínez Gila D. Aguilera Puerto J. Gámez García J. Gómez Ortega Robotics Automation and Computer Vision Group Universidad de Jaen Jaen JaÃ©n ES Plastic Technological Center ANDALTEC Jaen Spain Robotics Automation and Computer Vision Group University of Jaén Spain

关键词： Indexes Image color analysis Estimation Image segmentation computer vision Production Artificial neural networks

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Automatic classification of olives for oil production using computer vision

Automatic classification of olives for oil production using ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： D. Martinez Gila D. Aguilera Puerto J. Gámez García J. Gómez Ortega Group of Robotics Automation and Computer Vision University of Jaén. Agrifood Campus of International Excellence Jaén Spain

ISBN: (纸本)9781479978014

One of the most important parameters in olive oil production is the correct reception and classification of olive fruits in batches before starting the oil extraction process. This work proposes an automatic inspection system based on computer vision to classify automatically different lots of olives for oil production when the milling process starts. The classification is based on the differentiation, on line, between ground and tree olives. For this purpose, the samples of olives have been obtained by picking berries directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image processing algorithms have been employed and lineal classification techniques such as Fisher Discriminant Analysis. The system has reached good classification results distinguishing between soil and tree olives batches with success ratios of 100%.

关键词： Soil Production Histograms computer vision Image color analysis Cameras Principal component analysis

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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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Olive batches automatic classification in mill reception using computer vision

Olive batches automatic classification in mill reception usi...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： D. Aguilera Puerto O. Caceres Moreno D. M. Martinez Gila J. Gamez Garcia J. Gomez Ortega Plastic Technological Center ANDALTEC Spain Robotics Automation and Computer Vision Group Universidad de Jaen Jaen JaÃ©n ES

关键词： Image color analysis Cleaning Entropy Histograms Machine vision computer vision Artificial neural networks

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