Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073600
L. Grama, C. Rusu
In this paper, we study several audio classification schemes applied on different number of features for multiclass classification with imbalanced datasets. As features, we proposed the liftering Mel frequency cepstral coefficients, while for classification we use probabilistic methods, instance-based learning algorithms, support vector machines, neural networks, L∞-norm based classifier, fuzzy lattice reasoning classifier, and trees. The final goal is to find the appropriate number of liftering Mel frequency cepstral coefficients to provide the desired accuracy for audio classification purpose. The best results are obtained using 16 features and & k-Nearest Neighbor as a classifier. In this case, the correct classification rate is 99.79%, the false alarm rate is 0.05%, the miss rate is 0.21%, the precision is 99.80% and the F-measure is 99.79%.
{"title":"Choosing an accurate number of mel frequency cepstral coefficients for audio classification purpose","authors":"L. Grama, C. Rusu","doi":"10.1109/ISPA.2017.8073600","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073600","url":null,"abstract":"In this paper, we study several audio classification schemes applied on different number of features for multiclass classification with imbalanced datasets. As features, we proposed the liftering Mel frequency cepstral coefficients, while for classification we use probabilistic methods, instance-based learning algorithms, support vector machines, neural networks, L∞-norm based classifier, fuzzy lattice reasoning classifier, and trees. The final goal is to find the appropriate number of liftering Mel frequency cepstral coefficients to provide the desired accuracy for audio classification purpose. The best results are obtained using 16 features and & k-Nearest Neighbor as a classifier. In this case, the correct classification rate is 99.79%, the false alarm rate is 0.05%, the miss rate is 0.21%, the precision is 99.80% and the F-measure is 99.79%.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073562
Maja Temerinac-Ott, G. Forestier, J. Schmitz, M. Hermsen, J. H. Braseni, F. Feuerhake, Cédric Wemmert
We evaluate the detection of glomerular structures in whole slide images (WSIs) of histopathological slides stained with multiple histochemical and immuno-histochemical staining using a convolutional neural network (CNN) based approach. We mutually compare the CNN performance on different stainings (Jones H&E, PAS, Sirius Red and CDIO) and we present a novel approach to improve glomeruli detection on one staining by taking into account the classification results from differently stained consecutive sections of the same tissue. Using this integrative approach, the detection rate (Fl-score) on a single stain can be improved by up to 30%.
{"title":"Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities","authors":"Maja Temerinac-Ott, G. Forestier, J. Schmitz, M. Hermsen, J. H. Braseni, F. Feuerhake, Cédric Wemmert","doi":"10.1109/ISPA.2017.8073562","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073562","url":null,"abstract":"We evaluate the detection of glomerular structures in whole slide images (WSIs) of histopathological slides stained with multiple histochemical and immuno-histochemical staining using a convolutional neural network (CNN) based approach. We mutually compare the CNN performance on different stainings (Jones H&E, PAS, Sirius Red and CDIO) and we present a novel approach to improve glomeruli detection on one staining by taking into account the classification results from differently stained consecutive sections of the same tissue. Using this integrative approach, the detection rate (Fl-score) on a single stain can be improved by up to 30%.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134355287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073559
Borja Bovcon, Rok Mandeljc, J. Pers, M. Kristan
We present a new semantic segmentation algorithm for obstacle detection in unmanned surface vehicles. The novelty lies in the graphical model that incorporates boat tilt measurements from the on-board inertial measurement unit (IMU). The IMU readings are used to estimate the location of horizon line in the image, and automatically adjusts the priors in the probabilistic semantic segmentation algorithm. We derive the necessary horizon projection equations, an efficient optimization algorithm for the proposed graphical model, and a practical IMU-camera-USV calibration. A new challenging dataset, which is the largest multi-sensor dataset of its kind, is constructed. Results show that the proposed algorithm significantly outperforms state of the art, with 32% improvement in water-edge detection accuracy, an over 15 % reduction of false positive rate, an over 70 % reduction of false negative rate, and an over 55 % increase of true positive rate, while running in real-time on a single core in Matlab.
{"title":"Improving vision-based obstacle detection on USV using inertial sensor","authors":"Borja Bovcon, Rok Mandeljc, J. Pers, M. Kristan","doi":"10.1109/ISPA.2017.8073559","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073559","url":null,"abstract":"We present a new semantic segmentation algorithm for obstacle detection in unmanned surface vehicles. The novelty lies in the graphical model that incorporates boat tilt measurements from the on-board inertial measurement unit (IMU). The IMU readings are used to estimate the location of horizon line in the image, and automatically adjusts the priors in the probabilistic semantic segmentation algorithm. We derive the necessary horizon projection equations, an efficient optimization algorithm for the proposed graphical model, and a practical IMU-camera-USV calibration. A new challenging dataset, which is the largest multi-sensor dataset of its kind, is constructed. Results show that the proposed algorithm significantly outperforms state of the art, with 32% improvement in water-edge detection accuracy, an over 15 % reduction of false positive rate, an over 70 % reduction of false negative rate, and an over 55 % increase of true positive rate, while running in real-time on a single core in Matlab.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129261810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073579
Manfred Klopschitz, R. Perko, G. Lodron, G. Paar, H. Mayer
Active consumer grade depth sensors have motivated recent research on volumetric depth map fusion. This led to the development of new, efficient, video-rate integration and tracking methods. These approaches still suffer from the geometric inaccuracies of the input depth maps of consumer grade depth sensors. This paper presents a practical stereo system that combines highly accurate and robust projected texture stereo and efficient volumetric integration and allows to easily capture accurate 3D models of indoor scenes. We describe a stereo method that is optimized for random dot projection patterns and delivers complete and robust results. We also show the complementing hardware setup that delivers accurate, complete depth maps. Results of a real-world scene are compared to ground truth data.
{"title":"Projected texture fusion","authors":"Manfred Klopschitz, R. Perko, G. Lodron, G. Paar, H. Mayer","doi":"10.1109/ISPA.2017.8073579","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073579","url":null,"abstract":"Active consumer grade depth sensors have motivated recent research on volumetric depth map fusion. This led to the development of new, efficient, video-rate integration and tracking methods. These approaches still suffer from the geometric inaccuracies of the input depth maps of consumer grade depth sensors. This paper presents a practical stereo system that combines highly accurate and robust projected texture stereo and efficient volumetric integration and allows to easily capture accurate 3D models of indoor scenes. We describe a stereo method that is optimized for random dot projection patterns and delivers complete and robust results. We also show the complementing hardware setup that delivers accurate, complete depth maps. Results of a real-world scene are compared to ground truth data.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073574
B. Harangi, A. Hajdu, R. Lampé, P. Torok
Endoscope-based surgery has several beneficial effects regarding the rehabilitation of the patients, but has some drawbacks causing difficulties to medical experts, on the contrary. The main disadvantage is that the tactile information is lost to the expert who takes the surgical intervention. There are some organs (e.g. ureters and arteries) in the human body which have similar visual appearances, so the differentiation of them based on only visual expression via endoscopy is a challenging task to the medical experts. To support keyhole-surgery using state-of-the-art image processing solutions, we have developed a semi-automatic software which can distinguish ureters from arteries by a dedicated convolutional neural network (CNN). We have trained the CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones. 94.2% accuracy has been achieved in this two-classes classification task regarding a binary error function.
{"title":"Differentiating ureter and arteries in the pelvic via endoscope using deep neural network","authors":"B. Harangi, A. Hajdu, R. Lampé, P. Torok","doi":"10.1109/ISPA.2017.8073574","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073574","url":null,"abstract":"Endoscope-based surgery has several beneficial effects regarding the rehabilitation of the patients, but has some drawbacks causing difficulties to medical experts, on the contrary. The main disadvantage is that the tactile information is lost to the expert who takes the surgical intervention. There are some organs (e.g. ureters and arteries) in the human body which have similar visual appearances, so the differentiation of them based on only visual expression via endoscopy is a challenging task to the medical experts. To support keyhole-surgery using state-of-the-art image processing solutions, we have developed a semi-automatic software which can distinguish ureters from arteries by a dedicated convolutional neural network (CNN). We have trained the CNN on 2000 images acquired during endoscopic surgery and tested on 500 test ones. 94.2% accuracy has been achieved in this two-classes classification task regarding a binary error function.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073596
Maissa Chagmani, B. Xerri, B. Borloz, C. Jauffret
This paper introduces a new fast algorithm named CSMFST which estimates the p-dimensional optimal subspace, i.e. where the signal-to-noise ratio is maximized in the case of n-dimensional nonstationary signals. We assume that we treat both signal and noise which are characterized by their samples. This algorithm is an SP-type algorithm and uses the same principles as the Yet Another Subspace Tracking (YAST) algorithm when estimating the covariance matrices. At each step, it estimates a matrix which spans the optimal subspace.
{"title":"The constrained stochastic matched filter subspace tracking","authors":"Maissa Chagmani, B. Xerri, B. Borloz, C. Jauffret","doi":"10.1109/ISPA.2017.8073596","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073596","url":null,"abstract":"This paper introduces a new fast algorithm named CSMFST which estimates the p-dimensional optimal subspace, i.e. where the signal-to-noise ratio is maximized in the case of n-dimensional nonstationary signals. We assume that we treat both signal and noise which are characterized by their samples. This algorithm is an SP-type algorithm and uses the same principles as the Yet Another Subspace Tracking (YAST) algorithm when estimating the covariance matrices. At each step, it estimates a matrix which spans the optimal subspace.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126412699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073584
Uros Petkovic, Robert Korez, T. Vrtovec
The Cobb angle is the main diagnostic parameter for evaluating spinal deformities. Traditionally, it is measured on two-dimensional coronal radiographic (X-ray) images. In this study, we present a semi-automated algorithm for the evaluation of the Cobb angle from three-dimensional mesh models of the spine. The method was tested on 22 spine models, and the obtained mean absolute error of 2.89° against reference measurements indicates that the method performs well.
{"title":"Semi-automated measurement of the cobb angle from 3D mesh models of the scoliotic spine","authors":"Uros Petkovic, Robert Korez, T. Vrtovec","doi":"10.1109/ISPA.2017.8073584","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073584","url":null,"abstract":"The Cobb angle is the main diagnostic parameter for evaluating spinal deformities. Traditionally, it is measured on two-dimensional coronal radiographic (X-ray) images. In this study, we present a semi-automated algorithm for the evaluation of the Cobb angle from three-dimensional mesh models of the spine. The method was tested on 22 spine models, and the obtained mean absolute error of 2.89° against reference measurements indicates that the method performs well.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134235133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073587
A. Nurhadiyatna, S. Lončarić
A typical traffic monitoring system for pedestrian detection uses a stationary camera. In Advanced Driving Assistance Systems (ADAS), the camera is mounted in front of the vehicle's window so that the camera and the object move in any arbitrary direction. Semantic image segmentation is widely used for road scene interpretation. In this paper, a method for semantic image segmentation using a convolution neural network is proposed. After a candidate region is segmented we perform pedestrian detection based on shape and size features of the candidate region. The experiments show that the proposed approach can accurately detect pedestrians in real-time (40fps).
{"title":"Semantic image segmentation for pedestrian detection","authors":"A. Nurhadiyatna, S. Lončarić","doi":"10.1109/ISPA.2017.8073587","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073587","url":null,"abstract":"A typical traffic monitoring system for pedestrian detection uses a stationary camera. In Advanced Driving Assistance Systems (ADAS), the camera is mounted in front of the vehicle's window so that the camera and the object move in any arbitrary direction. Semantic image segmentation is widely used for road scene interpretation. In this paper, a method for semantic image segmentation using a convolution neural network is proposed. After a candidate region is segmented we perform pedestrian detection based on shape and size features of the candidate region. The experiments show that the proposed approach can accurately detect pedestrians in real-time (40fps).","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116918179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073560
Øyvind Meinich-Bache, K. Engan, T. S. Birkenes, H. Myklebust
Globally one of our major mortality challenges is out-of-hospital cardiac arrest. Good quality cardiopulmonary resuscitation (CPR) is extremely important for the chance of survival after cardiac arrest. Research has shown that telephone assisted guidance from the dispatcher to the bystander can improve the CPR quality provided to the patient. Some recent work has proposed to use the accelerometer in a bystander's smartphone to estimate compression rates, but this demands the phone to be placed on the patient during compression. Our research group has previously proposed a real-time application for bystander and dispatcher feedback using the smartphone camera to estimate the chest compression rate while the smartphone is placed flat on the ground. Some shortcomings were observed with the application in high noise situations. In this paper we propose a robust method where we have modeled and parametrized the power specter density to distinguish between noisy situations, improved the update procedure for the dynamic region of interest and added post-processing steps to suppress noise. The proposed method provides excellent results with acceptable performance at 99.8% of the time testing different rates in high and low noise situations, 99.5% in a disturbance test, and 92.5% of the time during random movements.
{"title":"Robust real-time chest compression rate detection from smartphone video","authors":"Øyvind Meinich-Bache, K. Engan, T. S. Birkenes, H. Myklebust","doi":"10.1109/ISPA.2017.8073560","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073560","url":null,"abstract":"Globally one of our major mortality challenges is out-of-hospital cardiac arrest. Good quality cardiopulmonary resuscitation (CPR) is extremely important for the chance of survival after cardiac arrest. Research has shown that telephone assisted guidance from the dispatcher to the bystander can improve the CPR quality provided to the patient. Some recent work has proposed to use the accelerometer in a bystander's smartphone to estimate compression rates, but this demands the phone to be placed on the patient during compression. Our research group has previously proposed a real-time application for bystander and dispatcher feedback using the smartphone camera to estimate the chest compression rate while the smartphone is placed flat on the ground. Some shortcomings were observed with the application in high noise situations. In this paper we propose a robust method where we have modeled and parametrized the power specter density to distinguish between noisy situations, improved the update procedure for the dynamic region of interest and added post-processing steps to suppress noise. The proposed method provides excellent results with acceptable performance at 99.8% of the time testing different rates in high and low noise situations, 99.5% in a disturbance test, and 92.5% of the time during random movements.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114618900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073594
A. Abreu, F. Frenois, S. Valitutti, P. Brousset, P. Denéfle, B. Naegel, Cédric Wemmert
In biology and pathology immunofluorescence microscopy approaches are leading techniques for deciphering of the molecular mechanisms of cell activation and disease progression. Although several commercial softwares for image analysis are presently in the market, available solutions do not allow a totally non subjective image analysis. There is therefore strong need for new methods that could allow a completely non-subjective image analysis procedure including for thresholding and for choice of the objects of interest. To address this need, we describe a fully automatic segmentation of cell nuclei in 3-D confocal immunofluorescence images. The method merges segments of the image to fit with a nuclei model learned by a trained random forest classifier. The merging procedure explores efficiently the fusion configurations space of an over-segmented image by using minimum spanning trees of its region adjacency graph.
{"title":"Optimal cut in minimum spanning trees for 3-D cell nuclei segmentation","authors":"A. Abreu, F. Frenois, S. Valitutti, P. Brousset, P. Denéfle, B. Naegel, Cédric Wemmert","doi":"10.1109/ISPA.2017.8073594","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073594","url":null,"abstract":"In biology and pathology immunofluorescence microscopy approaches are leading techniques for deciphering of the molecular mechanisms of cell activation and disease progression. Although several commercial softwares for image analysis are presently in the market, available solutions do not allow a totally non subjective image analysis. There is therefore strong need for new methods that could allow a completely non-subjective image analysis procedure including for thresholding and for choice of the objects of interest. To address this need, we describe a fully automatic segmentation of cell nuclei in 3-D confocal immunofluorescence images. The method merges segments of the image to fit with a nuclei model learned by a trained random forest classifier. The merging procedure explores efficiently the fusion configurations space of an over-segmented image by using minimum spanning trees of its region adjacency graph.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126923359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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