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Author  |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
Context-aware features and robust image representations |
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Journal Article |
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Year |
2014 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
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Volume |
25 |
Issue |
2 |
Pages |
339-348 |
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Abstract |
Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. |
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LAMP; 600.079;MILAB |
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Admin @ si @ MCG2014 |
Serial |
2467 |
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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
On the completeness of feature-driven maximally stable extremal regions |
Type |
Journal Article |
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Year |
2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
74 |
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Pages |
9-16 |
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Keywords |
Local features; Completeness; Maximally Stable Extremal Regions |
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By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. |
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Elsevier B.V. |
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0167-8655 |
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LAMP;MILAB; |
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Admin @ si @ MCG2016 |
Serial |
2748 |
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Author |
Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Robust non-blind color video watermarking using QR decomposition and entropy analysis |
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Journal Article |
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Year |
2016 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
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Volume |
38 |
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Pages |
838-847 |
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Keywords |
Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition |
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Abstract |
Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. |
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HuPBA;MILAB; |
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no |
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Call Number |
Admin @ si @RSA2016 |
Serial |
2766 |
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Author |
Pejman Rasti; Tonis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring |
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Conference Article |
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Year |
2016 |
Publication |
9th Conference on Articulated Motion and Deformable Objects |
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Palma de Mallorca; Spain; July 2016 |
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AMDO |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ RUE2016 |
Serial |
2846 |
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Author |
Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera |
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Title |
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images |
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Journal Article |
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Year |
2022 |
Publication |
PloS One |
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Plos |
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Volume |
17 |
Issue |
5 |
Pages |
e0267759 |
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Abstract |
Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. |
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Public Library of Science |
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HuPBA |
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no |
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Call Number |
Admin @ si @ TCC2022 |
Serial |
3743 |
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Author |
Petia Radeva |
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Title |
A Rule-Based Approach to Hand X-Ray image Segmentation. |
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Miscellaneous |
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1993 |
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MILAB |
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no |
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Call Number |
BCNPCL @ bcnpcl @ Rad1993a |
Serial |
171 |
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Author |
Petia Radeva |
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Title |
Segmentacion de Imagenes Radiograficas con Snakes. Aplicacion a la Determinacion de la Madurez Osea. |
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Miscellaneous |
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Year |
1993 |
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Master Thesis, UPPIA, Universitat Autonoma de Barcelona. |
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MILAB |
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BCNPCL @ bcnpcl @ Rad1993b |
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173 |
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Author |
Petia Radeva |
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Title |
On the Role of Intravascular Ultrasound Image Analysis |
Type |
Miscellaneous |
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Year |
2003 |
Publication |
Angiography and Plaque Imaging: Advanced Segmentation Techniques, CRC, pp.397–450, ISBN: 0849317401 |
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MILAB |
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BCNPCL @ bcnpcl @ Rad2003 |
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402 |
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Author |
Petia Radeva |
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Title |
Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? |
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2016 |
Publication |
19th International Conference of the Catalan Association for Artificial Intelligence |
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4 |
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Barcelona; October 2016 |
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CCIA |
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MILAB |
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no |
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Call Number |
Admin @ si @ Rad2016 |
Serial |
2832 |
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Author |
Petia Radeva |
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Title |
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis |
Type |
Conference Article |
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2020 |
Publication |
1st DELTA |
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MILAB |
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no |
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Admin @ si @ Rad2020 |
Serial |
3527 |
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Author |
Petia Radeva; A.Amini; J.Huang; Enric Marti |
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Title |
Deformable B-Solids and Implicit Snakes for Localization and Tracking of SPAMM MRI-Data |
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Conference Article |
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1996 |
Publication |
Workshop on Mathematical Methods in Biomedical Image Analysis |
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192-201 |
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To date, MRI-SPAMM data from different image slices have been analyzed independently. In this paper, we propose an approach for 3D tag localization and tracking of SPAMM data by a novel deformable B-solid. The solid is defined in terms of a 3D tensor product B-spline. The isoparametric curves of the B-spline solid have special importance. These are termed implicit snakes as they deform under image forces from tag lines in different image slices. The localization and tracking of tag lines is performed under constraints of continuity and smoothness of the B-solid. The framework unifies the problems of localization, and displacement fitting and interpolation into the same procedure utilizing B-spline bases for interpolation. To track motion from boundaries and restrict image forces to the myocardium, a volumetric model is employed as a pair of coupled endocardial and epicardial B-spline surfaces. To recover deformations in the LV an energy-minimization problem is posed where both tag and ... |
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San Francisco CA |
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IEEE Computer Society |
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0-8186-7368-0 |
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MMBIA ’96 |
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MILAB;IAM; |
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IAM @ iam @ RAH1996 |
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1630 |
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Author |
Petia Radeva; A.F. Sole; Antonio Lopez; Joan Serrat |
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Title |
Detecting Nets of Linear Structures in Satellite Images. |
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Miscellaneous |
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1998 |
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Londres |
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ADAS;MILAB |
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ADAS @ adas @ RSL1998 |
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25 |
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Petia Radeva; A.F. Sole; Antonio Lopez; Joan Serrat |
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Detecting Nets of Linear Structures in Satellite Images. |
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Miscellaneous |
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1999 |
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Machine Vision and Advanced Image Processing in Remote Sensing, Springer, 304–316. |
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ADAS @ adas @ RSL1999 |
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34 |
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Author |
Petia Radeva; Amir Amini; Jintao Huang; Enric Marti |
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Title |
Deformable B-Solids: application for localization and tracking of MRI-SPAMM data |
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1996 |
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CVC Technical Report |
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8 |
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To date, MRI-SPAMM data from different image slices have been analyzed independently. In this paper, we propose an approach for 3D tag localization and tracking of SPAMM data by a novel deformable B-solid. The solid is defined in terms of a 3D tensor product B-spline. The isoparametric curves of the B-spline solid have special importance. These are termed implicit snakes as they deform under image forces from tag lines in different image slices. The localization and tracking of tag lines is performed under constraints of continuity and smoothness of the B-solid. The framework unifies the problems of localization, and displacement fitting and interpolation into the same procedure utilizing B-spline bases for interpolation. To track motion from boundaries and restrict image forces to the myocardium, a volumetric model is employed as a pair of coupled endocardial and epicardial B-spline surfaces. To recover deformations in the LV an energy-minimization problem is posed where both tag and ... |
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CVC (UAB) |
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MILAB;IAM |
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IAM @ iam @ RHM1996 |
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1631 |
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Author |
Petia Radeva; Cristina Cañero; Juan J. Villanueva; J. Mauri; E Fernandez-Nofrerias |
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Title |
3D Reconstruction of a Stent by Deformable Models. |
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Miscellaneous |
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2001 |
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Proceedings of the IASTED International Conference, Visualization, Imaging and Image Processing, 417–422. |
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Abstract |
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Address |
Marbella. |
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Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Edition |
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ISSN |
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ISBN |
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Expedition |
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Conference |
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Notes |
MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ RCV2001 |
Serial |
158 |
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