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| Author | Mehdi Mirza-Mohammadi; Sergio Escalera; Petia Radeva | ||||
| Title | Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization | Type | Conference Article | ||
| Year | 2009 | Publication  |
13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 5702 | Issue | Pages | 748–756 | |
| Keywords | |||||
| Abstract | Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | HuPBA; MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MEP2009 | Serial | 1185 | ||
| Permanent link to this record | |||||
| Author | Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke | ||||
| Title | Graph-based k-means clustering: A comparison of the set versus the generalized median graph | Type | Conference Article | ||
| Year | 2009 | Publication |
13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 5702 | Issue | Pages | 342–350 | |
| Keywords | |||||
| Abstract | In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. | ||||
| Address | Münster, Germany | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ FVS2009d | Serial | 1219 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Burnat; Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-Preserving Smoothing of Biomedical Images | Type | Conference Article | ||
| Year | 2009 | Publication |
13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 5702 | Issue | Pages | 427-434 | |
| Keywords | non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography. | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
| Address | Münster, Germany | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GHB2009 | Serial | 1527 | ||
| Permanent link to this record | |||||
| Author | David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title | Real-time Object Segmentation using a Bag of Features Approach | Type | Conference Article | ||
| Year | 2010 | Publication |
13th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | 220 | Issue | Pages | 321–329 | |
| Keywords | Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors | ||||
| Abstract | In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | IOS Press Amsterdam, | Place of Publication | Editor | In R.Alquezar, A.Moreno, J.Aguilar. | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 9781607506423 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ARL2010b | Serial | 1417 | ||
| Permanent link to this record | |||||
| Author | Eloi Puertas; Sergio Escalera; Oriol Pujol | ||||
| Title | Classifying Objects at Different Sizes with Multi-Scale Stacked Sequential Learning | Type | Conference Article | ||
| Year | 2010 | Publication |
13th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | 220 | Issue | Pages | 193–200 | |
| Keywords | |||||
| Abstract | Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we use the Multi-scale Stacked Sequential Learning approach (MSSL) to solve the task of pixel-wise classification based on contextual information. The main contribution of this work is a shifting technique applied during the testing phase that makes possible, thanks to template images, to classify objects at different sizes. The results show that the proposed method robustly classifies such objects capturing their spatial relationships. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | R. Alquezar, A. Moreno, J. Aguilar | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-60750-642-3 | Medium | ||
| Area | Expedition | Conference | CCIA | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ PEP2010 | Serial | 1448 | ||
| Permanent link to this record | |||||
| Author | Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders | ||||
| Title | Segmentation as Selective Search for Object Recognition | Type | Conference Article | ||
| Year | 2011 | Publication |
13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1879-1886 | ||
| Keywords | |||||
| Abstract | For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge. | ||||
| Address | Barcelona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | 978-1-4577-1101-5 | Medium | |
| Area | Expedition | Conference | ICCV | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ SUG2011 | Serial | 1780 | ||
| Permanent link to this record | |||||
| Author | E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer | ||||
| Title | Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images | Type | Conference Article | ||
| Year | 2011 | Publication |
13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 850-857 | ||
| Keywords | |||||
| Abstract | |||||
| Address | Barcelona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICCV | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ SRL2011 | Serial | 1803 | ||
| Permanent link to this record | |||||
| Author | Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca | ||||
| Title | A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes | Type | Conference Article | ||
| Year | 2011 | Publication |
13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1776-1783 | ||
| Keywords | |||||
| Abstract | Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance. | ||||
| Address | Barcelona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | 978-1-4577-1101-5 | Medium | |
| Area | Expedition | Conference | ICCV | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ CHM2011 | Serial | 1811 | ||
| Permanent link to this record | |||||
| Author | Mohammad Rouhani; Angel Sappa | ||||
| Title | Correspondence Free Registration through a Point-to-Model Distance Minimization | Type | Conference Article | ||
| Year | 2011 | Publication |
13th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 2150-2157 | ||
| Keywords | |||||
| Abstract | This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. | ||||
| Address | Barcelona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | 978-1-4577-1101-5 | Medium | |
| Area | Expedition | Conference | ICCV | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RoS2011b; ADAS @ adas @ | Serial | 1832 | ||
| Permanent link to this record | |||||
| Author | Shida Beigpour; Joost Van de Weijer | ||||
| Title | Object Recoloring Based on Intrinsic Image Estimation | Type | Conference Article | ||
| Year | 2011 | Publication |
13th IEEE International Conference in Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 327 - 334 | ||
| Keywords | |||||
| Abstract | Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods. | ||||
| Address | Barcelona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | 978-1-4577-1101-5 | Medium | |
| Area | Expedition | Conference | ICCV | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ BeW2011 | Serial | 1781 | ||
| Permanent link to this record | |||||
| Author | Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi | ||||
| Title | Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures | Type | Conference Article | ||
| Year | 2018 | Publication |
13th IEEE Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 250 - 257 | ||
| Keywords | |||||
| Abstract | Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate. | ||||
| Address | Xian; China; May 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ HBN2018 | Serial | 3117 | ||
| Permanent link to this record | |||||
| Author | Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning | Type | Conference Article | ||
| Year | 2019 | Publication |
13th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 80-85 | ||
| Keywords | Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning | ||||
| Abstract | With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of sketches, showing promising results. |
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| Address | Sydney; Australia; September 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.140; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRF2019 | Serial | 3354 | ||
| Permanent link to this record | |||||
| Author | Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters | Type | Conference Article | ||
| Year | 2018 | Publication |
13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 97-102 | ||
| Keywords | Robust Reading; End-to-end Systems; CNN; Utility Meters | ||||
| Abstract | In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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| Address | Viena; Austria; April 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GRK2018 | Serial | 3102 | ||
| Permanent link to this record | |||||
| Author | Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou | ||||
| Title | The Robust Reading Competition Annotation and Evaluation Platform | Type | Conference Article | ||
| Year | 2018 | Publication |
13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 61-66 | ||
| Keywords | |||||
| Abstract | The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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| Address | Viena; Austria; April 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | KGR2018 | Serial | 3103 | ||
| Permanent link to this record | |||||
| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Manuscript text line detection and segmentation using second-order derivatives analysis | Type | Conference Article | ||
| Year | 2018 | Publication |
13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 293 - 298 | ||
| Keywords | text line detection; text line segmentation; text region detection; second-order derivatives | ||||
| Abstract | In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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| Address | Viena; Austria; April 2018 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 302.065; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ AlR2018a | Serial | 3104 | ||
| Permanent link to this record | |||||