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| Author | Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier | ||||
| Title | Réduction de l’espace de recherche pour les personnages de bandes dessinées | Type | Conference Article | ||
| Year | 2014 | Publication | 19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | contextual search; document analysis; comics characters | ||||
| Abstract | Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%. | ||||
| Address | Rouen; Francia; July 2014 | ||||
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| Area | Expedition | Conference | RFIA | ||
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DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GRB2014 | Serial | 2480 | ||
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| Author | Christophe Rigaud; Clement Guerin | ||||
| Title | Localisation contextuelle des personnages de bandes dessinées | Type | Conference Article | ||
| Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
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| Abstract | Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%. | ||||
| Address | Nancy; Francia; March 2014 | ||||
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| Area | Expedition | Conference | CIFED | ||
| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RiG2014 | Serial | 2481 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Scene Text Recognition: No Country for Old Men? | Type | Conference Article | ||
| Year | 2014 | Publication | 1st International Workshop on Robust Reading | Abbreviated Journal | |
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| Area | Expedition | Conference | IWRR | ||
| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GoK2014c | Serial | 2538 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 156-160 | ||
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| Abstract | This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2014 | Serial | 2543 | ||
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| Author | Antonio Clavelli | ||||
| Title | A computational model of eye guidance, searching for text in real scene images | Type | Book Whole | ||
| Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Searching for text objects in real scene images is an open problem and a very active computer vision research area. A large number of methods have been proposed tackling the text search as extension of the ones from the document analysis field or inspired by general purpose object detection methods. However the general problem of object search in real scene images remains an extremely challenging problem due to the huge variability in object appearance. This thesis builds on top of the most recent findings in the visual attention literature presenting a novel computational model of eye guidance aiming to better describe text object search in real scene images.
First are presented the relevant state-of-the-art results from the visual attention literature regarding eye movements and visual search. Relevant models of attention are discussed and integrated with recent observations on the role of top-down constraints and the emerging need for a layered model of attention in which saliency is not the only factor guiding attention. Visual attention is then explained by the interaction of several modulating factors, such as objects, value, plans and saliency. Then we introduce our probabilistic formulation of attention deployment in real scene. The model is based on the rationale that oculomotor control depends on two interacting but distinct processes: an attentional process that assigns value to the sources of information and motor process that flexibly links information with action. In such framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the reward of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. In the experimental section the model is tested in laboratory condition, comparing model simulations with data from eye tracking experiments. The comparison is qualitative in terms of observable scan paths and quantitative in terms of statistical similarity of gaze shift amplitude. Experiments are performed using eye tracking data from both a publicly available dataset of face and text and from newly performed eye-tracking experiments on a dataset of street view pictures containing text. The last part of this thesis is dedicated to study the extent to which the proposed model can account for human eye movements in a low constrained setting. We used a mobile eye tracking device and an ad-hoc developed methodology to compare model simulated eye data with the human eye data from mobile eye tracking recordings. Such setting allow to test the model in an incomplete visual information condition, reproducing a close to real-life search task. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Dimosthenis Karatzas;Giuseppe Boccignone;Josep Llados | |
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| ISSN | ISBN | 978-84-940902-6-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Cla2014 | Serial | 2571 | ||
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| Author | Jon Almazan | ||||
| Title | Learning to Represent Handwritten Shapes and Words for Matching and Recognition | Type | Book Whole | ||
| Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Writing is one of the most important forms of communication and for centuries, handwriting had been the most reliable way to preserve knowledge. However, despite the recent development of printing houses and electronic devices, handwriting is still broadly used for taking notes, doing annotations, or sketching ideas.
Transferring the ability of understanding handwritten text or recognizing handwritten shapes to computers has been the goal of many researches due to its huge importance for many different fields. However, designing good representations to deal with handwritten shapes, e.g. symbols or words, is a very challenging problem due to the large variability of these kinds of shapes. One of the consequences of working with handwritten shapes is that we need representations to be robust, i.e., able to adapt to large intra-class variability. We need representations to be discriminative, i.e., able to learn what are the differences between classes. And, we need representations to be efficient, i.e., able to be rapidly computed and compared. Unfortunately, current techniques of handwritten shape representation for matching and recognition do not fulfill some or all of these requirements. Through this thesis we focus on the problem of learning to represent handwritten shapes aimed at retrieval and recognition tasks. Concretely, on the first part of the thesis, we focus on the general problem of representing any kind of handwritten shape. We first present a novel shape descriptor based on a deformable grid that deals with large deformations by adapting to the shape and where the cells of the grid can be used to extract different features. Then, we propose to use this descriptor to learn statistical models, based on the Active Appearance Model, that jointly learns the variability in structure and texture of a given class. Then, on the second part, we focus on a concrete application, the problem of representing handwritten words, for the tasks of word spotting, where the goal is to find all instances of a query word in a dataset of images, and recognition. First, we address the segmentation-free problem and propose an unsupervised, sliding-window-based approach that achieves state-of- the-art results in two public datasets. Second, we address the more challenging multi-writer problem, where the variability in words exponentially increases. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace, and where those that represent the same word are close together. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. This leads to a low-dimensional, unified representation of word images and strings, resulting in a method that allows one to perform either image and text searches, as well as image transcription, in a unified framework. We evaluate our methods on different public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny;Alicia Fornes | |
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| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Alm2014 | Serial | 2572 | ||
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| Author | David Fernandez | ||||
| Title | Contextual Word Spotting in Historical Handwritten Documents | Type | Book Whole | ||
| Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | There are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners.
There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely dicult due the inherent de ciencies: poor physical preservation, di erent writing styles, obsolete languages, etc. Word spotting has become a popular an ecient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach. The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an ecient word segmentation is needed. Historical handwritten documents present some common diculties that can increase the diculties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as nding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path nding algorithm is used to nd the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art. Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an ecient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them. The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Alicia Fornes | |
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-940902-7-1 | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Fer2014 | Serial | 2573 | ||
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| Author | Lluis Pere de las Heras | ||||
| Title | Relational Models for Visual Understanding of Graphical Documents. Application to Architectural Drawings. | Type | Book Whole | ||
| Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Graphical documents express complex concepts using a visual language. This language consists of a vocabulary (symbols) and a syntax (structural relations between symbols) that articulate a semantic meaning in a certain context. Therefore, the automatic interpretation by computers of these sort of documents entails three main steps: the detection of the symbols, the extraction of the structural relations between these symbols, and the modeling of the knowledge that permits the extraction of the semantics. Di erent domains in graphical documents include: architectural and engineering drawings, maps, owcharts, etc.
Graphics Recognition in particular and Document Image Analysis in general are born from the industrial need of interpreting a massive amount of digitalized documents after the emergence of the scanner. Although many years have passed, the graphical document understanding problem still seems to be far from being solved. The main reason is that the vast majority of the systems in the literature focus on very speci c problems, where the domain of the document dictates the implementation of the interpretation. As a result, it is dicult to reuse these strategies on di erent data and on di erent contexts, hindering thus the natural progress in the eld. In this thesis, we face the graphical document understanding problem by proposing several relational models at di erent levels that are designed from a generic perspective. Firstly, we introduce three di erent strategies for the detection of symbols. The fi rst method tackles the problem structurally, wherein general knowledge of the domain guides the detection. The second is a statistical method that learns the graphical appearance of the symbols and easily adapts to the big variability of the problem. The third method is a combination of the previous two methods that inherits their respective strengths, i.e. copes the big variability and does not need annotated data. Secondly, we present two relational strategies that tackle the problem of the visual context extraction. The fi rst one is a full bottom up method that heuristically searches in a graph representation the contextual relations between symbols. Contrarily, the second is syntactic method that models probabilistically the structure of the documents. It automatically learns the model, which guides the inference algorithm to encounter the best structural representation for a given input. Finally, we construct a knowledge-based model consisting of an ontological de nition of the domain and real data. This model permits to perform contextual reasoning and to detect semantic inconsistencies within the data. We evaluate the suitability of the proposed contributions in the framework of floor plan interpretation. Since there is no standard in the modeling of these documents there exists an enormous notation variability from plan to plan in terms of vocabulary and syntax. Therefore, floor plan interpretation is a relevant task in the graphical document understanding problem. It is also worth to mention that we make freely available all the resources used in this thesis {the data, the tool used to generate the data, and the evaluation scripts{ with the aim of fostering research in the graphical document understanding task. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Gemma Sanchez | |
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| ISSN | ISBN | 978-84-940902-8-8 | Medium | ||
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| Notes |
DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ Her2014 | Serial | 2574 | ||
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| Author | Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta | ||||
| Title | The ICDAR/GREC 2013 Music Scores Competition: Staff Removal | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue | Pages | 207-220 | |
| Keywords | Competition; Graphics recognition; Music scores; Writer identification; Staff removal | ||||
| Abstract | The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | B.Lamiroy; J.-M. Ogier | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-662-44853-3 | Medium | |
| Area | Expedition | Conference | |||
| Notes |
DAG; 600.077; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ FKV2014 | Serial | 2581 | ||
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| Author | Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke | ||||
| Title | Keyword spotting for self-training of BLSTM NN based handwriting recognition systems | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 3 | Pages | 1073-1082 |
| Keywords | Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning | ||||
| Abstract | The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. | ||||
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DAG; 600.077; 602.101 | Approved | no | ||
| Call Number | Admin @ si @ FFB2014 | Serial | 2297 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
| Title | Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images | Type | Conference Article | ||
| Year | 2014 | Publication | 11th IAPR International Workshop on Document Analysis and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 181 - 185 | ||
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| Abstract | Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. | ||||
| Address | Tours; France; April 2014 | ||||
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| ISSN | ISBN | 978-1-4799-3243-6 | Medium | ||
| Area | Expedition | Conference | DAS | ||
| Notes |
DAG; 601.223; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RCO2014a | Serial | 2545 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
| Title | Normalisation et validation d'images de documents capturées en mobilité | Type | Conference Article | ||
| Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
| Volume | Issue | Pages | 109-124 | ||
| Keywords | mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction | ||||
| Abstract | Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods. |
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| Address | Nancy; France; March 2014 | ||||
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| Area | Expedition | Conference | CIFED | ||
| Notes |
DAG; 601.223; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RCO2014b | Serial | 2546 | ||
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| Author | Francisco Cruz; Oriol Ramos Terrades | ||||
| Title | EM-Based Layout Analysis Method for Structured Documents | Type | Conference Article | ||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 315-320 | ||
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| Abstract | In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task. |
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| ISSN | 1051-4651 | ISBN | Medium | ||
| Area | Expedition | Conference | ICPR | ||
| Notes |
DAG; 602.006; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CrR2014 | Serial | 2530 | ||
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| Author | Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados | ||||
| Title | Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue | Pages | 135-146 | |
| Keywords | Graphics recognition; Graphics retrieval; Image classification | ||||
| Abstract | This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-662-44853-3 | Medium | |
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DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ HFF2014 | Serial | 2536 | ||
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| Author | Lluis Pere de las Heras; Ahmed Sheraz; Marcus Liwicki; Ernest Valveny; Gemma Sanchez | ||||
| Title | Statistical Segmentation and Structural Recognition for Floor Plan Interpretation | Type | Journal Article | ||
| Year | 2014 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 17 | Issue | 3 | Pages | 221-237 |
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| Abstract | A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1433-2833 | ISBN | Medium | ||
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| Notes |
DAG; ADAS; 600.076; 600.077 | Approved | no | ||
| Call Number | HSL2014 | Serial | 2370 | ||
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