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Author | Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans | ||||
Title | Improved RGB-D-T based Face Recognition | Type | Journal Article | ||
Year | 2016 | Publication | IET Biometrics | Abbreviated Journal | BIO |
Volume | 5 | Issue | 4 | Pages | 297 - 303 |
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Abstract | Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. | ||||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ OCN2016 | Serial | 2854 | ||
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Author | Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima | ||||
Title | CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions | Type | Conference Article | ||
Year | 2016 | Publication | 16th International Conference for Integrated Care | Abbreviated Journal | |
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Address | Barcelona; Spain; May 2016 | ||||
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Area | Expedition | Conference | ICIC | ||
Notes | HuPBA; ISE;MV | Approved | no | ||
Call Number | Admin @ si @ ABE2016 | Serial | 2855 | ||
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Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
Title | Stable Anatomical Structure Tracking for video-bronchoscopy Navigation | Type | Conference Article | ||
Year | 2016 | Publication | 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops | Abbreviated Journal | |
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Keywords | Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking | ||||
Abstract | Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at dierent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations. | ||||
Address | Athens; Greece; October 2016 | ||||
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Area | Expedition | Conference | MICCAIW | ||
Notes | IAM; 600.075 | Approved | no | ||
Call Number | Admin @ si @ LSB2016b | Serial | 2857 | ||
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Author | Arash Akbarinia; Karl R. Gegenfurtner | ||||
Title | Metameric Mismatching in Natural and Artificial Reflectances | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Vision | Abbreviated Journal | JV |
Volume | 17 | Issue | 10 | Pages | 390-390 |
Keywords | Metamer; colour perception; spectral discrimination; photoreceptors | ||||
Abstract | The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants.
We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur. |
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Address | Florida, USA; May 2017 | ||||
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Notes | NEUROBIT; no menciona | Approved | no | ||
Call Number | Admin @ si @ AkG2017 | Serial | 2899 | ||
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Author | German Ros | ||||
Title | Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa;Julio Guerrero;Antonio Lopez | |
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ISSN | ISBN | 978-84-945373-1-8 | Medium | ||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Ros2016 | Serial | 2860 | ||
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Author | Francisco Cruz | ||||
Title | Probabilistic Graphical Models for Document Analysis | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.
Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models. In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Oriol Ramos Terrades | |
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ISSN | ISBN | 978-84-945373-2-5 | Medium | ||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ Cru2016 | Serial | 2861 | ||
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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction | Type | Journal Article | ||
Year | 2016 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
Volume | 19 | Issue | 4 | Pages | 335-349 |
Keywords | scene text; segmentation; detection; hierarchical grouping; perceptual organisation | ||||
Abstract | Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective. Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art methods in unconstrained scenarios. |
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Notes | DAG; 600.056; 601.197 | Approved | no | ||
Call Number | Admin @ si @ GoK2016a | Serial | 2862 | ||
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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A fine-grained approach to scene text script identification | Type | Conference Article | ||
Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
Volume | Issue | Pages | 192-197 | ||
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Abstract | This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. | ||||
Address | Santorini; Grecia; April 2016 | ||||
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Area | Expedition | Conference | DAS | ||
Notes | DAG; 601.197; 600.084 | Approved | no | ||
Call Number | Admin @ si @ GoK2016b | Serial | 2863 | ||
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Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso | ||||
Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Thoracic Oncology | Abbreviated Journal | JTO |
Volume | 12 | Issue | 1S | Pages | S596-S597 |
Keywords | Thorax CT; diagnosis; Peripheral Pulmonary Nodule | ||||
Abstract | A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP). | ||||
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Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
Call Number | Admin @ si @ DGC2017a | Serial | 2883 | ||
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Author | Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio | ||||
Title | The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation | Type | Conference Article | ||
Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Semantic Segmentation | ||||
Abstract | State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
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Address | Honolulu; USA; July 2017 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MILAB; ADAS; 600.076; 600.085; 601.281 | Approved | no | ||
Call Number | ADAS @ adas @ JDV2016 | Serial | 2866 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title | Biologically plausible boundary detection | Type | Conference Article | ||
Year | 2016 | Publication | 27th British Machine Vision Conference | Abbreviated Journal | |
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Abstract | Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. | ||||
Address | York; UK; September 2016 | ||||
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Area | Expedition | Conference | BMVC | ||
Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
Call Number | Admin @ si @ AkP2016a | Serial | 2867 | ||
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Author | Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez | ||||
Title | Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest | Type | Miscellaneous | ||
Year | 2016 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Domain Adaptation; Pedestrian detection; Random Forest | ||||
Abstract | Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ MVJ2016 | Serial | 2868 | ||
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Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
Title | Error-tolerant coarse-to-fine matching model for hierarchical graphs | Type | Conference Article | ||
Year | 2017 | Publication | 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition | Abbreviated Journal | |
Volume | 10310 | Issue | Pages | 107-117 | |
Keywords | Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching | ||||
Abstract | Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. | ||||
Address | Anacapri; Italy; May 2017 | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | Pasquale Foggia; Cheng-Lin Liu; Mario Vento | |
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Area | Expedition | Conference | GbRPR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RLF2017a | Serial | 2951 | ||
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Author | Ariel Amato | ||||
Title | Moving cast shadow detection | Type | Journal Article | ||
Year | 2014 | Publication | Electronic letters on computer vision and image analysis | Abbreviated Journal | ELCVIA |
Volume | 13 | Issue | 2 | Pages | 70-71 |
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Abstract | Motion perception is an amazing innate ability of the creatures on the planet. This adroitness entails a functional advantage that enables species to compete better in the wild. The motion perception ability is usually employed at different levels, allowing from the simplest interaction with the ’physis’ up to the most transcendental survival tasks. Among the five classical perception system , vision is the most widely used in the motion perception field. Millions years of evolution have led to a highly specialized visual system in humans, which is characterized by a tremendous accuracy as well as an extraordinary robustness. Although humans and an immense diversity of species can distinguish moving object with a seeming simplicity, it has proven to be a difficult and non trivial problem from a computational perspective. In the field of Computer Vision, the detection of moving objects is a challenging and fundamental research area. This can be referred to as the ’origin’ of vast and numerous vision-based research sub-areas. Nevertheless, from the bottom to the top of this hierarchical analysis, the foundations still relies on when and where motion has occurred in an image. Pixels corresponding to moving objects in image sequences can be identified by measuring changes in their values. However, a pixel’s value (representing a combination of color and brightness) could also vary due to other factors such as: variation in scene illumination, camera noise and nonlinear sensor responses among others. The challenge lies in detecting if the changes in pixels’ value are caused by a genuine object movement or not. An additional challenging aspect in motion detection is represented by moving cast shadows. The paradox arises because a moving object and its cast shadow share similar motion patterns. However, a moving cast shadow is not a moving object. In fact, a shadow represents a photometric illumination effect caused by the relative position of the object with respect to the light sources. Shadow detection methods are mainly divided in two domains depending on the application field. One normally consists of static images where shadows are casted by static objects, whereas the second one is referred to image sequences where shadows are casted by moving objects. For the first case, shadows can provide additional geometric and semantic cues about shape and position of its casting object as well as the localization of the light source. Although the previous information can be extracted from static images as well as video sequences, the main focus in the second area is usually change detection, scene matching or surveillance. In this context, a shadow can severely affect with the analysis and interpretation of the scene. The work done in the thesis is focused on the second case, thus it addresses the problem of detection and removal of moving cast shadows in video sequences in order to enhance the detection of moving object. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Ama2014 | Serial | 2870 | ||
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Author | Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras | ||||
Title | Information Theoretic Rotationwise Robust Binary Descriptor Learning | Type | Conference Article | ||
Year | 2016 | Publication | Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) | Abbreviated Journal | |
Volume | Issue | Pages | 368-378 | ||
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Abstract | In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. | ||||
Address | Mérida; Mexico; November 2016 | ||||
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Area | Expedition | Conference | S+SSPR | ||
Notes | DAG; ADAS; 600.097; 600.086 | Approved | no | ||
Call Number | Admin @ si @ RLL2016 | Serial | 2871 | ||
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