Home | [1–10] << 11 12 13 14 15 16 17 18 19 20 >> [21–30] |
![]() |
Records | |||||
---|---|---|---|---|---|
Author | Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy | ||||
Title | On Offline Evaluation of Vision-based Driving Models | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11219 | Issue | Pages | 246-262 | |
Keywords | Autonomous driving; deep learning | ||||
Abstract | Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. |
||||
Address | Munich; September 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCV | ||
Notes | ADAS; 600.124; 600.118 | Approved | no | ||
Call Number | Admin @ si @ CLK2018 | Serial | 3162 | ||
Permanent link to this record | |||||
Author | Alicia Fornes; Bart Lamiroy | ||||
Title | Graphics Recognition, Current Trends and Evolutions | Type | Book Whole | ||
Year | 2018 | Publication | Graphics Recognition, Current Trends and Evolutions | Abbreviated Journal | |
Volume | 11009 | Issue | Pages | ||
Keywords | |||||
Abstract | This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ FoL2018 | Serial | 3171 | ||
Permanent link to this record | |||||
Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
Title | Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | 11134 | Issue | Pages | 530-544 | |
Keywords | |||||
Abstract | Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis. | ||||
Address | Munich; Alemanya; September 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | DAG; 600.129; 601.338; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GGG2018b | Serial | 3176 | ||
Permanent link to this record | |||||
Author | Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas | ||||
Title | Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings | Type | Conference Article | ||
Year | 2018 | Publication | International Workshop on Reproducible Research in Pattern Recognition | Abbreviated Journal | |
Volume | 11455 | Issue | Pages | 71-82 | |
Keywords | |||||
Abstract | Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ NDC2018 | Serial | 3178 | ||
Permanent link to this record | |||||
Author | Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
Title | Automatic Verification of Properly Signed Multi-page Document Images | Type | Conference Article | ||
Year | 2015 | Publication | Proceedings of the Eleventh International Symposium on Visual Computing | Abbreviated Journal | |
Volume | 9475 | Issue | Pages | 327-336 | |
Keywords | Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow | ||||
Abstract | In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. | ||||
Address | Las Vegas, Nevada, USA; December 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | 9475 | Series Issue | Edition | ||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ISVC | ||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3189 | ||
Permanent link to this record | |||||
Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
Year | 2018 | Publication | 15th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
Volume | 10882 | Issue | Pages | 353-362 | |
Keywords | |||||
Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
||||
Address | Povoa de Varzim; Portugal; June 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIAR | ||
Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ SSV2018c | Serial | 3196 | ||
Permanent link to this record | |||||
Author | Marc Oliu; Javier Selva; Sergio Escalera | ||||
Title | Folded Recurrent Neural Networks for Future Video Prediction | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11218 | Issue | Pages | 745-761 | |
Keywords | |||||
Abstract | Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach. | ||||
Address | Munich; September 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCV | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ OSE2018 | Serial | 3204 | ||
Permanent link to this record | |||||
Author | Ciprian Corneanu; Meysam Madadi; Sergio Escalera | ||||
Title | Deep Structure Inference Network for Facial Action Unit Recognition | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11216 | Issue | Pages | 309-324 | |
Keywords | Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference | ||||
Abstract | Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively. | ||||
Address | Munich; September 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCV | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CME2018 | Serial | 3205 | ||
Permanent link to this record | |||||
Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
Title | Optical Music Recognition by Long Short-Term Memory Networks | Type | Book Chapter | ||
Year | 2018 | Publication | Graphics Recognition. Current Trends and Evolutions | Abbreviated Journal | |
Volume | 11009 | Issue | Pages | 81-95 | |
Keywords | Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory | ||||
Abstract | Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer | Place of Publication | Editor | A. Fornes, B. Lamiroy | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
Area | Expedition | Conference | GREC | ||
Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRC2018 | Serial | 3227 | ||
Permanent link to this record | |||||
Author | Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas | ||||
Title | Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets | Type | Conference Article | ||
Year | 2018 | Publication | International Workshop on Artificial Intelligence and Pattern Recognition | Abbreviated Journal | |
Volume | 11047 | Issue | Pages | 34-42 | |
Keywords | Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis | ||||
Abstract | The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. | ||||
Address | Cuba; September 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | IWAIPR | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ BGÑ2018 | Serial | 3237 | ||
Permanent link to this record | |||||
Author | Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska | ||||
Title | Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation | Type | Conference Article | ||
Year | 2018 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
Volume | 11384 | Issue | Pages | 393-405 | |
Keywords | Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution | ||||
Abstract | In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | MICCAIW | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ PSH2018 | Serial | 3251 | ||
Permanent link to this record | |||||
Author | Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu | ||||
Title | Automatic Image-Based Waste Classification | Type | Conference Article | ||
Year | 2019 | Publication | International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning | Abbreviated Journal | |
Volume | 11487 | Issue | Pages | 422–431 | |
Keywords | Computer Vision; Deep learning; Convolutional neural networks; Waste classification | ||||
Abstract | The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. | ||||
Address | Almeria; June 2019 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | IWINAC | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | RSV2019 | Serial | 3273 | ||
Permanent link to this record | |||||
Author | Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez | ||||
Title | Data Augmentation from Sketch | Type | Conference Article | ||
Year | 2019 | Publication | International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging | Abbreviated Journal | |
Volume | 11840 | Issue | Pages | 155-162 | |
Keywords | Data augmentation; cycleGANs; Multi-objective optimization | ||||
Abstract | State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.
We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives. |
||||
Address | Shenzhen; China; October 2019 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CLIP | ||
Notes | IAM; 600.145; 601.337; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GES2019 | Serial | 3359 | ||
Permanent link to this record | |||||
Author | Eduardo Aguilar; Petia Radeva | ||||
Title | Class-Conditional Data Augmentation Applied to Image Classification | Type | Conference Article | ||
Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 11679 | Issue | Pages | 182-192 | |
Keywords | CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition | ||||
Abstract | Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty. | ||||
Address | Salermo; Italy; September 2019 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CAIP | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ AgR2019 | Serial | 3366 | ||
Permanent link to this record | |||||
Author | Estefania Talavera; Nicolai Petkov; Petia Radeva | ||||
Title | Unsupervised Routine Discovery in Egocentric Photo-Streams | Type | Conference Article | ||
Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 11678 | Issue | Pages | 576-588 | |
Keywords | Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis | ||||
Abstract | The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people. | ||||
Address | Salermo; Italy; September 2019 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CAIP | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ TPR2019a | Serial | 3367 | ||
Permanent link to this record |