| Records |
| Author |
Yaxing Wang; Luis Herranz; Joost Van de Weijer |
| Title |
Mix and match networks: multi-domain alignment for unpaired image-to-image translation |
Type |
Journal Article |
| Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
Volume  |
128 |
Issue |
|
Pages |
2849–2872 |
| Keywords |
|
| Abstract |
This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
LAMP; 600.109; 600.106; 600.141; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ WHW2020 |
Serial |
3424 |
| Permanent link to this record |
| |
|
| |
| Author |
Noha Elfiky |
| Title |
Enhancing Local Binary Patterns with Spatial Pyramid Kernel: Application to Scene Classification |
Type |
Report |
| Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
129 |
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
|
| Address |
|
| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
|
Place of Publication |
Bellaterra, Barcelona |
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ Elf2009 |
Serial |
2388 |
| Permanent link to this record |
| |
|
| |
| Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
| Title |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
129 |
Issue |
|
Pages |
108766 |
| Keywords |
|
| Abstract |
The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
| Address |
Sept. 2022 |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG; 600.121; 600.162 |
Approved |
no |
| Call Number |
Admin @ si @ KRR2022 |
Serial |
3556 |
| Permanent link to this record |
| |
|
| |
| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| Title |
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
129 |
Issue |
|
Pages |
2499–2512 |
| Keywords |
|
| Abstract |
In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBE2021 |
Serial |
3654 |
| Permanent link to this record |
| |
|
| |
| Author |
Hany Salah Eldeen |
| Title |
Colour Naming in Context through a Perceptual Model |
Type |
Report |
| Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
130 |
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
|
| Address |
|
| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
|
Place of Publication |
Bellaterra, Barcelona |
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
Admin @ si @ Eld2009 |
Serial |
2389 |
| Permanent link to this record |
| |
|
| |
| Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
| Title |
Learning to measure for preshipment garment sizing |
Type |
Journal Article |
| Year |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
| Volume |
130 |
Issue |
|
Pages |
327-339 |
| Keywords |
Apparel; Computer vision; Structured prediction; Regression |
| Abstract |
Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ADAS; MSIAU; 600.122; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SLR2018 |
Serial |
3128 |
| Permanent link to this record |
| |
|
| |
| Author |
Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; Alvaro Pascual; B. Cardenas; G. Fonseka; E. Anton; Richard Frodsham; Francesca Vidal; Zaida Sarrate |
| Title |
Chromosomal positioning in spermatogenic cells is influenced by chromosomal factors associated with gene activity, bouquet formation, and meiotic sex-chromosome inactivation |
Type |
Journal Article |
| Year |
2021 |
Publication |
Chromosoma |
Abbreviated Journal |
|
| Volume |
130 |
Issue |
|
Pages |
163-175 |
| Keywords |
|
| Abstract |
Chromosome territoriality is not random along the cell cycle and it is mainly governed by intrinsic chromosome factors and gene expression patterns. Conversely, very few studies have explored the factors that determine chromosome territoriality and its influencing factors during meiosis. In this study, we analysed chromosome positioning in murine spermatogenic cells using three-dimensionally fluorescence in situ hybridization-based methodology, which allows the analysis of the entire karyotype. The main objective of the study was to decipher chromosome positioning in a radial axis (all analysed germ-cell nuclei) and longitudinal axis (only spermatozoa) and to identify the chromosomal factors that regulate such an arrangement. Results demonstrated that the radial positioning of chromosomes during spermatogenesis was cell-type specific and influenced by chromosomal factors associated to gene activity. Chromosomes with specific features that enhance transcription (high GC content, high gene density and high numbers of predicted expressed genes) were preferentially observed in the inner part of the nucleus in virtually all cell types. Moreover, the position of the sex chromosomes was influenced by their transcriptional status, from the periphery of the nucleus when its activity was repressed (pachytene) to a more internal position when it is partially activated (spermatid). At pachytene, chromosome positioning was also influenced by chromosome size due to the bouquet formation. Longitudinal chromosome positioning in the sperm nucleus was not random either, suggesting the importance of ordered longitudinal positioning for the release and activation of the paternal genome after fertilisation. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
IAM; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ SBG2021 |
Serial |
3592 |
| Permanent link to this record |
| |
|
| |
| Author |
Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio |
| Title |
A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding |
Type |
Journal |
| Year |
2021 |
Publication |
SMPTE Motion Imaging Journal |
Abbreviated Journal |
SMPTE |
| Volume |
130 |
Issue |
4 |
Pages |
53 - 65 |
| Keywords |
|
| Abstract |
In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
CIC |
Approved |
no |
| Call Number |
SCV2021 |
Serial |
3671 |
| Permanent link to this record |
| |
|
| |
| Author |
Naila Murray |
| Title |
Perceptual Feature Detection |
Type |
Report |
| Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
131 |
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
|
| Address |
|
| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
|
Place of Publication |
Bellaterra, Barcelona |
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ Mur2009 |
Serial |
2390 |
| Permanent link to this record |
| |
|
| |
| Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
| Title |
Decremental generalized discriminative common vectors applied to images classification |
Type |
Journal Article |
| Year |
2017 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
131 |
Issue |
|
Pages |
46-57 |
| Keywords |
Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
| Abstract |
In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ADAS; 600.118; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DMH2017a |
Serial |
3003 |
| Permanent link to this record |
| |
|
| |
| Author |
Josep M. Gonfaus |
| Title |
Semantic Segmentation of Images Using Random Ferns |
Type |
Report |
| Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
132 |
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
|
| Address |
|
| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
|
Place of Publication |
Bellaterra, Barcelona |
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ Gon2009 |
Serial |
2391 |
| Permanent link to this record |
| |
|
| |
| Author |
Yaxing Wang; Abel Gonzalez-Garcia; Chenshen Wu; Luis Herranz; Fahad Shahbaz Khan; Shangling Jui; Jian Yang; Joost Van de Weijer |
| Title |
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains |
Type |
Journal Article |
| Year |
2024 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
132 |
Issue |
|
Pages |
490–514 |
| Keywords |
|
| Abstract |
Given the often enormous effort required to train GANs, both computationally as well as in dataset collection, the re-use of pretrained GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset. We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. MineGAN. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
LAMP; MACO |
Approved |
no |
| Call Number |
Admin @ si @ WGW2024 |
Serial |
3888 |
| Permanent link to this record |
| |
|
| |
| Author |
David Augusto Rojas |
| Title |
Colouring Local Feature Detection for Matching |
Type |
Report |
| Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
133 |
Issue |
|
Pages |
|
| Keywords |
|
| Abstract |
|
| Address |
|
| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
|
Place of Publication |
Bellaterra, Barcelona |
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ Roj2009 |
Serial |
2392 |
| Permanent link to this record |
| |
|
| |
| Author |
Meysam Madadi; Hugo Bertiche; Wafa Bouzouita; Isabelle Guyon; Sergio Escalera |
| Title |
Learning Cloth Dynamics: 3D+Texture Garment Reconstruction Benchmark |
Type |
Conference Article |
| Year |
2021 |
Publication |
Proceedings of Machine Learning Research |
Abbreviated Journal |
|
| Volume |
133 |
Issue |
|
Pages |
57-76 |
| Keywords |
|
| Abstract |
Human avatars are important targets in many computer applications. Accurately tracking, capturing, reconstructing and animating the human body, face and garments in 3D are critical for human-computer interaction, gaming, special effects and virtual reality. In the past, this has required extensive manual animation. Regardless of the advances in human body and face reconstruction, still modeling, learning and analyzing human dynamics need further attention. In this paper we plan to push the research in this direction, e.g. understanding human dynamics in 2D and 3D, with special attention to garments. We provide a large-scale dataset (more than 2M frames) of animated garments with variable topology and type, calledCLOTH3D++. The dataset contains RGBA video sequences paired with its corresponding 3D data. We pay special care to garment dynamics and realistic rendering of RGB data, including lighting, fabric type and texture. With this dataset, we hold a competition at NeurIPS2020. We design three tracks so participants can compete to develop the best method to perform 3D garment reconstruction in a sequence from (1) 3D-to-3D garments, (2) RGB-to-3D garments, and (3) RGB-to-3D garments plus texture. We also provide a baseline method, based on graph convolutional networks, for each track. Baseline results show that there is a lot of room for improvements. However, due to the challenging nature of the problem, no participant could outperform the baselines. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBB2021 |
Serial |
3655 |
| Permanent link to this record |
| |
|
| |
| Author |
Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez |
| Title |
Single image super-resolution based on directional variance attention network |
Type |
Journal Article |
| Year |
2023 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
133 |
Issue |
|
Pages |
108997 |
| Keywords |
|
| Abstract |
Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ BPF2023 |
Serial |
3861 |
| Permanent link to this record |
