Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–11] |
Records | |||||
---|---|---|---|---|---|
Author | Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov | ||||
Title | Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks | Type | Journal | ||
Year | 2018 | Publication | Journal of Medical Robotics Research | Abbreviated Journal | JMRR |
Volume | 3 | Issue | 2 | Pages | |
Keywords | convolutional neural networks; colonoscopy; computer aided diagnosis | ||||
Abstract | Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance. |
||||
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 | MV; no menciona | Approved | no | ||
Call Number | BZM2018 | Serial | 2976 | ||
Permanent link to this record | |||||
Author | Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora | ||||
Title | Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts | Type | Conference Article | ||
Year | 2018 | Publication | 16th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 528-533 | ||
Keywords | Crowdsourcing; Gamification; Handwritten documents; Performance evaluation | ||||
Abstract | Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
||||
Address | Niagara Falls, USA; August 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICFHR | ||
Notes | DAG; 600.097; 603.057; 600.121 | Approved | no | ||
Call Number | Admin @ si @ CRF2018 | Serial | 3169 | ||
Permanent link to this record | |||||
Author | Jorge Charco; Boris X. Vintimilla; Angel Sappa | ||||
Title | Deep learning based camera pose estimation in multi-view environment | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet Based System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Deep learning; Camera pose estimation; Multiview environment; Siamese architecture | ||||
Abstract | This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
||||
Address | Las Palmas de Gran Canaria; November 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SITIS | ||
Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ CVS2018 | Serial | 3194 | ||
Permanent link to this record | |||||
Author | Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa | ||||
Title | Color Naming for Multi-Color Fashion Items | Type | Conference Article | ||
Year | 2018 | Publication | 6th World Conference on Information Systems and Technologies | Abbreviated Journal | |
Volume | 747 | Issue | Pages | 64-73 | |
Keywords | Deep learning; Color; Multi-label | ||||
Abstract | There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. | ||||
Address | Naples; March 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WORLDCIST | ||
Notes | LAMP; 600.109; 601.309; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YWR2018 | Serial | 3161 | ||
Permanent link to this record | |||||
Author | Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez | ||||
Title | Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11212 | Issue | Pages | 357-372 | |
Keywords | Deep Learning; Convolutional Neural Networks; Attention | ||||
Abstract | We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100. | ||||
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 | ISE; 600.098; 602.121; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RGC2018 | Serial | 3139 | ||
Permanent link to this record | |||||
Author | Francisco Cruz; Oriol Ramos Terrades | ||||
Title | A probabilistic framework for handwritten text line segmentation | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning | ||||
Abstract | We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine tuning step. |
||||
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 | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ CrR2018 | Serial | 3253 | ||
Permanent link to this record | |||||
Author | Antonio Lopez; David Vazquez; Gabriel Villalonga | ||||
Title | Data for Training Models, Domain Adaptation | Type | Book Chapter | ||
Year | 2018 | Publication | Intelligent Vehicles. Enabling Technologies and Future Developments | Abbreviated Journal | |
Volume | Issue | Pages | 395–436 | ||
Keywords | Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data | ||||
Abstract | Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection. | ||||
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 | Approved | no | ||
Call Number | Admin @ si @ LVV2018 | Serial | 3047 | ||
Permanent link to this record | |||||
Author | Xavier Soria; Angel Sappa | ||||
Title | Improving Edge Detection in RGB Images by Adding NIR Channel | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet Based System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images | ||||
Abstract | The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
||||
Address | Las Palmas de Gran Canaria; November 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SITIS | ||
Notes | MSIAU; 600.122 | Approved | no | ||
Call Number | Admin @ si @ SoS2018 | Serial | 3192 | ||
Permanent link to this record | |||||
Author | Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli | ||||
Title | Batch-based activity recognition from egocentric photo-streams revisited | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 21 | Issue | 4 | Pages | 953–965 |
Keywords | Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks | ||||
Abstract | Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. | ||||
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 | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ CMR2018 | Serial | 3186 | ||
Permanent link to this record | |||||
Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva | ||||
Title | Egocentric video description based on temporally-linked sequences | Type | Journal Article | ||
Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
Volume | 50 | Issue | Pages | 205-216 | |
Keywords | egocentric vision; video description; deep learning; multi-modal learning | ||||
Abstract | Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
||||
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 | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ BPC2018 | Serial | 3109 | ||
Permanent link to this record | |||||
Author | Laura Lopez-Fuentes; Joost Van de Weijer; Manuel Gonzalez-Hidalgo; Harald Skinnemoen; Andrew Bagdanov | ||||
Title | Review on computer vision techniques in emergency situations | Type | Journal Article | ||
Year | 2018 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 77 | Issue | 13 | Pages | 17069–17107 |
Keywords | Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation | ||||
Abstract | In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies. | ||||
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; 600.068; 600.120 | Approved | no | ||
Call Number | Admin @ si @ LWG2018 | Serial | 3041 | ||
Permanent link to this record | |||||
Author | Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Review on Emotion Recognition Databases | Type | Book Chapter | ||
Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | emotion; computer vision; databases | ||||
Abstract | Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. | ||||
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 | 978-1-78923-316-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | HUPBA; 602.133 | Approved | no | ||
Call Number | Admin @ si @ HRL2018 | Serial | 3212 | ||
Permanent link to this record | |||||
Author | Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez | ||||
Title | Beyond Oneshot Encoding: lower dimensional target embedding | Type | Journal Article | ||
Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 75 | Issue | Pages | 21-31 | |
Keywords | Error correcting output codes; Output embeddings; Deep learning; Computer vision | ||||
Abstract | Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. | ||||
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; HuPBA; 600.098; 602.133; 602.121; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RBE2018 | Serial | 3120 | ||
Permanent link to this record | |||||
Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Deep Learning based Single Image Dehazing | Type | Conference Article | ||
Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop | Abbreviated Journal | |
Volume | Issue | Pages | 1250 - 12507 | ||
Keywords | Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis | ||||
Abstract | This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
||||
Address | Salt Lake City; USA; June 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ SSV2018d | Serial | 3197 | ||
Permanent link to this record | |||||
Author | Yaxing Wang; Chenshen Wu; Luis Herranz; Joost Van de Weijer; Abel Gonzalez-Garcia; Bogdan Raducanu | ||||
Title | Transferring GANs: generating images from limited data | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11210 | Issue | Pages | 220-236 | |
Keywords | Generative adversarial networks; Transfer learning; Domain adaptation; Image generation | ||||
Abstract | ransferring knowledge of pre-trained networks to new domains by means of fine-tuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pre-trained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pre-trained model was trained without conditioning. Our results also suggest that density is more important than diversity and a dataset with one or few densely sampled classes is a better source model than more diverse datasets such as ImageNet or Places. | ||||
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 | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WWH2018a | Serial | 3130 | ||
Permanent link to this record |