Home | << 1 2 3 4 5 6 7 8 9 10 >> |
Records | |||||
---|---|---|---|---|---|
Author | Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
Title | Generic Feature Learning for Wireless Capsule Endoscopy Analysis | Type | Journal Article | ||
Year | 2016 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 79 | Issue | Pages | 163-172 | |
Keywords | Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis | ||||
Abstract | The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase). | ||||
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 | OR; MILAB;MV; | Approved | no | ||
Call Number | Admin @ si @ SDP2016 | Serial | 2836 | ||
Permanent link to this record | |||||
Author | Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Robust non-blind color video watermarking using QR decomposition and entropy analysis | Type | Journal Article | ||
Year | 2016 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
Volume | 38 | Issue | Pages | 838-847 | |
Keywords | Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition | ||||
Abstract | Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. | ||||
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;MILAB; | Approved | no | ||
Call Number | Admin @ si @RSA2016 | Serial | 2766 | ||
Permanent link to this record | |||||
Author | Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta | ||||
Title | Video Description Using Bidirectional Recurrent Neural Networks | Type | Conference Article | ||
Year | 2016 | Publication | 25th International Conference on Artificial Neural Networks | Abbreviated Journal | |
Volume | 2 | Issue | Pages | 3-11 | |
Keywords | Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks | ||||
Abstract | Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. | ||||
Address | Barcelona; September 2016 | ||||
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 | ICANN | ||
Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @ PBR2016 | Serial | 2833 | ||
Permanent link to this record | |||||
Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
Title | Sparse representation over learned dictionary for symbol recognition | Type | Journal Article | ||
Year | 2016 | Publication | Signal Processing | Abbreviated Journal | SP |
Volume | 125 | Issue | Pages | 36-47 | |
Keywords | Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points | ||||
Abstract | In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. | ||||
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; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ DTR2016 | Serial | 2946 | ||
Permanent link to this record | |||||
Author | Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez | ||||
Title | Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching | Type | Conference Article | ||
Year | 2016 | Publication | GPU Technology Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Stereo; Autonomous Driving; GPU; 3d reconstruction | ||||
Abstract | Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms | ||||
Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
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 | GTC | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ HME2016 | Serial | 2738 | ||
Permanent link to this record | |||||
Author | Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio | ||||
Title | EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game | Type | Conference Article | ||
Year | 2016 | Publication | 5th International Conference Games and Learning Alliance | Abbreviated Journal | |
Volume | 10056 | Issue | Pages | 50-59 | |
Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
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 | GALA | ||
Notes | ADAS;IAM; | Approved | no | ||
Call Number | HAC2016 | Serial | 2864 | ||
Permanent link to this record | |||||
Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier | ||||
Title | LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode | Type | Conference Article | ||
Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | 9915 | Issue | Pages | 894-900 | |
Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
Address | Amsterdam; The Netherlands; October 2016 | ||||
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 | ADAS;IAM; 600.085; 600.076 | Approved | no | ||
Call Number | MHE2016 | Serial | 2865 | ||
Permanent link to this record | |||||
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. |
||||
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; 600.056; 601.197 | Approved | no | ||
Call Number | Admin @ si @ GoK2016a | Serial | 2862 | ||
Permanent link to this record | |||||
Author | Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas | ||||
Title | Dynamic Lexicon Generation for Natural Scene Images | Type | Conference Article | ||
Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 395-410 | ||
Keywords | scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN | ||||
Abstract | Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge benet from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons for scene images using only visual information. For this, we exploit the correlation between visual and textual information in a dataset consisting of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline. |
||||
Address | Amsterdam; The Netherlands; October 2016 | ||||
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 | ECCVW | ||
Notes | DAG; 600.084 | Approved | no | ||
Call Number | Admin @ si @ PGR2016 | Serial | 2825 | ||
Permanent link to this record | |||||
Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
Title | Incremental texture mapping for autonomous driving | Type | Journal Article | ||
Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
Volume | 84 | Issue | Pages | 113-128 | |
Keywords | Scene reconstruction; Autonomous driving; Texture mapping | ||||
Abstract | Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. | ||||
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; 600.086 | Approved | no | ||
Call Number | Admin @ si @ OSS2016b | Serial | 2912 | ||
Permanent link to this record | |||||
Author | Maria Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester | ||||
Title | A Computational Model for Amodal Completion | Type | Journal Article | ||
Year | 2016 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
Volume | 56 | Issue | 3 | Pages | 511–534 |
Keywords | Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica | ||||
Abstract | This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
||||
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; 601.235 | Approved | no | ||
Call Number | Admin @ si @ OHD2016b | Serial | 2745 | ||
Permanent link to this record | |||||
Author | Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez | ||||
Title | GPU-based pedestrian detection for autonomous driving | Type | Conference Article | ||
Year | 2016 | Publication | GPU Technology Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Pedestrian Detection; GPU | ||||
Abstract | Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. | ||||
Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
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 | GTC | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ CSM2016 | Serial | 2737 | ||
Permanent link to this record | |||||
Author | Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez | ||||
Title | Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison | Type | Journal Article | ||
Year | 2016 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 16 | Issue | 6 | Pages | 820 |
Keywords | Pedestrian Detection; FIR | ||||
Abstract | Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset. | ||||
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 | 1424-8220 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.085; 600.076; 600.082; 601.281 | Approved | no | ||
Call Number | ADAS @ adas @ GFS2016 | Serial | 2754 | ||
Permanent link to this record | |||||
Author | Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez | ||||
Title | GPU-based pedestrian detection for autonomous driving | Type | Conference Article | ||
Year | 2016 | Publication | 16th International Conference on Computational Science | Abbreviated Journal | |
Volume | 80 | Issue | Pages | 2377-2381 | |
Keywords | Pedestrian detection; Autonomous Driving; CUDA | ||||
Abstract | We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. | ||||
Address | San Diego; CA; USA; June 2016 | ||||
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 | ICCS | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ CSE2016 | Serial | 2741 | ||
Permanent link to this record | |||||
Author | Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan | ||||
Title | Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach | Type | Journal Article | ||
Year | 2016 | Publication | Journal of Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 27 | Issue | Pages | 511-527 | |
Keywords | particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging | ||||
Abstract | In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor quality data, particles and trajectories can be characterized by an a-contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that do not require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well established baseline show that the proposed approach outperforms the state of the art. |
||||
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; | Approved | no | ||
Call Number | Admin @ si @ DJM2016 | Serial | 2735 | ||
Permanent link to this record |