Home | [151–160] << 161 162 163 164 165 166 167 168 169 170 >> [171–180] |
![]() |
Records | |||||
---|---|---|---|---|---|
Author | Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
Title | Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | Type | Conference Article | ||
Year | 2019 | Publication | Visual Recognition for Medical Images workshop | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
406-415 | ||
Keywords | |||||
Abstract | In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. | ||||
Address | Seul; Korea; October 2019 | ||||
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 | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ AAF2019 | Serial | 3324 | ||
Permanent link to this record | |||||
Author | D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom | ||||
Title | Automated classification of renal interstitium and tubules by local texture analysis and a neural network | Type | Journal Article | ||
Year | 1996 | Publication | Analytical and Quantitative Cytology and Histology | Abbreviated Journal | |
Volume | 18 | Issue | 5 | Pages ![]() |
410-9, PMID: 8908314 |
Keywords | |||||
Abstract | |||||
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;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ SMG1996 | Serial | 76 | ||
Permanent link to this record | |||||
Author | Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich | ||||
Title | Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023 | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
412-421 | ||
Keywords | |||||
Abstract | This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement. | ||||
Address | Vancouver; Canada; June 2023 | ||||
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 | Approved | no | ||
Call Number | Admin @ si @ LNS2023b | Serial | 3915 | ||
Permanent link to this record | |||||
Author | Francesco Brughi; Debora Gil; Llorenç Badiella; Eva Jove Casabella; Oriol Ramos Terrades | ||||
Title | Exploring the impact of inter-query variability on the performance of retrieval systems | Type | Conference Article | ||
Year | 2014 | Publication | 11th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
Volume | 8814 | Issue | Pages ![]() |
413–420 | |
Keywords | |||||
Abstract | This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes. | ||||
Address | Algarve; Portugal; October 2014 | ||||
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 | 0302-9743 | ISBN | 978-3-319-11757-7 | Medium | |
Area | Expedition | Conference | ICIAR | ||
Notes | IAM; DAG; 600.060; 600.061; 600.077; 600.075 | Approved | no | ||
Call Number | Admin @ si @ BGB2014 | Serial | 2559 | ||
Permanent link to this record | |||||
Author | Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz | ||||
Title | Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis | Type | Journal Article | ||
Year | 2015 | Publication | American Journal of Physiology-Gastrointestinal and Liver Physiology | Abbreviated Journal | AJPGI |
Volume | 309 | Issue | 6 | Pages ![]() |
G413--G419 |
Keywords | capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning | ||||
Abstract | We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | American Physiological Society | 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; OR;MV | Approved | no | ||
Call Number | Admin @ si @ MDS2015 | Serial | 2666 | ||
Permanent link to this record | |||||
Author | Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari | ||||
Title | Action recognition using single-pixel time-of-flight detection | Type | Journal Article | ||
Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
Volume | 21 | Issue | 4 | Pages ![]() |
414 |
Keywords | single pixel single photon image acquisition; time-of-flight; action recognition | ||||
Abstract | Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network. |
||||
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 @ OHC2019 | Serial | 3319 | ||
Permanent link to this record | |||||
Author | Rafael E. Rivadeneira; Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Thermal Image SuperResolution Through Deep Convolutional Neural Network | Type | Conference Article | ||
Year | 2019 | Publication | 16th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
417-426 | ||
Keywords | |||||
Abstract | Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a super-resolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process, a new thermal images dataset is used. Different experiments have been carried out, firstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.
cidis.espol.edu.ec/es/dataset. |
||||
Address | Waterloo; Canada; August 2019 | ||||
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 | ICIAR | ||
Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
Call Number | Admin @ si @ RSS2019 | Serial | 3269 | ||
Permanent link to this record | |||||
Author | David Geronimo; Antonio Lopez; Daniel Ponsa; Angel Sappa | ||||
Title | Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection | Type | Conference Article | ||
Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 | Abbreviated Journal | |
Volume | 1 | Issue | Pages ![]() |
418–425 | |
Keywords | Pedestrian detection | ||||
Abstract | |||||
Address | Girona (Spain) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | J. Marti et al. | ||
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 | Approved | no | ||
Call Number | ADAS @ adas @ GLP2007a | Serial | 805 | ||
Permanent link to this record | |||||
Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Jin Kim; Dogun Kim; Zhihao Li; Yingchun Jian; Bo Yan; Leilei Cao; Fengliang Qi; Hongbin Wang Rongyuan Wu; Lingchen Sun; Yongqiang Zhao; Lin Li; Kai Wang; Yicheng Wang; Xuanming Zhang; Huiyuan Wei; Chonghua Lv; Qigong Sun; Xiaolin Tian; Zhuang Jia; Jiakui Hu; Chenyang Wang; Zhiwei Zhong; Xianming Liu; Junjun Jiang | ||||
Title | Thermal Image Super-Resolution Challenge Results – PBVS 2022 | Type | Conference Article | ||
Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
418-426 | ||
Keywords | |||||
Abstract | This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. | ||||
Address | New Orleans; USA; June 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 | CVPRW | ||
Notes | MSIAU; no menciona | Approved | no | ||
Call Number | Admin @ si @ RSV2022c | Serial | 3775 | ||
Permanent link to this record | |||||
Author | Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados | ||||
Title | Textual Descriptions for Browsing People by Visual Apperance. | Type | Book Chapter | ||
Year | 2002 | Publication | Lecture Notes in Artificial Intelligence | Abbreviated Journal | |
Volume | 2504 | Issue | Pages ![]() |
419-429 | |
Keywords | |||||
Abstract | This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer Verlag | 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;CIC | Approved | no | ||
Call Number | CAT @ cat @ TBB2002b | Serial | 319 | ||
Permanent link to this record | |||||
Author | David Guillamet; Jordi Vitria | ||||
Title | A Comparison of Local versus Global Color Histograms for Object Recognition. | Type | Conference Article | ||
Year | 2000 | Publication | 15 th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | 2 | Issue | Pages ![]() |
422-425 | |
Keywords | |||||
Abstract | |||||
Address | Barcelona. | ||||
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 | ICPR | ||
Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ GuV2000 b | Serial | 229 | ||
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 | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
Title | MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams | Type | Conference Article | ||
Year | 2018 | Publication | European Conference on Computer Vision workshops | Abbreviated Journal | |
Volume | Issue | Pages ![]() |
423-433 | ||
Keywords | |||||
Abstract | First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LCNS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ SRR2018b | Serial | 3185 | ||
Permanent link to this record | |||||
Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
Title | Single view facial hair 3D reconstruction | Type | Conference Article | ||
Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 11867 | Issue | Pages ![]() |
423-436 | |
Keywords | 3D Vision; Shape Reconstruction; Facial Hair Modeling | ||||
Abstract | n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. | ||||
Address | Madrid; July 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 | IbPRIA | ||
Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3707 | ||
Permanent link to this record | |||||
Author | V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich | ||||
Title | Adaptive Correlation Filters for Pattern Recognition | Type | Journal | ||
Year | 2006 | Publication | Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 16 | Issue | 3 | Pages ![]() |
425-431 |
Keywords | Pattern recognition, Correlation filters, A adaptive filters | ||||
Abstract | Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition 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 | ISE | Approved | no | ||
Call Number | ISE @ ise @ KMA2006a | Serial | 673 | ||
Permanent link to this record |