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Author Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat edit   pdf
openurl 
Title Nightime Vehicle Detecion for Intelligent Headlight Control Type Conference Article
Year 2008 Publication Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, Abbreviated Journal  
Volume 5259 Issue Pages 113–124  
Keywords Intelligent Headlights; vehicle detection  
Abstract  
Address (up) Juan-les-Pins, France  
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 ACIVS  
Notes ADAS;CIC Approved no  
Call Number ADAS @ adas @ LHB2008a Serial 1098  
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell edit  url
doi  isbn
openurl 
Title Top-Down Color Attention for Object Recognition Type Conference Article
Year 2009 Publication 12th International Conference on Computer Vision Abbreviated Journal  
Volume Issue Pages 979 - 986  
Keywords  
Abstract Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.  
Address (up) Kyoto, Japan  
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 1550-5499 ISBN 978-1-4244-4420-5 Medium  
Area Expedition Conference ICCV  
Notes CIC Approved no  
Call Number CAT @ cat @ SWV2009 Serial 1196  
Permanent link to this record
 
 
Author Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris edit  url
openurl 
Title Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code Type Conference Article
Year 2015 Publication European Conference on Visual Perception ECVP2015 Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract  
Address (up) Liverpool; uk; August 2015  
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 ECVP  
Notes NEUROBIT;CIC Approved no  
Call Number Admin @ si @ POW2015 Serial 2633  
Permanent link to this record
 
 
Author O. Fors; Xavier Otazu; J. Nuñez edit  openurl
Title Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT. Type Miscellaneous
Year 2001 Publication Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 546–550. Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract  
Address (up) Lleida  
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 CAT @ cat @ FON2001 Serial 94  
Permanent link to this record
 
 
Author Xavier Otazu; J. Nuñez edit  openurl
Title Algoritmo de Clasificacion no Supervisada Basado en Wavelets. Type Miscellaneous
Year 2001 Publication Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 437–440. Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract  
Address (up) Lleida  
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 CAT @ cat @ ONu2001 Serial 147  
Permanent link to this record
 
 
Author Miquel Ferrer; Robert Benavente; Ernest Valveny; J. Garcia; Agata Lapedriza; Gemma Sanchez edit  openurl
Title Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica Type Miscellaneous
Year 2008 Publication Octava Jornada sobre Aprendizaje Cooperativo, 41–46 Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract  
Address (up) Lleida (Spain).  
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;DAG;CIC;MV Approved no  
Call Number BCNPCL @ bcnpcl @ FBV2008 Serial 955  
Permanent link to this record
 
 
Author Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell edit   pdf
url  doi
openurl 
Title Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures Type Journal Article
Year 2011 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
Volume 33 Issue 5 Pages 917-930  
Keywords  
Abstract The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost.  
Address (up) Los Alamitos; CA; USA;  
Corporate Author Thesis  
Publisher IEEE Computer Society Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 0162-8828 ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ VBW2011 Serial 1715  
Permanent link to this record
 
 
Author Arjan Gijsenij; Theo Gevers; Joost Van de Weijer edit   pdf
url  doi
openurl 
Title Improving Color Constancy by Photometric Edge Weighting Type Journal Article
Year 2012 Publication IEEE Transaction on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
Volume 34 Issue 5 Pages 918-929  
Keywords  
Abstract : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy.  
Address (up) Los Alamitos; CA; USA;  
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 0162-8828 ISBN Medium  
Area Expedition Conference  
Notes CIC;ISE Approved no  
Call Number Admin @ si @ GGW2012 Serial 1850  
Permanent link to this record
 
 
Author Robert Benavente; Maria Vanrell edit  openurl
Title Parametrizacion del Espacio de Categorias de Color Type Miscellaneous
Year 2007 Publication Proceedings del VIII Congreso Nacional del Color Abbreviated Journal  
Volume Issue Pages 77–78  
Keywords  
Abstract  
Address (up) Madrid (Spain)  
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 CNC’07  
Notes CAT;CIC Approved no  
Call Number CAT @ cat @ BeV2007 Serial 905  
Permanent link to this record
 
 
Author Eduard Vazquez; Joost Van de Weijer; Ramon Baldrich edit  openurl
Title Image Segmentation in the Presence of Shadows and Highligts Type Conference Article
Year 2008 Publication 10th European Conference on Computer Vision Abbreviated Journal  
Volume 5305 Issue Pages 1–14  
Keywords  
Abstract  
Address (up) Marseille (France)  
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 CAT;CIC Approved no  
Call Number CAT @ cat @ VVB2008b Serial 1013  
Permanent link to this record
 
 
Author Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet edit   pdf
doi  openurl
Title Towards multispectral data acquisition with hand-held devices Type Conference Article
Year 2013 Publication 20th IEEE International Conference on Image Processing Abbreviated Journal  
Volume Issue Pages 2053 - 2057  
Keywords Multispectral; mobile devices; color measurements  
Abstract We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral
reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases
the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic.		  
Address (up) Melbourne; Australia; September 2013  
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 ICIP  
Notes CIC; DAG; 600.048 Approved no  
Call Number Admin @ si @ KWK2013b Serial 2265  
Permanent link to this record
 
 
Author Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras edit   pdf
doi  openurl
Title Intrinsic Image Evaluation On Synthetic Complex Scenes Type Conference Article
Year 2013 Publication 20th IEEE International Conference on Image Processing Abbreviated Journal  
Volume Issue Pages 285 - 289  
Keywords  
Abstract Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.		  
Address (up) Melbourne; Australia; September 2013  
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 ICIP  
Notes CIC; 600.048; 600.052; 600.051 Approved no  
Call Number Admin @ si @ BSW2013 Serial 2264  
Permanent link to this record
 
 
Author Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich edit   pdf
url  isbn
openurl 
Title Perception Based Representations for Computational Colour Type Conference Article
Year 2011 Publication 3rd International Workshop on Computational Color Imaging Abbreviated Journal  
Volume 6626 Issue Pages 16-30  
Keywords colour perception, induction, naming, psychophysical data, saliency, segmentation  
Abstract The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space.  
Address (up) Milan, Italy  
Corporate Author Thesis  
Publisher Springer-Verlag Place of Publication Editor Raimondo Schettini, Shoji Tominaga, Alain Trémeau  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title LNCS  
Series Volume Series Issue Edition  
ISSN ISBN 978-3-642-20403-6 Medium  
Area Expedition Conference CCIW  
Notes CIC Approved no  
Call Number Admin @ si @ VMB2011 Serial 1733  
Permanent link to this record
 
 
Author Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta edit   pdf
doi  openurl
Title Area Under the ROC Curve Maximization for Metric Learning Type Conference Article
Year 2022 Publication CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) Abbreviated Journal  
Volume Issue Pages  
Keywords Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition  
Abstract Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.  
Address (up) New Orleans, USA; 20 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 CIC; LAMP; Approved no  
Call Number Admin @ si @ GAB2022 Serial 3700  
Permanent link to this record
 
 
Author Anna Salvatella; Maria Vanrell; Juan J. Villanueva edit  isbn
openurl 
Title Texture Description based on Subtexture Components, 3rd International Workshop on Texture Syntesis and Analysis Type Conference Article
Year 2003 Publication 3rd International Workshop on Texture Synthesis and Analysis, Abbreviated Journal  
Volume Issue Pages 77–82  
Keywords  
Abstract  
Address (up) Nice  
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 1-904410-11-1 Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number CAT @ cat @ SVV2003 Serial 422  
Permanent link to this record
 
 
Author A. Ruiz; Joost Van de Weijer; Xavier Binefa edit   pdf
url  openurl
Title Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization Type Conference Article
Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection.  
Address (up) Nottingham; UK; September 2014  
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 BMVC  
Notes LAMP; CIC; 600.074; 600.079 Approved no  
Call Number Admin @ si @ RWB2014 Serial 2508  
Permanent link to this record
 
 
Author M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer edit   pdf
doi  openurl
Title Adaptive color attributes for real-time visual tracking Type Conference Article
Year 2014 Publication 27th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
Volume Issue Pages 1090 - 1097  
Keywords  
Abstract Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second.		  
Address (up) Nottingham; UK; September 2014  
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 CVPR  
Notes CIC; LAMP; 600.074; 600.079 Approved no  
Call Number Admin @ si @ DKF2014 Serial 2509  
Permanent link to this record
 
 
Author Ivet Rafegas edit  isbn
openurl 
Title Color in Visual Recognition: from flat to deep representations and some biological parallelisms Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.

Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations.

Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias.		  
Address (up) November 2017  
Corporate Author Thesis Ph.D. thesis  
Publisher Ediciones Graficas Rey Place of Publication Editor Maria Vanrell  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN 978-84-945373-7-0 Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Raf2017 Serial 3100  
Permanent link to this record