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| Author | Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez | ||||
| Title | Discriminative Compact Pyramids for Object and Scene Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
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45 | Issue | 4 | Pages | 1627-1636 |
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| Abstract | Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; CAT;CIC | Approved | no | ||
| Call Number | Admin @ si @ EKW2012 | Serial | 1807 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 6 | Pages | 2432-2444 |
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| Abstract | IF= 2.61
IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR; MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2012a | Serial | 1884 | ||
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| Author | Jon Almazan; Alicia Fornes; Ernest Valveny | ||||
| Title | A non-rigid appearance model for shape description and recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 9 | Pages | 3105--3113 |
| Keywords | Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition | ||||
| Abstract | In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ AFV2012 | Serial | 1982 | ||
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| Author | Jaume Gibert; Ernest Valveny; Horst Bunke | ||||
| Title | Graph Embedding in Vector Spaces by Node Attribute Statistics | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 9 | Pages | 3072-3083 |
| Keywords | Structural pattern recognition; Graph embedding; Data clustering; Graph classification | ||||
| Abstract | Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GVB2012a | Serial | 1992 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Towards Automatic Polyp Detection with a Polyp Appearance Model | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 9 | Pages | 3166-3182 |
| Keywords | Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot | ||||
| Abstract | This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | IbPRIA | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSV2012; IAM @ iam | Serial | 1997 | ||
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| Author | Mario Hernandez; Joao Sanchez; Jordi Vitria | ||||
| Title | Selected papers from Iberian Conference on Pattern Recognition and Image Analysis | Type | Book Whole | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | |
| Volume |
45 | Issue | 9 | Pages | 3047-3582 |
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ HSV2012 | Serial | 2069 | ||
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| Author | Susana Alvarez; Maria Vanrell | ||||
| Title | Texton theory revisited: a bag-of-words approach to combine textons | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 12 | Pages | 4312-4325 |
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| Abstract | The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ AlV2012a | Serial | 2130 | ||
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| Author | Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre | ||||
| Title | Multi-oriented touching text character segmentation in graphical documents using dynamic programming | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 5 | Pages | 1972-1983 |
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| Abstract | 2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes. |
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ RPL2012a | Serial | 2133 | ||
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| Author | Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez | ||||
| Title | A reduced feature set for driver head pose estimation | Type | Journal Article | ||
| Year | 2016 | Publication | Applied Soft Computing | Abbreviated Journal | ASOC |
| Volume |
45 | Issue | Pages | 98-107 | |
| Keywords | Head pose estimation; driving performance evaluation; subspace based methods; linear regression | ||||
| Abstract | Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. | ||||
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| Notes | ADAS; 600.085; 600.076; | Approved | no | ||
| Call Number | Admin @ si @ DHL2016 | Serial | 2760 | ||
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| Author | Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz | ||||
| Title | Gate-Shift-Fuse for Video Action Recognition | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume |
45 | Issue | 9 | Pages | 10913-10928 |
| Keywords | Action Recognition; Video Classification; Spatial Gating; Channel Fusion | ||||
| Abstract | Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks. | ||||
| Address | 1 Sept. 2023 | ||||
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SEL2023 | Serial | 3814 | ||
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| Author | Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes | ||||
| Title | Video transformers: A survey | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume |
45 | Issue | 11 | Pages | 12922-12943 |
| Keywords | Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations | ||||
| Abstract | Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. | ||||
| Address | 1 Nov. 2023 | ||||
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SJE2023 | Serial | 3823 | ||
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| Author | Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer | ||||
| Title | Generative Multi-Label Zero-Shot Learning | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume |
45 | Issue | 12 | Pages | 14611-14624 |
| Keywords | Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis | ||||
| Abstract | Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. | ||||
| Address | December 2023 | ||||
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| Notes | LAMP; PID2021-128178OB-I00 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3853 | ||
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| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui; Jian Yang | ||||
| Title | Trust Your Good Friends: Source-Free Domain Adaptation by Reciprocal Neighborhood Clustering | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume |
45 | Issue | 12 | Pages | 15883-15895 |
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| Abstract | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g., due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. | ||||
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| Notes | LAMP; MACO | Approved | no | ||
| Call Number | Admin @ si @ YWW2023 | Serial | 3889 | ||
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| Author | Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez | ||||
| Title | Rank Estimation in 3D Multibody Motion Segmentation | Type | Journal Article | ||
| Year | 2008 | Publication | Electronic Letters | Abbreviated Journal | |
| Volume |
44 | Issue | 4 | Pages | 279-280 |
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| Abstract | A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ JSL2008a | Serial | 939 | ||
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| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-preserving smoothing of biomedical images | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
44 | Issue | 9 | Pages | 1842-1851 |
| Keywords | Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ GHB2011 | Serial | 1526 | ||
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