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| Author | Lluis Gomez | ||||
| Title | Perceptual Organization for Text Extraction in Natural Scenes | Type | Report | ||
| Year | 2012 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 173 | Issue | Pages | ||
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| Address | Bellaterra | ||||
| Corporate Author | Thesis | Master's thesis | |||
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| Notes | DAG | Approved | no | ||
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Admin @ si @ Gom2012 | Serial | 2309 | ||
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| Author | Lluis Gomez | ||||
| Title | Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding | Type | Book Whole | ||
| Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision. Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which text emerges as a perceptually significant group of atomic objects. In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, 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 hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods. Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve the overall tracking performance of the system. Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Dimosthenis Karatzas | ||
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| Notes | DAG | Approved | no | ||
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Admin @ si @ Gom2016 | Serial | 2891 | ||
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| Author | Jose Luis Gomez | ||||
| Title | Synth-to-real semi-supervised learning for visual tasks | Type | Book Whole | ||
| Year | 2023 | Publication | Going beyond Classification Problems for the Continual Learning of Deep Neural Networks | Abbreviated Journal | |
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| Abstract | The curse of data labeling is a costly bottleneck in supervised deep learning, where large amounts of labeled data are needed to train intelligent systems. In onboard perception for autonomous driving, this cost corresponds to the labeling of raw data from sensors such as cameras, LiDARs, RADARs, etc. Therefore, synthetic data with automatically generated ground truth (labels) has aroused as a reliable alternative for training onboard perception models.
However, synthetic data commonly suffers from synth-to-real domain shift, i.e., models trained on the synthetic domain do not show their achievable accuracy when performing in the real world. This shift needs to be addressed by techniques falling in the realm of domain adaptation (DA). The semi-supervised learning (SSL) paradigm can be followed to address DA. In this case, a model is trained using source data with labels (here synthetic) and leverages minimal knowledge from target data (here the real world) to generate pseudo-labels. These pseudo-labels help the training process to reduce the gap between the source and the target domains. In general, we can assume accessing both, pseudo-labels and a few amounts of human-provided labels for the target-domain data. However, the most interesting and challenging setting consists in assuming that we do not have human-provided labels at all. This setting is known as unsupervised domain adaptation (UDA). This PhD focuses on applying SSL to the UDA setting, for onboard visual tasks related to autonomous driving. We start by addressing the synth-to-real UDA problem on onboard vision-based object detection (pedestrians and cars), a critical task for autonomous driving and driving assistance. In particular, we propose to apply an SSL technique known as co-training, which we adapt to work with deep models that process a multi-modal input. The multi-modality consists of the visual appearance of the images (RGB) and their monocular depth estimation. The synthetic data we use as the source domain contains both, object bounding boxes and depth information. This prior knowledge is the starting point for the co-training technique, which iteratively labels unlabeled real-world data and uses such pseudolabels (here bounding boxes with an assigned object class) to progressively improve the labeling results. Along this process, two models collaborate to automatically label the images, in a way that one model compensates for the errors of the other, so avoiding error drift. While this automatic labeling process is done offline, the resulting pseudolabels can be used to train object detection models that must perform in real-time onboard a vehicle. We show that multi-modal co-training improves the labeling results compared to single-modal co-training, remaining competitive compared to human labeling. Given the success of co-training in the context of object detection, we have also adapted this technique to a more crucial and challenging visual task, namely, onboard semantic segmentation. In fact, providing labels for a single image can take from 30 to 90 minutes for a human labeler, depending on the content of the image. Thus, developing automatic labeling techniques for this visual task is of great interest to the automotive industry. In particular, the new co-training framework addresses synth-to-real UDA by an initial stage of self-training. Intermediate models arising from this stage are used to start the co-training procedure, for which we have elaborated an accurate collaboration policy between the two models performing the automatic labeling. Moreover, our co-training seamlessly leverages datasets from different synthetic domains. In addition, the co-training procedure is agnostic to the loss function used to train the semantic segmentation models which perform the automatic labeling. We achieve state-of-the-art results on publicly available benchmark datasets, again, remaining competitive compared to human labeling. Finally, on the ground of our previous experience, we have designed and implemented a new SSL technique for UDA in the context of visual semantic segmentation. In this case, we mimic the labeling methodology followed by human labelers. In particular, rather than labeling full images at a time, categories of semantic classes are defined and only those are labeled in a labeling pass. In fact, different human labelers can become specialists in labeling different categories. Afterward, these per-category-labeled layers are combined to provide fully labeled images. Our technique is inspired by this methodology since we perform synth-to-real UDA per category, using the self-training stage previously developed as part of our co-training framework. The pseudo-labels obtained for each category are finally fused to obtain fully automatically labeled images. In this context, we have also contributed to the development of a new photo-realistic synthetic dataset based on path-tracing rendering. Our new SSL technique seamlessly leverages publicly available synthetic datasets as well as this new one to obtain state-of-the-art results on synth-to-real UDA for semantic segmentation. We show that the new dataset allows us to reach better labeling accuracy than previously existing datasets, at the same time that it complements well them when combined. Moreover, we also show that the new human-inspired SSL technique outperforms co-training. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Antonio Lopez | |
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| Notes | ADAS | Approved | no | ||
| Call Number |
Admin @ si @ Gom2023 | Serial | 3961 | ||
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| Author | Josep M. Gonfaus | ||||
| Title | Semantic Segmentation of Images Using Random Ferns | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 132 | Issue | Pages | ||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
| Language | Summary Language | Original Title | |||
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| Notes | ISE | Approved | no | ||
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Admin @ si @ Gon2009 | Serial | 2391 | ||
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| Author | Wenjuan Gong | ||||
| Title | Action priors for human pose tracking by particle filter | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Gon2009 | Serial | 2401 | ||
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| Author | Alejandro Gonzalez Alzate | ||||
| Title | Evaluation of spatiotemporal descriptors for pedestrian detection in video sequences | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 166 | Issue | Pages | ||
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| Abstract | |||||
| Address | Bellaterra (Spain) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Editor | |||
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| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number |
Admin @ si @ Gon2011 | Serial | 1932 | ||
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| Author | Josep M. Gonfaus | ||||
| Title | Towards Deep Image Understanding: From pixels to semantics | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene.
Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity. Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Theo Gevers | |
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Gon2012 | Serial | 2208 | ||
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| Author | Wenjuan Gong | ||||
| Title | 3D Motion Data aided Human Action Recognition and Pose Estimation | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this work, we explore human action recognition and pose estimation prob-
lems. Different from traditional works of learning from 2D images or video sequences and their annotated output, we seek to solve the problems with ad- ditional 3D motion capture information, which helps to fill the gap between 2D image features and human interpretations. We first compare two different schools of approaches commonly used for 3D pose estimation from 2D pose configuration: modeling and learning methods. By looking into experiments results and considering our problems, we fixed a learning method as the following approaches to do pose estimation. We then establish a framework by adding a module of detecting 2D pose configuration from images with varied background, which widely extend the application of the approach. We also seek to directly estimate 3D poses from image features, instead of estimating 2D poses as a intermediate module. We explore a robust input feature, which combined with the proposed distance measure, provides a solution for noisy or corrupted inputs. We further utilize the above method to estimate weak poses,which is a concise representation of the original poses by using dimension deduction technologies, from image features. Weak pose space is where we calculate vocabulary and label action types using a bog of words pipeline. Temporal information of an action is taken into consideration by considering several consecutive frames as a single unit for computing vocabulary and histogram assignments. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
| Language | Summary Language | Original Title | |||
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| Notes | ISE | Approved | no | ||
| Call Number |
Admin @ si @ Gon2013 | Serial | 2279 | ||
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| Author | Alejandro Gonzalez Alzate | ||||
| Title | Multi-modal Pedestrian Detection | Type | Book Whole | ||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Pedestrian detection continues to be an extremely challenging problem in real scenarios, in which situations like illumination changes, noisy images, unexpected objects, uncontrolled scenarios and variant appearance of objects occur constantly. All these problems force the development of more robust detectors for relevant applications like vision-based autonomous vehicles, intelligent surveillance, and pedestrian tracking for behavior analysis. Most reliable vision-based pedestrian detectors base their decision on features extracted using a single sensor capturing complementary features, e.g., appearance, and texture. These features usually are extracted from the current frame, ignoring temporal information, or including it in a post process step e.g., tracking or temporal coherence. Taking into account these issues we formulate the following question: can we generate more robust pedestrian detectors by introducing new information sources in the feature extraction step?
In order to answer this question we develop different approaches for introducing new information sources to well-known pedestrian detectors. We start by the inclusion of temporal information following the Stacked Sequential Learning (SSL) paradigm which suggests that information extracted from the neighboring samples in a sequence can improve the accuracy of a base classifier. We then focus on the inclusion of complementary information from different sensors like 3D point clouds (LIDAR – depth), far infrared images (FIR), or disparity maps (stereo pair cameras). For this end we develop a multi-modal framework in which information from different sensors is used for increasing detection accuracy (by increasing information redundancy). Finally we propose a multi-view pedestrian detector, this multi-view approach splits the detection problem in n sub-problems. Each sub-problem will detect objects in a given specific view reducing in that way the variability problem faced when a single detectors is used for the whole problem. We show that these approaches obtain competitive results with other state-of-the-art methods but instead of design new features, we reuse existing ones boosting their performance. |
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| Address | November 2015 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | David Vazquez;Antonio Lopez; | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-943427-7-6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number |
Admin @ si @ Gon2015 | Serial | 2706 | ||
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| Author | Albert Gordo; Florent Perronnin | ||||
| Title | A Bag-of-Pages Approach to Unordered Multi-Page Document Classification | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1920–1923 | ||
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| Abstract | We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system. | ||||
| Address | Istanbul (Turkey) | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ GoP2010 | Serial | 1480 | ||
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| Author | Albert Gordo; Florent Perronnin | ||||
| Title | Asymmetric Distances for Binary Embeddings | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 729 - 736 | ||
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| Abstract | In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. | ||||
| Address | Providence, RI | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4577-0394-2 | Medium | ||
| Area | Expedition | Conference | CVPR | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ GoP2011; IAM @ iam @ GoP2011 | Serial | 1817 | ||
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| Author | Albert Gordo | ||||
| Title | A Cyclic Page Layout Descriptor for Document Classification & Retrieval | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 128 | Issue | Pages | ||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Area | Expedition | Conference | |||
| Notes | CIC;DAG | Approved | no | ||
| Call Number |
Admin @ si @ Gor2009 | Serial | 2387 | ||
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| Author | Albert Gordo | ||||
| Title | Document Image Representation, Classification and Retrieval in Large-Scale Domains | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements. Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny;Florent Perronnin | |
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| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ Gor2013 | Serial | 2277 | ||
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| Author | Debora Gil; Rosa Maria Ortiz; Carles Sanchez; Antoni Rosell | ||||
| Title | Objective endoscopic measurements of central airway stenosis. A pilot study | Type | Journal Article | ||
| Year | 2018 | Publication | Respiration | Abbreviated Journal | RES |
| Volume | 95 | Issue | Pages | 63–69 | |
| Keywords | Bronchoscopy; Tracheal stenosis; Airway stenosis; Computer-assisted analysis | ||||
| Abstract | Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. Objective: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. Methods: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. Results: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. Conclusion: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI. | ||||
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| Notes | IAM; 600.075; 600.096; 600.145 | Approved | no | ||
| Call Number |
Admin @ si @ GOS2018 | Serial | 3043 | ||
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| Author | S.Grau; Anna Puig; Sergio Escalera; Maria Salamo; Oscar Amoros | ||||
| Title | Efficient complementary viewpoint selection in volume rendering | Type | Conference Article | ||
| Year | 2013 | Publication | 21st WSCG Conference on Computer Graphics, | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Dual camera; Visualization; Interactive Interfaces; Dynamic Time Warping. | ||||
| Abstract | A major goal of visualization is to appropriately express knowledge of scientific data. Generally, gathering visual information contained in the volume data often requires a lot of expertise from the final user to setup the parameters of the visualization. One way of alleviating this problem is to provide the position of inner structures with different viewpoint locations to enhance the perception and construction of the mental image. To this end, traditional illustrations use two or three different views of the regions of interest. Similarly, with the aim of assisting the users to easily place a good viewpoint location, this paper proposes an automatic and interactive method that locates different complementary viewpoints from a reference camera in volume datasets. Specifically, the proposed method combines the quantity of information each camera provides for each structure and the shape similarity of the projections of the remaining viewpoints based on Dynamic Time Warping. The selected complementary viewpoints allow a better understanding of the focused structure in several applications. Thus, the user interactively receives feedback based on several viewpoints that helps him to understand the visual information. A live-user evaluation on different data sets show a good convergence to useful complementary viewpoints. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-808694374-9 | Medium | ||
| Area | Expedition | Conference | WSCG | ||
| Notes | HuPBA; 600.046;MILAB | Approved | no | ||
| Call Number |
Admin @ si @ GPE2013a | Serial | 2255 | ||
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