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Author Bogdan Raducanu; Fadi Dornaika
Title A Discriminative Non-Linear Manifold Learning Technique for Face Recognition Type Book Chapter
Year 2011 Publication Informatics Engineering and Information Science Abbreviated Journal
Volume 254 Issue 6 Pages 339-353
Keywords
Abstract (down) In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. 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 variance in their appearance.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1865-0929 ISBN 978-3-642-25482-6 Medium
Area Expedition Conference ICIEIS
Notes OR;MV Approved no
Call Number Admin @ si @ RaD2011 Serial 1804
Permanent link to this record
 
 
Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach Type Conference Article
Year 2011 Publication 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems Abbreviated Journal
Volume Issue Pages 62-71
Keywords Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time.
Abstract (down) In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction.
Address Rome, Italy
Corporate Author Thesis
Publisher SciTePress Place of Publication Editor Djemal, Khalifa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference MIAD
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BSV2011a Serial 1695
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Author Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; J. Mauri; Petia Radeva
Title A Holistic Approach for the Detection of Media-Adventitia Border in IVUS Type Conference Article
Year 2011 Publication 14th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume 6893 Issue Pages 401-408
Keywords
Abstract (down) In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
Address Toronto, Canada
Corporate Author Thesis
Publisher Springer Berlin Heidelberg 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 978-3-642-23625-9 Medium
Area Expedition Conference MICCAI
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPG2011 Serial 1739
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Author Daniel Ponsa; Joan Serrat; Antonio Lopez
Title On-board image-based vehicle detection and tracking Type Journal Article
Year 2011 Publication Transactions of the Institute of Measurement and Control Abbreviated Journal TIM
Volume 33 Issue 7 Pages 783-805
Keywords vehicle detection
Abstract (down) In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ PSL2011 Serial 1413
Permanent link to this record
 
 
Author Jorge Bernal; Fernando Vilariño; F. Javier Sanchez
Title Towards Intelligent Systems for Colonoscopy Type Book Chapter
Year 2011 Publication Colonoscopy Abbreviated Journal
Volume 1 Issue Pages 257-282
Keywords
Abstract (down) In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions
Address
Corporate Author Thesis
Publisher Intech Place of Publication Editor Paul Miskovitz
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-953-307-568-6 Medium
Area 800 Expedition Conference
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BVS2011 Serial 1697
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Author Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados
Title The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification Type Conference Article
Year 2011 Publication 11th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1511-1515
Keywords
Abstract (down) In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a ground-truth of handwritten music score images. This paper describes the competition details, including the dataset and ground-truth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results.
Address Beijing, China
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 978-0-7695-4520-2 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ FDG2011b Serial 1794
Permanent link to this record
 
 
Author Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal
Title A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video Type Journal Article
Year 2011 Publication Pattern Recognition Abbreviated Journal PR
Volume 44 Issue 8 Pages 1671-1683
Keywords
Abstract (down) In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ SDP2011 Serial 1727
Permanent link to this record
 
 
Author Santiago Segui
Title Contributions to the Diagnosis of Intestinal Motility by Automatic Image Analysis Type Book Whole
Year 2011 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) In the early twenty first century Given Imaging Ltd. presented wireless capsule endoscopy (WCE) as a new technological breakthrough that allowed the visualization of
the intestine by using a small, swallowed camera. This small size device was received
with a high enthusiasm within the medical community, and until now, it is still one
of the medical devices with the highest use growth rate. WCE can be used as a novel
diagnostic tool that presents several clinical advantages, since it is non-invasive and
at the same time it provides, for the first time, a full picture of the small bowel morphology, contents and dynamics. Since its appearance, the WCE has been used to
detect several intestinal dysfunctions such as: polyps, ulcers and bleeding. However,
the visual analysis of WCE videos presents an important drawback: the long time
required by the physicians for proper video visualization. In this sense and regarding
to this limitation, the development of computer aided systems is required for the extensive use of WCE in the medical community.
The work presented in this thesis is a set of contributions for the automatic image
analysis and computer-aided diagnosis of intestinal motility disorders using WCE.
Until now, the diagnosis of small bowel motility dysfunctions was basically performed
by invasive techniques such as the manometry test, which can only be conducted at
some referral centers around the world owing to the complexity of the procedure and
the medial expertise required in the interpretation of the results.
Our contributions are divided in three main blocks:
1. Image analysis by computer vision techniques to detect events in the endoluminal WCE scene. Several methods have been proposed to detect visual events
such as: intestinal contractions, intestinal content, tunnel and wrinkles;
2. Machine learning techniques for the analysis and the manipulation of the data
from WCE. These methods have been proposed in order to overcome the problems that the analysis of WCE presents such as: video acquisition cost, unlabeled data and large number of data;
3. Two different systems for the computer-aided diagnosis of intestinal motility
disorders using WCE. The first system presents a fully automatic method that
aids at discriminating healthy subjects from patients with severe intestinal motor disorders like pseudo-obstruction or food intolerance. The second system presents another automatic method that models healthy subjects and discriminate them from mild intestinal motility patients.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Vitria
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ Seg2011 Serial 1836
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo
Title An inference model for analyzing termination conditions of Evolutionary Algorithms Type Conference Article
Year 2011 Publication 14th Congrès Català en Intel·ligencia Artificial Abbreviated Journal
Volume Issue Pages 216-225
Keywords Evolutionary Computation Convergence, Termination Conditions, Statistical Inference
Abstract (down) In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution.
Address Lleida, Catalonia (Spain)
Corporate Author Associació Catalana Intel·ligència Artificial 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 978-1-60750-841-0 Medium
Area Expedition Conference CCIA
Notes IAM Approved no
Call Number IAM @ iam @ RGG2011a Serial 1677
Permanent link to this record
 
 
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
Keywords
Abstract (down) 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
Permanent link to this record
 
 
Author Eduard Vazquez
Title Unsupervised image segmentation based on material reflectance description and saliency Type Book Whole
Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Ramon Baldrich
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 Admin @ si @ Vaz2011b Serial 1835
Permanent link to this record
 
 
Author David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin
Title Cool world: domain adaptation of virtual and real worlds for human detection using active learning Type Conference Article
Year 2011 Publication NIPS Domain Adaptation Workshop: Theory and Application Abbreviated Journal NIPS-DA
Volume Issue Pages
Keywords Pedestrian Detection; Virtual; Domain Adaptation; Active Learning
Abstract (down) Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity.
Address Granada, Spain
Corporate Author Thesis
Publisher Place of Publication Granada, Spain Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DA-NIPS
Notes ADAS Approved no
Call Number ADAS @ adas @ VLP2011b Serial 1756
Permanent link to this record
 
 
Author David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin
Title Virtual Worlds and Active Learning for Human Detection Type Conference Article
Year 2011 Publication 13th International Conference on Multimodal Interaction Abbreviated Journal
Volume Issue Pages 393-400
Keywords Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning
Abstract (down) Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid.
Address Alicante, Spain
Corporate Author Thesis
Publisher ACM DL Place of Publication New York, NY, USA, USA Editor
Language English Summary Language English Original Title Virtual Worlds and Active Learning for Human Detection
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-0641-6 Medium
Area Expedition Conference ICMI
Notes ADAS Approved yes
Call Number ADAS @ adas @ VLP2011a Serial 1683
Permanent link to this record
 
 
Author Maria Salamo; Sergio Escalera
Title Increasing Retrieval Quality in Conversational Recommenders Type Journal Article
Year 2011 Publication IEEE Transactions on Knowledge and Data Engineering Abbreviated Journal TKDE
Volume 99 Issue Pages 1-1
Keywords
Abstract (down) IF JCR CCIA 2.286 2009 24/103
JCR Impact Factor 2010: 1.851
A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches
Address
Corporate Author Thesis
Publisher IEEE Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1041-4347 ISBN Medium
Area Expedition Conference
Notes MILAB; HuPBA Approved no
Call Number Admin @ si @ SaE2011 Serial 1713
Permanent link to this record
 
 
Author Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol
Title Online Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 32 Issue 3 Pages 458-467
Keywords
Abstract (down) IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication North Holland Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes MILAB;OR;HuPBA;MV Approved no
Call Number Admin @ si @ EMP2011 Serial 1714
Permanent link to this record