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Author Olivier Penacchio; C. Alejandro Parraga
Title What is the best criterion for an efficient design of retinal photoreceptor mosaics? Type Journal Article
Year 2011 Publication Perception Abbreviated Journal PER
Volume (down) 40 Issue Pages 197
Keywords
Abstract The proportions of L, M and S photoreceptors in the primate retina are arguably determined by evolutionary pressure and the statistics of the visual environment. Two information theory-based approaches have been recently proposed for explaining the asymmetrical spatial densities of photoreceptors in humans. In the first approach Garrigan et al (2010 PLoS ONE 6 e1000677), a model for computing the information transmitted by cone arrays which considers the differential blurring produced by the long-wavelength accommodation of the eye’s lens is proposed. Their results explain the sparsity of S-cones but the optimum depends weakly on the L:M cone ratio. In the second approach (Penacchio et al, 2010 Perception 39 ECVP Supplement, 101), we show that human cone arrays make the visual representation scale-invariant, allowing the total entropy of the signal to be preserved while decreasing individual neurons’ entropy in further retinotopic representations. This criterion provides a thorough description of the distribution of L:M cone ratios and does not depend on differential blurring of the signal by the lens. Here, we investigate the similarities and differences of both approaches when applied to the same database. Our results support a 2-criteria optimization in the space of cone ratios whose components are arguably important and mostly unrelated.
[This work was partially funded by projects TIN2010-21771-C02-1 and Consolider-Ingenio 2010-CSD2007-00018 from the Spanish MICINN. CAP was funded by grant RYC-2007-00484]
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Notes CIC Approved no
Call Number Admin @ si @ PeP2011a Serial 1719
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Author C. Alejandro Parraga; Olivier Penacchio; Maria Vanrell
Title Retinal Filtering Matches Natural Image Statistics at Low Luminance Levels Type Journal Article
Year 2011 Publication Perception Abbreviated Journal PER
Volume (down) 40 Issue Pages 96
Keywords
Abstract The assumption that the retina’s main objective is to provide a minimum entropy representation to higher visual areas (ie efficient coding principle) allows to predict retinal filtering in space–time and colour (Atick, 1992 Network 3 213–251). This is achieved by considering the power spectra of natural images (which is proportional to 1/f2) and the suppression of retinal and image noise. However, most studies consider images within a limited range of lighting conditions (eg near noon) whereas the visual system’s spatial filtering depends on light intensity and the spatiochromatic properties of natural scenes depend of the time of the day. Here, we explore whether the dependence of visual spatial filtering on luminance match the changes in power spectrum of natural scenes at different times of the day. Using human cone-activation based naturalistic stimuli (from the Barcelona Calibrated Images Database), we show that for a range of luminance levels, the shape of the retinal CSF reflects the slope of the power spectrum at low spatial frequencies. Accordingly, the retina implements the filtering which best decorrelates the input signal at every luminance level. This result is in line with the body of work that places efficient coding as a guiding neural principle.
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Notes CIC Approved no
Call Number Admin @ si @ PPV2011 Serial 1720
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Author J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin
Title Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm Type Journal Article
Year 2013 Publication Expert Systems with Applications Abbreviated Journal EXWA
Volume (down) 40 Issue 17 Pages 6707-6712
Keywords Neural gas; Expert vision; Eye-tracking; Fixations
Abstract Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 0957-4174 ISBN Medium
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Notes ADAS Approved no
Call Number Admin @ si @ CRM2013 Serial 2438
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Author Tadashi Araki; Sumit K. Banchhor; Narendra D. Londhe; Nobutaka Ikeda; Petia Radeva; Devarshi Shukla; Luca Saba; Antonella Balestrieri; Andrew Nicolaides; Shoaib Shafique; John R. Laird; Jasjit S. Suri
Title Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos Type Journal Article
Year 2016 Publication Journal of Medical Systems Abbreviated Journal JMS
Volume (down) 40 Issue 3 Pages 51:1-51:20
Keywords Interventional cardiology; Atherosclerosis; Coronary arteries; IVUS; calcium volume; Soft computing; Performance Reliability; Accuracy
Abstract Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically.
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Notes MILAB; Approved no
Call Number Admin @ si @ ABL2016 Serial 2729
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Author Arash Akbarinia; C. Alejandro Parraga
Title Colour Constancy Beyond the Classical Receptive Field Type Journal Article
Year 2018 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume (down) 40 Issue 9 Pages 2081 - 2094
Keywords
Abstract The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us.
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Notes NEUROBIT; 600.068; 600.072 Approved no
Call Number Admin @ si @ AkP2018a Serial 2990
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Author Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera
Title Error-Correcting Factorization Type Journal Article
Year 2018 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume (down) 40 Issue Pages 2388-2401
Keywords
Abstract Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0162-8828 ISBN Medium
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ BPT2018 Serial 3015
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Author Hugo Bertiche; Meysam Madadi; Sergio Escalera
Title PBNS: Physically Based Neural Simulation for Unsupervised Garment Pose Space Deformation Type Journal Article
Year 2021 Publication ACM Transactions on Graphics Abbreviated Journal
Volume (down) 40 Issue 6 Pages 1-14
Keywords
Abstract We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to unsupervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth.
While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ BME2021c Serial 3643
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Author Victor M. Campello; Polyxeni Gkontra; Cristian Izquierdo; Carlos Martin-Isla; Alireza Sojoudi; Peter M. Full; Klaus Maier-Hein; Yao Zhang; Zhiqiang He; Jun Ma; Mario Parreno; Alberto Albiol; Fanwei Kong; Shawn C. Shadden; Jorge Corral Acero; Vaanathi Sundaresan; Mina Saber; Mustafa Elattar; Hongwei Li; Bjoern Menze; Firas Khader; Christoph Haarburger; Cian M. Scannell; Mitko Veta; Adam Carscadden; Kumaradevan Punithakumar; Xiao Liu; Sotirios A. Tsaftaris; Xiaoqiong Huang; Xin Yang; Lei Li; Xiahai Zhuang; David Vilades; Martin L. Descalzo; Andrea Guala; Lucia La Mura; Matthias G. Friedrich; Ria Garg; Julie Lebel; Filipe Henriques; Mahir Karakas; Ersin Cavus; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Jose F. Rodriguez Palomares; Karim Lekadir
Title Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge Type Journal Article
Year 2021 Publication IEEE Transactions on Medical Imaging Abbreviated Journal TMI
Volume (down) 40 Issue 12 Pages 3543-3554
Keywords
Abstract The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ CGI2021 Serial 3653
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Author David Masip; Agata Lapedriza; Jordi Vitria
Title Boosted Online Learning for Face Recognition Type Journal Article
Year 2009 Publication IEEE Transactions on Systems, Man and Cybernetics part B Abbreviated Journal TSMCB
Volume (down) 39 Issue 2 Pages 530–538
Keywords
Abstract Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1083–4419 ISBN Medium
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MLV2009 Serial 1155
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Author Fadi Dornaika; Bogdan Raducanu
Title Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application Type Journal Article
Year 2009 Publication IEEE Transactions on Systems, Man and Cybernetics part B Abbreviated Journal TSMCB
Volume (down) 39 Issue 4 Pages 935–944
Keywords
Abstract Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ DoR2009a Serial 1218
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Author C. Alejandro Parraga; Robert Benavente; Maria Vanrell
Title Towards a general model of colour categorization which considers context Type Journal Article
Year 2010 Publication Perception. ECVP Abstract Supplement Abbreviated Journal PER
Volume (down) 39 Issue Pages 86
Keywords
Abstract In two previous experiments [Parraga et al, 2009 J. of Im. Sci. and Tech 53(3) 031106; Benavente et al,2009 Perception 38 ECVP Supplement, 36] the boundaries of basic colour categories were measured.
In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours.
Results from these experiments showed significant di erences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the di erences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that
although boundary locations are very similar, boundaries measured in context are significantly di erent(more di use) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions). In addition, we completed the mapping of colour name space by measuring the boundaries between chromatic colours and the achromatic centre. With these results we
completed our parametric fuzzy-sets model of colour naming space.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes CIC Approved no
Call Number CAT @ cat @ PBV2010b Serial 1326
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Author Olivier Penacchio; C. Alejandro Parraga; Maria Vanrell
Title Natural Scene Statistics account for Human Cones Ratios Type Journal Article
Year 2010 Publication Perception. ECVP Abstract Supplement Abbreviated Journal PER
Volume (down) 39 Issue Pages 101
Keywords
Abstract In two previous experiments [Parraga et al, 2009 J. of Im. Sci. and Tech 53(3) 031106; Benavente et al,2009 Perception 38 ECVP Supplement, 36] the boundaries of basic colour categories were measured.
In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours.
Results from these experiments showed significant di erences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the di erences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that
although boundary locations are very similar, boundaries measured in context are significantly di erent(more di use) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions). In addition, we completed the mapping of colour name space by measuring the boundaries between chromatic colours and the achromatic centre. With these results we completed our parametric fuzzy-sets model of colour naming space.
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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
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Area Expedition Conference
Notes CIC Approved no
Call Number CAT @ cat @ PPV2010 Serial 1357
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Rank Estimation in Missing Data Matrix Problems Type Journal Article
Year 2011 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume (down) 39 Issue 2 Pages 140-160
Keywords
Abstract A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0924-9907 ISBN Medium
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Notes ADAS Approved no
Call Number Admin @ si @ JSL2011; Serial 1710
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Author Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva
Title Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation Type Journal Article
Year 2013 Publication Ultrasound in Medicine and Biology Abbreviated Journal UMB
Volume (down) 39 Issue 9 Pages 1698-712
Keywords Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy
Abstract Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages.
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Notes MILAB Approved no
Call Number Admin @ si @ ABC2013 Serial 2313
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Author Simone Balocco; Carlo Gatta; Marina Alberti; Xavier Carrillo; Juan Rigla; Petia Radeva
Title Relation between plaque type, plaque thickness, blood shear stress and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound Type Journal Article
Year 2012 Publication Medical Physics Abbreviated Journal MEDPHYS
Volume (down) 39 Issue 12 Pages 7430-7445
Keywords
Abstract PMID 23231293
PURPOSE:
Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries.
METHODS:
First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound (IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations.
RESULTS:
The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall.
CONCLUSIONS:
Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed.
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Notes MILAB Approved no
Call Number Admin @ si @BGA2012 Serial 2170
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