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Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
Title | Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 30 | Issue | 3 | Pages | 285–297 |
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Abstract | Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. | ||||
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Notes | MILAB;HuPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EPR2009a | Serial | 1153 | ||
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Author | Marçal Rusiñol; Agnes Borras; Josep Llados | ||||
Title | Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images | Type | Journal Article | ||
Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 31 | Issue | 3 | Pages | 188–201 |
Keywords | Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings | ||||
Abstract | This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ RBL2010 | Serial | 1177 | ||
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Author | Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados | ||||
Title | Blurred Shape Model for Binary and Grey-level Symbol Recognition | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 30 | Issue | 15 | Pages | 1424–1433 |
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Abstract | Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. | ||||
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Notes | HuPBA; DAG; MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EFP2009a | Serial | 1180 | ||
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Author | Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados | ||||
Title | Unsupervised writer adaptation of whole-word HMMs with application to word-spotting | Type | Journal Article | ||
Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 31 | Issue | 8 | Pages | 742–749 |
Keywords | Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis | ||||
Abstract | In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ RPS2010 | Serial | 1290 | ||
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Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
Title | Re-coding ECOCs without retraining | Type | Journal Article | ||
Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 31 | Issue | 7 | Pages | 555–562 |
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Abstract | A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | MILAB;HUPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
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Author | Josep Llados; Horst Bunke; Enric Marti | ||||
Title | Finding rotational symmetries by cyclic string matching | Type | Journal Article | ||
Year | 1997 | Publication | Pattern recognition letters | Abbreviated Journal | PRL |
Volume | 18 | Issue | 14 | Pages | 1435-1442 |
Keywords | Rotational symmetry; Reflectional symmetry; String matching | ||||
Abstract | Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG;IAM; | Approved | no | ||
Call Number | IAM @ iam @ LBM1997a | Serial | 1562 | ||
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Author | Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca | ||||
Title | Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 32 | Issue | 13 | Pages | 1581-1587 |
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Abstract | Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.
This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one. In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster. |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ PGB2011a | Serial | 1707 | ||
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Author | Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
Title | Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 32 | Issue | 6 | Pages | 878–889 |
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Abstract | The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless, existing public databases cannot scale the presence of appearing agents, which would be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to show the feasibility of the proposed approach in terms of robustness, scalability, and semantics. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ FBR2011b | Serial | 1723 | ||
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Author | Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo | ||||
Title | Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 50 | Issue | 1 | Pages | 112-121 |
Keywords | RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition | ||||
Abstract | PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach. |
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Notes | HuPBA;MV; 605.203 | Approved | no | ||
Call Number | Admin @ si @ HBP2014 | Serial | 2353 | ||
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Author | Jaume Gibert; Ernest Valveny; Horst Bunke | ||||
Title | Feature Selection on Node Statistics Based Embedding of Graphs | Type | Journal Article | ||
Year | 2012 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 33 | Issue | 15 | Pages | 1980–1990 |
Keywords | Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification | ||||
Abstract | Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. | ||||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ GVB2012b | Serial | 1993 | ||
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Author | Albert Clapes; Miguel Reyes; Sergio Escalera | ||||
Title | Multi-modal User Identification and Object Recognition Surveillance System | Type | Journal Article | ||
Year | 2013 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 34 | Issue | 7 | Pages | 799-808 |
Keywords | Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning | ||||
Abstract | We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | HUPBA; 600.046; 605.203;MILAB | Approved | no | ||
Call Number | Admin @ si @ CRE2013 | Serial | 2248 | ||
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Author | Fernando Barrera; Felipe Lumbreras; Angel Sappa | ||||
Title | Multispectral Piecewise Planar Stereo using Manhattan-World Assumption | Type | Journal Article | ||
Year | 2013 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 34 | Issue | 1 | Pages | 52-61 |
Keywords | Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images | ||||
Abstract | This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. | ||||
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Notes | ADAS; 600.054; 600.055; 605.203 | Approved | no | ||
Call Number | Admin @ si @ BLS2013 | Serial | 2245 | ||
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Author | Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro | ||||
Title | Non-Verbal Communication Analysis in Victim-Offender Mediations | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 67 | Issue | 1 | Pages | 19-27 |
Keywords | Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning | ||||
Abstract | We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals. | ||||
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Notes | HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ PEP2015 | Serial | 2583 | ||
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Author | Frederic Sampedro; Sergio Escalera; Anna Puig | ||||
Title | Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 46 | Issue | Pages | 1-10 | |
Keywords | Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation | ||||
Abstract | In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ SEP2014 | Serial | 2550 | ||
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Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen | ||||
Title | Compact color texture description for texture classification | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 51 | Issue | Pages | 16-22 | |
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Abstract | Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively. |
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Notes | LAMP; 600.068; 600.079;ADAS | Approved | no | ||
Call Number | Admin @ si @ KRW2015a | Serial | 2587 | ||
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