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Author |
Albert Andaluz |
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Title |
LV Contour Segmentation in TMR images using Semantic Description of Tissue and Prior Knowledge Correction |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
142 |
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Keywords |
Active Contour Models; Snakes; Active Shape Models; Deformable Templates; Left Ventricle Segmentation; Generalized Orthogonal Procrustes Analysis; Harmonic Phase Flow; Principal Component Analysis; Tagged Magnetic Resonance |
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Abstract |
The Diagnosis of Left Ventricle (LV) pathologies is related to regional wall motion analysis. Health indicator scores such as the rotation and the torsion are useful for the diagnose of the Left Ventricle (LV) function. However, this requires proper identification of LV segments. On one hand, manual segmentation is robust, but it is slow and requires medical expertise. On the other hand, the tag pattern in Tagged Magnetic Resonance (TMR) sequences is a problem for the automatic segmentation of the LV boundaries. Consequently, we propose a method based in the classical formulation of parametric Snakes, combined with Active Shape models. Our semantic definition of the LV is tagged tissue that experiences motion in the systolic cycle. This defines two energy potentials for the Snake convergence. Additionally, the mean shape corrects excessive deviation from the anatomical shape. We have validated our approach in 15 healthy volunteers and two short axis cuts. In this way, we have compared the automatic segmentations to manual shapes outlined by medical experts. Also, we have explored the accuracy of clinical scores computed using automatic contours. The results show minor divergence in the approximation and the manual segmentations as well as robust computation of clinical scores in all cases. From this we conclude that the proposed method is a promising support tool for clinical analysis. |
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Master's thesis |
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Bellaterra 08193, Barcelona, Spain |
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IAM; |
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no |
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IAM @ iam @ And2009 |
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1667 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Logo Spotting by a Bag-of-words Approach for Document Categorization |
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Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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111–115 |
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In this paper we present a method for document categorization which processes incoming document images such as invoices or receipts. The categorization of these document images is done in terms of the presence of a certain graphical logo detected without segmentation. The graphical logos are described by a set of local features and the categorization of the documents is performed by the use of a bag-of-words model. Spatial coherence rules are added to reinforce the correct category hypothesis, aiming also to spot the logo inside the document image. Experiments which demonstrate the effectiveness of this system on a large set of real data are presented. |
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Barcelona; Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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DAG @ dag @ RuL2009b |
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1179 |
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Author |
Jaume Gibert |
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Title |
Learning structural representations and graph matching paradigms in the context of object recognition |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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143 |
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Computer Vision Center |
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Master's thesis |
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DAG |
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Admin @ si @ Gib2009 |
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2397 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
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Conference Article |
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Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
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565–572 |
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Keywords |
road detection |
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Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
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Miami (USA) |
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1063-6919 |
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978-1-4244-3992-8 |
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CVPR |
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ADAS;ISE |
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no |
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ADAS @ adas @ AGL2009 |
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1169 |
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Author |
Xavier Boix |
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Title |
Learning Conditional Random Fields for Stereo |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
136 |
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Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Boi2009 |
Serial |
2395 |
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Author |
Joost Van de Weijer; Cordelia Schmid; Jakob Verbeek; Diane Larlus |
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Title |
Learning Color Names for Real-World Applications |
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Journal Article |
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Year |
2009 |
Publication |
IEEE Transaction in Image Processing |
Abbreviated Journal |
TIP |
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Volume |
18 |
Issue |
7 |
Pages |
1512–1524 |
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Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a well-defined experimental setup by human test subjects. However naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labelling real-world images with color names we use Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips for both image retrieval and image annotation. |
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1057-7149 |
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no |
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CAT @ cat @ WSV2009 |
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1195 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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Journal Article |
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Year |
2009 |
Publication |
Journal of Signal Processing Systems |
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55 |
Issue |
1-3 |
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35–47 |
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Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPM2009 |
Serial |
1258 |
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Author |
Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
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Title |
Intestinal motor activity, endoluminal motion and transit |
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Journal Article |
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Year |
2009 |
Publication |
Neurogastroenterology & Motility |
Abbreviated Journal |
NEUMOT |
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Volume |
21 |
Issue |
12 |
Pages |
1264–e119 |
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A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression |
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Journal Article |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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30 |
Issue |
5 |
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535–543 |
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This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes. |
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Elsevier Science Inc. |
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0167-8655 |
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ADAS |
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ADAS @ adas @ DoS2009a |
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1115 |
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Author |
Mirko Arnold; Anarta Ghosh; Gerard Lacey; Stephen Patchett; Hugh Mulcahy |
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Indistinct frame detection in colonoscopy videos |
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Conference Article |
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2009 |
Publication |
Machine Vision and Image Processing Conference |
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47-52 |
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800 |
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MV |
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fernando @ fernando @ |
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2424 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Juan J. Villanueva |
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Title |
High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients |
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Conference Article |
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2009 |
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4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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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 the detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a Support Vector Machine (SVM) composed by features at different resolution, from coarse for the first level to fine for the last one.
Considering that the spatial stride of the sliding window search is affected by the HOG features size, unlike previous methods based on Adaboost cascades, we can adopt a spatial stride inversely proportional to the features resolution. This produces that the speed-up of the cascade is not only due to the low number of features that need to be computed in the first levels, but also to the lower number of detection windows that needs to be evaluated.
Experimental results shows that our method permits a detection rate comparable with the state of the art, but at the same time a gain in the speed of the detection search of 10-20 times depending on the cascade configuration. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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ISE |
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ISE @ ise @ PGV2009 |
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1214 |
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Jose Antonio Rodriguez; Florent Perronnin |
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Handwritten word-spotting using hidden Markov models and universal vocabularies |
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Journal Article |
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2009 |
Publication |
Pattern Recognition |
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PR |
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42 |
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9 |
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2103-2116 |
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Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition |
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Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians. |
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Elsevier |
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0031-3203 |
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no |
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Admin @ si @ RoP2009 |
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1053 |
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Author |
Ricard Coll; Alicia Fornes; Josep Llados |
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Title |
Graphological Analysis of Handwritten Text Documents for Human Resources Recruitment |
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Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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1081–1085 |
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The use of graphology in recruitment processes has become a popular tool in many human resources companies. This paper presents a model that links features from handwritten images to a number of personality characteristics used to measure applicant aptitudes for the job in a particular hiring scenario. In particular we propose a model of measuring active personality and leadership of the writer. Graphological features that define such a profile are measured in terms of document and script attributes like layout configuration, letter size, shape, slant and skew angle of lines, etc. After the extraction, data is classified using a neural network. An experimental framework with real samples has been constructed to illustrate the performance of the approach. |
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Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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Call Number |
DAG @ dag @ CFL2009 |
Serial |
1221 |
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Permanent link to this record |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke |
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Title |
Graph-based k-means clustering: A comparison of the set versus the generalized median graph |
Type |
Conference Article |
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Year |
2009 |
Publication |
13th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
5702 |
Issue |
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Pages |
342–350 |
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Abstract |
In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. |
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Address |
Münster, Germany |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-03766-5 |
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Conference |
CAIP |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ FVS2009d |
Serial |
1219 |
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Author |
Jose Carlos Rubio |
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Title |
Graph matching based on graphical models with application to vehicle tracking and classification at night |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
144 |
Issue |
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Pages |
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Keywords |
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Abstract |
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Address |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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Notes |
CIC |
Approved |
no |
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Call Number |
Admin @ si @ Rub2009 |
Serial |
2398 |
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Permanent link to this record |