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| Author | Laura Igual; Antonio Hernandez; Sergio Escalera; Miguel Reyes; Josep Moya; Joan Carles Soliva; Jordi Faquet; Oscar Vilarroya; Petia Radeva | ||||
| Title | Automatic Techniques for Studying Attention-Deficit/Hyperactivity Disorder | Type | Conference Article | ||
| Year | 2011 | Publication | Jornada TIC Salut Girona | Abbreviated Journal | |
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| Area | Expedition | Conference | TICGI | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ IHE2011 | Serial | 1755 | ||
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| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | Approximate Convex Hulls Family for One-Class Cassification | Type | Conference Article | ||
| Year | 2011 | Publication | 10th International Workshop on Multiple Classifier Systems | Abbreviated Journal | |
| Volume | 6713 | Issue | Pages | 106-115 | |
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| Abstract | In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification. | ||||
| Address | Napoli, Italy | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Carlo Sansone; Josef Kittler; Fabio Roli | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-21556-8 | Medium | |
| Area | Expedition | Conference | MCS | ||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPR2011b | Serial | 1761 | ||
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| Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
| Title | User Verification From Walking Activity. First Steps Towards a Personal Verification System | Type | Conference Article | ||
| Year | 2011 | Publication | 1st International Conference on Pervasive and Embedded Computing and Communication Systems | Abbreviated Journal | |
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| Address | Algarve, Portugal | ||||
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| Area | Expedition | Conference | PECCS | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPR2011c | Serial | 1762 | ||
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| Author | Francesco Ciompi; Oriol Pujol; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | Automatic Key Frames Detection in Intravascular Ultrasound Sequences | Type | Conference Article | ||
| Year | 2011 | Publication | In MICCAI 2011 Workshop on Computing and Visualization for Intra Vascular Imaging | Abbreviated Journal | |
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| Abstract | We present a method for the automatic detection of key frames in Intravascular Ultrasound (IVUS) sequences. The key frames are markers delimiting morphological changes along the vessel. The aim of defining key frames is two-fold: (1) they allow to summarize the content of the pullback into few representative frames; (2) they represent the basis for the automatic detection of clinical events in IVUS. The proposed approach achieved a compression ratio of 0.016 with respect to the original sequence and an average inter-frame distance of 61.76 frame, minimizing the number of missed clinical events. | ||||
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| Area | Expedition | Conference | CVII | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPB2011 | Serial | 1767 | ||
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| Author | Miguel Reyes; Jose Ramirez Moreno; Juan R Revilla; Petia Radeva; Sergio Escalera | ||||
| Title | ADiBAS: Sistema Multisensor de Adquisicion Automatica de Datos Corporales Objetivos, Robustos y Fiables para el Analisis de la Postura y el Movimiento | Type | Conference Article | ||
| Year | 2011 | Publication | 6th Congreso Iberoamericano de Tecnologia de Apoyo a la Discapacidad | Abbreviated Journal | |
| Volume | Issue | Pages | 939-944 | ||
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| Abstract | El análisis de la postura y del rango de movimiento son fundamentales para conocer la optimización del gesto y mejorar, de este modo, el rendimiento y la detección de posibles lesiones. Esta cuantificación es especialmente interesante en deportistas o en pacientes que presentan alguna lesión neurológica o del sistema musculo-esquelético, ya que permite conocer el proceso evolutivo de estos pacientes, evaluar la eficacia de la terapia aplicada y proponer, en caso necesario, una modificación del protocolo de tratamiento.
En este trabajo presentamos un sistema automático que permite, mediante una tecnología no invasiva, la captación automática de marcadores LED situados sobre el paciente y su posterior análisis con el fin de mostrar al especialista datos objetivos que permitan un mejor soporte diagnóstico. También se describe un sistema analítico de la postura corporal sin marcadores, donde su ejecución durante secuencias dinámicas aporta un alto grado de naturalidad al paciente a la hora de realizar los ejercicios funcionales. |
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| Address | Palma de Mallorca | ||||
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| Area | Expedition | Conference | IBERDISCAP | ||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ RRR2011 | Serial | 1768 | ||
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| Author | Antonio Hernandez; Carlo Gatta; Sergio Escalera; Laura Igual; Victoria Martin Yuste; Petia Radeva | ||||
| Title | Accurate and Robust Fully-Automatic QCA: Method and Numerical Validation | Type | Conference Article | ||
| Year | 2011 | Publication | 14th International Conference on Medical Image Computing and Computer Assisted Intervention | Abbreviated Journal | |
| Volume | 14 | Issue | 3 | Pages | 496-503 |
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| Abstract | The Quantitative Coronary Angiography (QCA) is a methodology used to evaluate the arterial diseases and, in particular, the degree of stenosis. In this paper we propose AQCA, a fully automatic method for vessel segmentation based on graph cut theory. Vesselness, geodesic paths and a new multi-scale edgeness map are used to compute a globally optimal artery segmentation. We evaluate the method performance in a rigorous numerical way on two datasets. The method can detect an artery with precision 92.9 +/- 5% and sensitivity 94.2 +/- 6%. The average absolute distance error between detected and ground truth centerline is 1.13 +/- 0.11 pixels (about 0.27 +/- 0.025 mm) and the absolute relative error in the vessel caliber estimation is 2.93% with almost no bias. Moreover, the method can discriminate between arteries and catheter with an accuracy of 96.4%. | ||||
| Address | Toronto, Canada | ||||
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| Publisher | Springer | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-3-642-23625-9 | Medium | ||
| Area | Expedition | Conference | MICCAI | ||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ HGE2011 | Serial | 1769 | ||
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| Author | Xavier Carrillo; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Petia Radeva; Neus Salvatella; Oriol Pujol; J. Mauri; A. Bayes | ||||
| Title | Changes in Radial Artery Volume Assessed Using Intravascular Ultrasound: A Comparison of Two Vasodilator Regimens in Transradial Coronary Intervention | Type | Journal Article | ||
| Year | 2011 | Publication | Journal of Invasive Cardiology | Abbreviated Journal | JOIC |
| Volume | 23 | Issue | 10 | Pages | 401-404 |
| Keywords | radial; vasodilator treatment; percutaneous coronary intervention; IVUS; volumetric IVUS analysis | ||||
| Abstract | OBJECTIVES:
This study used intravascular ultrasound (IVUS) to evaluate radial artery volume changes after intraarterial administration of nitroglycerin and/or verapamil. BACKGROUND: Radial artery spasm, which is associated with radial artery size, is the main limitation of the transradial approach in percutaneous coronary interventions (PCI). METHODS: This prospective, randomized study compared the effect of two intra-arterial vasodilator regimens on radial artery volume: 0.2 mg of nitroglycerin plus 2.5 mg of verapamil (Group 1; n = 15) versus 2.5 mg of verapamil alone (Group 2; n = 15). Radial artery lumen volume was assessed using IVUS at two time points: at baseline (5 minutes after sheath insertion) and post-vasodilator (1 minute after drug administration). The luminal volume of the radial artery was computed using ECOC Random Fields (ECOC-RF), a technique used for automatic segmentation of luminal borders in longitudinal cut images from IVUS sequences. RESULTS: There was a significant increase in arterial lumen volume in both groups, with an increase from 451 ± 177 mm³ to 508 ± 192 mm³ (p = 0.001) in Group 1 and from 456 ± 188 mm³ to 509 ± 170 mm³ (p = 0.001) in Group 2. There were no significant differences between the groups in terms of absolute volume increase (58 mm³ versus 53 mm³, respectively; p = 0.65) or in relative volume increase (14% versus 20%, respectively; p = 0.69). CONCLUSIONS: Administration of nitroglycerin plus verapamil or verapamil alone to the radial artery resulted in similar increases in arterial lumen volume according to ECOC-RF IVUS measurements. |
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CFC2011 | Serial | 1797 | ||
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| Author | Francesco Ciompi; A. Palaioroutas; M. Loeve; Oriol Pujol; Petia Radeva; H. Tiddens; M. de Bruijne | ||||
| Title | Lung Tissue Classification in Severe Advanced Cystic Fibrosis from CT Scans | Type | Conference Article | ||
| Year | 2011 | Publication | In MICCAI 2011 4th International Workshop on Pulmonary Image Analysis | Abbreviated Journal | |
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| Address | Toronto, Canada | ||||
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| Area | Expedition | Conference | PIA | ||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPL2011 | Serial | 1798 | ||
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| Author | Carlo Gatta; Eloi Puertas; Oriol Pujol | ||||
| Title | Multi-Scale Stacked Sequential Learning | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 44 | Issue | 10-11 | Pages | 2414-2416 |
| Keywords | Stacked sequential learning; Multiscale; Multiresolution; Contextual classification | ||||
| Abstract | One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ GPP2011 | Serial | 1802 | ||
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| Author | Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera | ||||
| Title | Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps | Type | Conference Article | ||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 726-732 | ||
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| Abstract | We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. | ||||
| Address | Portland; Oregon; June 2013 | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ HZM2012b | Serial | 2046 | ||
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| Author | Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Xavier Jimenez ; Oscar Vilarroya; Petia Radeva | ||||
| Title | A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder | Type | Journal Article | ||
| Year | 2011 | Publication | BioMedical Engineering Online | Abbreviated Journal | BEO |
| Volume | 10 | Issue | 105 | Pages | 1-23 |
| Keywords | Brain caudate nucleus; segmentation; MRI; atlas-based strategy; Graph Cut framework | ||||
| Abstract | Background
Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD. |
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| ISSN | 1475-925X | ISBN | Medium | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ISH2011 | Serial | 1882 | ||
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| Author | Sergio Escalera; Josep Moya; Laura Igual; Veronica Violant; Maria Teresa Anguera | ||||
| Title | Automatic Human Behavior Analysis in ADHD | Type | Conference Article | ||
| Year | 2012 | Publication | Eunethydis 2nd International ADHD Conference | Abbreviated Journal | |
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| Abstract | Poster | ||||
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| Area | Expedition | Conference | EUNETHYDIS | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EMI2012a | Serial | 2058 | ||
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| Author | Francesco Ciompi; Oriol Pujol; Carlo Gatta; Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | HoliMab: A Holistic Approach for Media-Adventitia Border Detection in Intravascular Ultrasound | Type | Journal Article | ||
| Year | 2012 | Publication | Medical Image Analysis | Abbreviated Journal | MIA |
| Volume | 16 | Issue | 6 | Pages | 1085-1100 |
| Keywords | Media–Adventitia border detection; Intravascular ultrasound; Multi-Scale Stacked Sequential Learning; Error-correcting output codes; Holistic segmentation | ||||
| Abstract | We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed. | ||||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPG2012 | Serial | 1995 | ||
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| Author | Marina Alberti; Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Joana Silva; Xavier Carrillo; Petia Radeva | ||||
| Title | Automatic Bifurcation Detection in Coronary IVUS Sequences | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Biomedical Engineering | Abbreviated Journal | TBME |
| Volume | 59 | Issue | 4 | Pages | 1022-2031 |
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| Abstract | In this paper, we present a fully automatic method which identifies every bifurcation in an intravascular ultrasound (IVUS) sequence, the corresponding frames, the angular orientation with respect to the IVUS acquisition, and the extension. This goal is reached using a two-level classification scheme: first, a classifier is applied to a set of textural features extracted from each image of a sequence. A comparison among three state-of-the-art discriminative classifiers (AdaBoost, random forest, and support vector machine) is performed to identify the most suitable method for the branching detection task. Second, the results are improved by exploiting contextual information using a multiscale stacked sequential learning scheme. The results are then successively refined using a-priori information about branching dimensions and geometry. The proposed approach provides a robust tool for the quick review of pullback sequences, facilitating the evaluation of the lesion at bifurcation sites. The proposed method reaches an F-Measure score of 86.35%, while the F-Measure scores for inter- and intraobserver variability are 71.63% and 76.18%, respectively. The obtained results are positive. Especially, considering the branching detection task is very challenging, due to high variability in bifurcation dimensions and appearance. | ||||
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| ISSN | 0018-9294 | ISBN | Medium | ||
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MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ABG2012 | Serial | 1996 | ||
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| Author | Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera | ||||
| Title | Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization | Type | Journal Article | ||
| Year | 2012 | Publication | Journal of Ambient Intelligence and Smart Environments | Abbreviated Journal | JAISE |
| Volume | 4 | Issue | 6 | Pages | 535-546 |
| Keywords | Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation | ||||
| Abstract | We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. | ||||
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| ISSN | 1876-1364 | ISBN | Medium | ||
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| Notes |
MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ HZM2012a | Serial | 2006 | ||
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