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Author |
S.Grau; Anna Puig; Sergio Escalera; Maria Salamo; Oscar Amoros |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Efficient complementary viewpoint selection in volume rendering |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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21st WSCG Conference on Computer Graphics, |
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Dual camera; Visualization; Interactive Interfaces; Dynamic Time Warping. |
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Abstract |
A major goal of visualization is to appropriately express knowledge of scientific data. Generally, gathering visual information contained in the volume data often requires a lot of expertise from the final user to setup the parameters of the visualization. One way of alleviating this problem is to provide the position of inner structures with different viewpoint locations to enhance the perception and construction of the mental image. To this end, traditional illustrations use two or three different views of the regions of interest. Similarly, with the aim of assisting the users to easily place a good viewpoint location, this paper proposes an automatic and interactive method that locates different complementary viewpoints from a reference camera in volume datasets. Specifically, the proposed method combines the quantity of information each camera provides for each structure and the shape similarity of the projections of the remaining viewpoints based on Dynamic Time Warping. The selected complementary viewpoints allow a better understanding of the focused structure in several applications. Thus, the user interactively receives feedback based on several viewpoints that helps him to understand the visual information. A live-user evaluation on different data sets show a good convergence to useful complementary viewpoints. |
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978-808694374-9 |
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WSCG |
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HuPBA; 600.046;MILAB |
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Admin @ si @ GPE2013a |
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2255 |
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Author |
Vitaliy Konovalov; Albert Clapes; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automatic Hand Detection in RGB-Depth Data Sequences |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
Publication |
16th Catalan Conference on Artificial Intelligence |
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91-100 |
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Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, viewpoint variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point.
In a given frame, the human body is segmented first in the depth image. Then, a
graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths’ connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures. |
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Vic; October 2013 |
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CCIA |
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HuPBA;MILAB |
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Admin @ si @ KCE2013 |
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2323 |
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Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Tri-modal Person Re-identification with RGB, Depth and Thermal Features |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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2013 |
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9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition |
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301-307 |
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Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios. |
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Portland; oregon; June 2013 |
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978-0-7695-4990-3 |
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CVPRW |
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HUPBA;MILAB |
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Admin @ si @ MBM2013 |
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2253 |
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Author |
Daniel Sanchez; J.C.Ortega; Miguel Angel Bautista |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
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7887 |
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50-58 |
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Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts |
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Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38627-5 |
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IbPRIA |
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HUPBA |
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no |
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SOB2013 |
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2250 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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2013 |
Publication |
26th Canadian Conference on Artificial Intelligence |
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7884 |
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1-12 |
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Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature |
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Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology. |
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Canada; May 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38456-1 |
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AI |
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HuPBA;MILAB |
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no |
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Admin @ si @ BGE2013b |
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2249 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
Publication |
15th International Conference on Computer Analysis of Images and Patterns |
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8048 |
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483-490 |
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Optical flow; regularization; Driver Assistance Systems; Performance Evaluation |
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Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI). |
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York; UK; August 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40245-6 |
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CAIP |
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ADAS; 600.055; 601.215 |
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Admin @ si @ OnS2013b |
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2244 |
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Author |
Alicia Fornes; Xavier Otazu; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Show through cancellation and image enhancement by multiresolution contrast processing |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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200-204 |
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Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 602.006; 600.045; 600.061; 600.052;CIC |
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Admin @ si @ FOL2013 |
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2241 |
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Author |
Sandra Jimenez; Xavier Otazu; Valero Laparra; Jesus Malo |
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Title |
Chromatic induction and contrast masking: similar models, different goals? |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
Publication |
Human Vision and Electronic Imaging XVIII |
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8651 |
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Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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San Francisco CA; USA; February 2013 |
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HVEI |
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CIC |
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Admin @ si @ JOL2013 |
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2240 |
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Author |
Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A new image centrality descriptor for wrinkle frame detection in WCE videos |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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13th IAPR Conference on Machine Vision Applications |
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Small bowel motility dysfunctions are a widespread functional disorder characterized by abdominal pain and altered bowel habits in the absence of specific and unique organic pathology. Current methods of diagnosis are complex and can only be conducted at some highly specialized referral centers. Wireless Video Capsule Endoscopy (WCE) could be an interesting diagnostic alternative that presents excellent clinical advantages, since it is non-invasive and can be conducted by non specialists. The purpose of this work is to present a new method for the detection of wrinkle frames in WCE, a critical characteristic to detect one of the main motility events: contractions. The method goes beyond the use of one of the classical image feature, the Histogram |
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Kyoto; Japan; May 2013 |
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MVA |
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OR; MILAB; 600.046;MV |
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Admin @ si @ SDZ2013 |
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2239 |
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Victor Borjas; Jordi Vitria; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Gradient Histogram Background Modeling for People Detection in Stationary Camera Environments |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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13th IAPR Conference on Machine Vision Applications |
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Best Poster AwardOne of the big challenges of today person detectors is the decreasing of the false positive rate. In this paper, we propose a novel framework to customize person detectors in static camera scenarios in order to reduce this rate. This scheme includes background modeling for subtraction based on gradient histograms and Mean-Shift clustering. Our experiments show that the detection improved compared to using only the output from the pedestrian detector reducing 87% of the false positives and therefore the overall precision of the detection
was increased signicantly. |
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Kyoto; Japan; May 2013 |
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OR; MILAB;MV |
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BVR2013 |
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2238 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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IEEE International Workshop on Analysis and Modeling of Faces and Gestures |
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862-868 |
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Manifold learning techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data---the out-of-sample problem. For the first aspect, the proposed schemes were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only reached for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that sparse coding theory not only serves for automatic graph reconstruction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the k-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on four public face databases. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. |
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Portland; USA; June 2013 |
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CVPRW |
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OR; 600.046;MV |
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Admin @ si @ DoR2013 |
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2236 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Antonio Lopez |
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Title |
VSLAM pose initialization via Lie groups and Lie algebras optimization |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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Proceedings of IEEE International Conference on Robotics and Automation |
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Pages |
5740 - 5747 |
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Keywords |
SLAM |
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Abstract |
We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm. |
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Address |
Karlsruhe; Germany; May 2013 |
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ISSN |
1050-4729 |
ISBN |
978-1-4673-5641-1 |
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Conference |
ICRA |
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Notes |
ADAS; 600.054; 600.055; 600.057 |
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no |
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Call Number |
Admin @ si @ RGS2013a; ADAS @ adas @ |
Serial |
2225 |
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Permanent link to this record |
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Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Integrating Visual and Textual Cues for Query-by-String Word Spotting |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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Pages |
511 - 515 |
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Abstract |
In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. |
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Address |
Washington; USA; August 2013 |
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ISSN |
1520-5363 |
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ICDAR |
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Notes |
DAG; ADAS; 600.045; 600.055; 600.061 |
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no |
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Call Number |
Admin @ si @ ART2013 |
Serial |
2224 |
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Author |
Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2013 |
Publication |
Proceedings of the International Conference on Computer Vision Theory and Applications |
Abbreviated Journal |
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Volume |
1 |
Issue |
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Pages |
153--161 |
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Keywords |
Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model |
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Abstract |
Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability |
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Address |
Barcelona; February 2013 |
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SciTePress |
Place of Publication |
Portugal |
Editor |
Sebastiano Battiato and José Braz |
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LNCS |
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ISSN |
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ISBN |
978-989-8565-47-1 |
Medium |
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Area |
800 |
Expedition |
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Conference |
VISAPP |
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Notes |
IAM;MV; 600.044; 600.047; 600.060; 605.203 |
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no |
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Call Number |
IAM @ iam @ SGR2013 |
Serial |
2123 |
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Author |
Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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Volume |
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Issue |
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Pages |
2312 - 2319 |
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Keywords |
Semantic Segmentation |
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Abstract |
Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
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Address |
Sydney; Australia; December 2013 |
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Edition |
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ISSN |
1550-5499 |
ISBN |
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Area |
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Expedition |
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Conference |
ICCV |
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Notes |
ADAS; 600.057 |
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no |
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Call Number |
ADAS @ adas @ RBN2013 |
Serial |
2377 |
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Permanent link to this record |