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Author |
Mirko Arnold; Anarta Ghosh; Glen Doherty; Hugh Mulcahy; Stephen Patchett; Gerard Lacey |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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Proceedings of the International Conference on Computer Vision Theory and Applications |
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48-53 |
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800 |
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VISIGRAPP |
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MV |
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fernando @ fernando @ |
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2427 |
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Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Speech balloon contour classification in comics |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Comic books digitization combined with subsequent comic book understanding create a variety of new applications, including mobile reading and data mining. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. In this work we detail a novel approach for classifying speech balloon in scanned comics book pages based on their contour time series. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.056 |
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no |
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Admin @ si @ RKB2013 |
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2429 |
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Author |
Fernando Vilariño; Gerard Lacey |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
QUALITY ASSESSMENT IN COLONOSCOPY New challenges through computer vision-based systems |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2009 |
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in Proc. 3rd International Conference on Biomedical Electronics and Devices |
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MV;SIAI |
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fernando @ fernando @ |
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2430 |
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Fernando Vilariño; Gerard Lacey; Jiang Zhou; Hugh Mulcahy; Stephen Patchett |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Automatic Labeling of Colonoscopy Video for Cancer Detection |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2007 |
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In Proc. berian Conference, IbPRIA |
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290-297 |
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800 |
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MV;SIAI |
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fernando @ fernando @ |
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2431 |
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Author |
Patricia Marquez; Debora Gil; R.Mester; Aura Hernandez-Sabate |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Local Analysis of Confidence Measures for Optical Flow Quality Evaluation |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
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3 |
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450-457 |
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Optical Flow; Confidence Measure; Performance Evaluation. |
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Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance. |
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Lisboa; January 2014 |
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VISAPP |
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IAM; ADAS; 600.044; 600.060; 600.057; 601.145; 600.076; 600.075 |
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no |
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Admin @ si @ MGM2014 |
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2432 |
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Author |
Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Cost-sensitive Structured SVM for Multi-category Domain Adaptation |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3886 - 3891 |
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Domain Adaptation; Pedestrian Detection |
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Abstract |
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. |
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Stockholm; Sweden; August 2014 |
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IEEE |
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1051-4651 |
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ADAS; 600.057; 600.054; 601.217; 600.076 |
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ADAS @ adas @ XRV2014a |
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2434 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2013 |
Publication |
20th International Conference On Neural Information Processing |
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Volume |
8228 |
Issue |
III |
Pages |
368-375 |
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Abstract |
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-42050-4 |
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ICONIP |
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ADAS |
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no |
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Admin @ si @ DFD2013 |
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2439 |
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Permanent link to this record |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Generic Subclass Ensemble: A Novel Approach to Ensemble Classification |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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1254 - 1259 |
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Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches. |
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Stockholm; August 2014 |
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1051-4651 |
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ICPR |
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HuPBA;MILAB |
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Admin @ si @ BGE2014b |
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2445 |
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Permanent link to this record |
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Author |
Mohammad Ali Bagheri; Gang Hu; Qigang Gao; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Framework of Multi-Classifier Fusion for Human Action Recognition |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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1260 - 1265 |
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The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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ICPR |
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HuPBA;MILAB |
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Admin @ si @ BHG2014 |
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2446 |
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Author |
Jorge Bernal; Fernando Vilariño; F. Javier Sanchez; M. Arnold; Anarta Ghosh; Gerard Lacey |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Experts vs Novices: Applying Eye-tracking Methodologies in Colonoscopy Video Screening for Polyp Search |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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Year |
2014 |
Publication |
2014 Symposium on Eye Tracking Research and Applications |
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223-226 |
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We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group. |
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USA; March 2014 |
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978-1-4503-2751-0 |
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ETRA |
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MV; 600.047; 600.060;SIAI |
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Admin @ si @ BVS2014 |
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2448 |
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Author |
Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Scale Coding Bag-of-Words for Action Recognition |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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1514-1519 |
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Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant
strategy is sub-optimal since it ignores the multi-scale information
available with each bounding box of a person.
This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music,
riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods. |
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Stockholm; August 2014 |
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ICPR |
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CIC; LAMP; 601.240; 600.074; 600.079 |
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Admin @ si @ KWB2014 |
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2450 |
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Author |
Q. Xue; Laura Igual; A. Berenguel; M. Guerrieri; L. Garrido |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Active Contour Segmentation with Affine Coordinate-Based Parametrization |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
Conference Article |
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2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
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1 |
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5-14 |
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Active Contours; Affine Coordinates; Mean Value Coordinates |
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In this paper, we present a new framework for image segmentation based on parametrized active contours. The contour and the points of the image space are parametrized using a set of reduced control points that have to form a closed polygon in two dimensional problems and a closed surface in three dimensional problems. By moving the control points, the active contour evolves. We use mean value coordinates as the parametrization tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon
or surface. Region-based energies such as the one proposed by Chan and Vese can be easily implemented in both two and three dimensional segmentation problems. We show the usefulness of our approach with several experiments. |
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Lisboa; January 2014 |
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VISAPP |
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OR;MILAB |
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no |
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Admin @ si @ XIB2014 |
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2452 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Abstract |
Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Incremental Domain Adaptation of Deformable Part-based Models |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2014 |
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25th British Machine Vision Conference |
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Pedestrian Detection; Part-based models; Domain Adaptation |
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Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
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Nottingham; uk; September 2014 |
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BMVA Press |
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Valstar, Michel and French, Andrew and Pridmore, Tony |
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ADAS; 600.057; 600.054; 600.076 |
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XRV2014c; ADAS @ adas @ xrv2014c |
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2455 |
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Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester |
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Anatomical parameterization for volumetric meshing of the liver |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2014 |
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SPIE – Medical Imaging |
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9036 |
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Coordinate System; Anatomy Modeling; Parameterization |
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A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values
at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites
of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the
volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient’s liver, and allows comparing livers from several patients in a common framework of reference. |
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Amsterdam; September 2014 |
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SPIE-MI |
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IAM; 600.075 |
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Admin @ si @ VGG2014 |
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2456 |
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