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Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Visible-Thermal Fusion based Monocular Visual Odometry |
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Conference Article |
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Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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517-528 |
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Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. |
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The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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Lisboa; Portugal; November 2015 |
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Springer International Publishing |
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2194-5357 |
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978-3-319-27145-3 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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no |
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Admin @ si @ PAD2015 |
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2663 |
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Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
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Conference Article |
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2015 |
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International Conference on Intelligent Robots and Systems |
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2488 - 2495 |
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Visual Learning; Computer Vision; Autonomous Agents |
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In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
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Hamburg; Germany; October 2015 |
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IROS |
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ADAS; 600.076 |
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Admin @ si @ OSL2015 |
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2664 |
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Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz |
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Title |
Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis |
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Journal Article |
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Year |
2015 |
Publication |
American Journal of Physiology-Gastrointestinal and Liver Physiology |
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AJPGI |
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309 |
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6 |
Pages |
G413--G419 |
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capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning |
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We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. |
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American Physiological Society |
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MILAB; OR;MV |
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no |
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Admin @ si @ MDS2015 |
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2666 |
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R.A.Bendezu; E.Barba; E.Burri; D.Cisternas; Carolina Malagelada; Santiago Segui; Anna Accarino; S.Quiroga; E.Monclus; I.Navazo |
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Title |
Intestinal gas content and distribution in health and in patients with functional gut symptoms |
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Journal Article |
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2015 |
Publication |
Neurogastroenterology & Motility |
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NEUMOT |
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27 |
Issue |
9 |
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1249-1257 |
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BACKGROUND:
The precise relation of intestinal gas to symptoms, particularly abdominal bloating and distension remains incompletely elucidated. Our aim was to define the normal values of intestinal gas volume and distribution and to identify abnormalities in relation to functional-type symptoms.
METHODS:
Abdominal computed tomography scans were evaluated in healthy subjects (n = 37) and in patients in three conditions: basal (when they were feeling well; n = 88), during an episode of abdominal distension (n = 82) and after a challenge diet (n = 24). Intestinal gas content and distribution were measured by an original analysis program. Identification of patients outside the normal range was performed by machine learning techniques (one-class classifier). Results are expressed as median (IQR) or mean ± SE, as appropriate.
KEY RESULTS:
In healthy subjects the gut contained 95 (71, 141) mL gas distributed along the entire lumen. No differences were detected between patients studied under asymptomatic basal conditions and healthy subjects. However, either during a spontaneous bloating episode or once challenged with a flatulogenic diet, luminal gas was found to be increased and/or abnormally distributed in about one-fourth of the patients. These patients detected outside the normal range by the classifier exhibited a significantly greater number of abnormal features than those within the normal range (3.7 ± 0.4 vs 0.4 ± 0.1; p < 0.001).
CONCLUSIONS & INFERENCES:
The analysis of a large cohort of subjects using original techniques provides unique and heretofore unavailable information on the volume and distribution of intestinal gas in normal conditions and in relation to functional gastrointestinal symptoms. |
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MILAB |
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Admin @ si @ BBB2015 |
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2667 |
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Author |
Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez |
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Title |
Recognizing Actions through Action-specific Person Detection |
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Journal Article |
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Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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Volume |
24 |
Issue |
11 |
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4422-4432 |
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Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test tim- , outperforms on both data sets state-of-the-art methods, which do use person locations. |
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1057-7149 |
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ADAS; LAMP; 600.076; 600.079 |
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Admin @ si @ KXR2015 |
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2668 |
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G.Blasco; Simone Balocco; J.Puig; J.Sanchez-Gonzalez; W.Ricart; J.Daunis-I-Estadella; X.Molina; S.Pedraza; J.M.Fernandez-Real |
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Title |
Carotid pulse wave velocity by magnetic resonance imaging is increased in middle-aged subjects with the metabolic syndrome |
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Journal Article |
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2015 |
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International Journal of Cardiovascular Imaging |
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ICJI |
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31 |
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3 |
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603-612 |
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Metabolic syndrome; Arterial stiffness; Pulse wave velocity; Carotid artery; Magnetic resonance |
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Arterial pulse wave velocity (PWV), an independent predictor of cardiovascular disease, physiologically increases with age; however, growing evidence suggests metabolic syndrome (MetS) accelerates this increase. Magnetic resonance imaging (MRI) enables reliable noninvasive assessment of arterial stiffness by measuring arterial PWV in specific vascular segments. We investigated the association between the presence of MetS and its components with carotid PWV (cPWV) in asymptomatic subjects without diabetes. We assessed cPWV by MRI in 61 individuals (mean age, 55.3 ± 14.1 years; median age, 55 years): 30 with MetS and 31 controls with similar age, sex, body mass index, and LDL-cholesterol levels. The study population was dichotomized by the median age. To remove the physiological association between PWV and age, unpaired t tests and multiple regression analyses were performed using the residuals of the regression between PWV and age. cPWV was higher in middle-aged subjects with MetS than in those without (p = 0.001), but no differences were found in elder subjects (p = 0.313). cPWV was associated with diastolic blood pressure (r = 0.276, p = 0.033) and waist circumference (r = 0.268, p = 0.038). The presence of MetS was associated with increased cPWV regardless of age, sex, blood pressure, and waist (p = 0.007). The MetS components contributing independently to an increased cPWV were hypertension (p = 0.018) and hypertriglyceridemia (p = 0.002). The presence of MetS is associated with an increased cPWV in middle-aged subjects. In particular, hypertension and hypertriglyceridemia may contribute to early progression of carotid stiffness. |
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Springer Netherlands |
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1569-5794 |
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MILAB |
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Admin @ si @ BBP2015 |
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2670 |
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Author |
Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
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Title |
From emotions to action units with hidden and semi-hidden-task learning |
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Conference Article |
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2015 |
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16th IEEE International Conference on Computer Vision |
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3703-3711 |
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Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units)for which samples are not available but, in contrast, training data is easier to obtain from a set of related VisibleTasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training. |
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Santiago de Chile; Chile; December 2015 |
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ICCV |
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LAMP; 600.068; 600.079 |
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Admin @ si @ RWB2015 |
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2671 |
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Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
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Title |
Deep semantic pyramids for human attributes and action recognition |
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Conference Article |
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2015 |
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Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 |
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9127 |
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341-353 |
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Action recognition; Human attributes; Semantic pyramids |
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Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature. |
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Denmark; Copenhagen; June 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-19664-0 |
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SCIA |
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LAMP; 600.068; 600.079;ADAS |
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Admin @ si @ KRW2015b |
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2672 |
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Lluis Garrido; M.Guerrieri; Laura Igual |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Image Segmentation with Cage Active Contours |
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Journal Article |
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2015 |
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IEEE Transactions on Image Processing |
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TIP |
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24 |
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12 |
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5557 - 5566 |
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Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates |
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In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. |
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MILAB |
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Admin @ si @ GGI2015 |
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2673 |
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Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual |
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FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization |
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2015 |
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Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 |
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61-68 |
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Munich; Germany; October 2015 |
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MLMI |
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MILAB |
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Admin @ si @ NSJ2015 |
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2674 |
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Chen Zhang; Maria del Mar Vila Muñoz; Petia Radeva; Roberto Elosua; Maria Grau; Angels Betriu; Elvira Fernandez-Giraldez; Laura Igual |
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Carotid Artery Segmentation in Ultrasound Images |
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2015 |
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Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT2015), Joint MICCAI Workshops |
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Munich; Germany; October 2015 |
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CVII-STENT |
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MILAB |
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Admin @ si @ ZVR2015 |
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2675 |
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Author |
Onur Ferhat; Arcadi Llanza; Fernando Vilariño |
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Title |
Gaze interaction for multi-display systems using natural light eye-tracker |
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Conference Article |
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2015 |
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2nd International Workshop on Solutions for Automatic Gaze Data Analysis |
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Bielefeld; Germany; September 2015 |
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SAGA |
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MV;SIAI |
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Admin @ si @ FLV2015b |
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2676 |
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Martha Mackay; Fernando Alonso; Pere Salamero; Xavier Baro; Jordi Gonzalez; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Care and caring: future proofing the new demographics |
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2015 |
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6th International Carers Conference |
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With an ageing population, the issue of care provision is becoming increasingly important. The simple aspiration of the majority of older people is to live safely and well at home. Housing will be part of health & care integration in the following years and decades. A higher proportion of people will have to rely on informal care through family, friends, neighbors and others who
provide care to an older person in need of assistance (around 80% of care across the EU). They do not usually have a formal status and are usually unpaid. We need to ensure that all disabled or chronically ill people can get the help they need without overburdening their families.
The physical and emotional stress of carers is one of the dangers that this dependency can bring. To prevent carers burnout it is necessary to provide new solutions that are affordable and user friendly for the families and caregivers. |
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Gothenburg; Sweden; September 2015 |
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CARERS |
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HuPBA; ISE; 600.078;MV |
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Admin @ si @ MAS2015b |
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2678 |
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David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting |
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2015 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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18 |
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3 |
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223-234 |
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Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation |
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The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 |
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Admin @ si @ ART2015 |
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2679 |
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J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier |
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Title |
Improving Document Matching Performance by Local Descriptor Filtering |
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2015 |
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6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 |
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1216 - 1220 |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Nancy; France; August 2015 |
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CBDAR |
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DAG; 600.077; 601.223; 600.084 |
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Admin @ si @ CRO2015a |
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2680 |
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