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Maurizio Mencuccini; Jordi Martinez-Vilalta; Josep Piñol; Lasse Loepfe; Mireia Burnat ; Xavier Alvarez; Juan Camacho; Debora Gil |
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Title |
A quantitative and statistically robust method for the determination of xylem conduit spatial distribution |
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Journal Article |
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2010 |
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American Journal of Botany |
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AJB |
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97 |
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8 |
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1247-1259 |
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Geyer; hydraulic conductivity; point pattern analysis; Ripley; Spatstat; vessel clusters; xylem anatomy; xylem network |
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Abstract |
Premise of the study: Because of their limited length, xylem conduits need to connect to each other to maintain water transport from roots to leaves. Conduit spatial distribution in a cross section plays an important role in aiding this connectivity. While indices of conduit spatial distribution already exist, they are not well defined statistically. * Methods: We used point pattern analysis to derive new spatial indices. One hundred and five cross-sectional images from different species were transformed into binary images. The resulting point patterns, based on the locations of the conduit centers-of-area, were analyzed to determine whether they departed from randomness. Conduit distribution was then modeled using a spatially explicit stochastic model. * Key results: The presence of conduit randomness, uniformity, or aggregation depended on the spatial scale of the analysis. The large majority of the images showed patterns significantly different from randomness at least at one spatial scale. A strong phylogenetic signal was detected in the spatial variables. * Conclusions: Conduit spatial arrangement has been largely conserved during evolution, especially at small spatial scales. Species in which conduits were aggregated in clusters had a lower conduit density compared to those with uniform distribution. Statistically sound spatial indices must be employed as an aid in the characterization of distributional patterns across species and in models of xylem water transport. Point pattern analysis is a very useful tool in identifying spatial patterns. |
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IAM; |
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IAM @ iam @ MMG2010 |
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1623 |
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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 |
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Neurogastroenterology & Motility |
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NEUMOT |
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27 |
Issue |
9 |
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1249-1257 |
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Abstract |
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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Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
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Conference Article |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop |
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1250 - 12507 |
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Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis |
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This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018d |
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3197 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Generic Subclass Ensemble: A Novel Approach to Ensemble Classification |
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Conference Article |
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2014 |
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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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Sergio Vera; Debora Gil; Agnes Borras; Marius George Linguraru; Miguel Angel Gonzalez Ballester |
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Title |
Geometric Steerable Medial Maps |
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Journal Article |
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2013 |
Publication |
Machine Vision and Applications |
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MVA |
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24 |
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6 |
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1255-1266 |
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Medial Representations ,Medial Manifolds Comparation , Surface , Reconstruction |
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In order to provide more intuitive and easily interpretable representations of complex shapes/organs, medial manifolds should reach a compromise between simplicity in geometry and capability for restoring the anatomy/shape of the organ/volume. Existing morphological methods show excellent results when applied to 2D objects, but their quality drops across dimensions.
This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoids degenerated medial axis segments. Second, we introduce a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to syn- thetic shapes of known medial geometry. We also show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume. |
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Springer Berlin Heidelberg |
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Mubarak Shah |
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0932-8092 |
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IAM; 605.203; 600.060; 600.044 |
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IAM @ iam @ VGB2013 |
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2192 |
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Mohammad Ali Bagheri; Gang Hu; Qigang Gao; Sergio Escalera |
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Title |
A Framework of Multi-Classifier Fusion for Human Action Recognition |
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Conference Article |
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2014 |
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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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Fosca De Iorio; C. Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
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Intestinal motor activity, endoluminal motion and transit |
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Journal Article |
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2009 |
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Neurogastroenterology & Motility |
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NEUMOT |
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21 |
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12 |
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1264–e119 |
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A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz |
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SDIT: Scalable and Diverse Cross-domain Image Translation |
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Conference Article |
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2019 |
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27th ACM International Conference on Multimedia |
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1267–1276 |
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Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. |
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Nice; Francia; October 2019 |
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ACM-MM |
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LAMP; 600.106; 600.109; 600.141; 600.120 |
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Admin @ si @ WGW2019 |
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3363 |
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Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Wall Patch-Based Segmentation in Architectural Floorplans |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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1270-1274 |
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Segmentation of architectural floor plans is a challenging task, mainly because of the large variability in the notation between different plans. In general, traditional techniques, usually based on analyzing and grouping structural primitives obtained by vectorization, are only able to handle a reduced range of similar notations. In this paper we propose an alternative patch-based segmentation approach working at pixel level, without need of vectorization. The image is divided into a set of patches and a set of features is extracted for every patch. Then, each patch is assigned to a visual word of a previously learned vocabulary and given a probability of belonging to each class of objects. Finally, a post-process assigns the final label for every pixel. This approach has been applied to the detection of walls on two datasets of architectural floor plans with different notations, achieving high accuracy rates. |
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Beiging, China |
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1520-5363 |
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978-0-7695-4520-2 |
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ICDAR |
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DAG |
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Admin @ si @ HMS2011a |
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1792 |
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Zhijie Fang; Antonio Lopez |
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Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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1271 - 1276 |
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Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ FaL2018 |
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3181 |
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Antonio Hernandez; Carlos Primo; Sergio Escalera |
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Automatic user interaction correction via Multi-label Graph cuts |
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2011 |
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In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV |
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1276-1281 |
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Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm. |
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978-1-4673-0062-9 |
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HICV |
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MILAB; HuPBA |
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Admin @ si @ HPE2011 |
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1892 |
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Partha Pratim Roy; Umapada Pal; Josep Llados |
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Document Seal Detection Using Ght and Character Proximity Graphs |
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Journal Article |
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2011 |
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Pattern Recognition |
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PR |
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44 |
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6 |
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1282-1295 |
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Seal recognition; Graphical symbol spotting; Generalized Hough transform; Multi-oriented character recognition |
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This paper deals with automatic detection of seal (stamp) from documents with cluttered background. Seal detection involves a difficult challenge due to its multi-oriented nature, arbitrary shape, overlapping of its part with signature, noise, etc. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors computed from recognition result of individual connected components (characters). Scale and rotation invariant features are used in a Support Vector Machine (SVM) classifier to recognize multi-scale and multi-oriented text characters. The concept of generalized Hough transform (GHT) is used to detect the seal and a voting scheme is designed for finding possible location of the seal in a document based on the spatial feature descriptor of neighboring component pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal in a document. Experiment is performed in an archive of historical documents of handwritten/printed English text. Experimental results show that the method is robust in locating seal instances of arbitrary shape and orientation in documents, and also efficient in indexing a collection of documents for retrieval purposes. |
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Elsevier |
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Admin @ si @ RPL2011 |
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1820 |
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Ekta Vats; Anders Hast; Alicia Fornes |
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Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1294-1299 |
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Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching |
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Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method. |
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Sydney; Australia; September 2019 |
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DAG; 600.140; 600.121 |
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Admin @ si @ VHF2019 |
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3356 |
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L. Rothacker; Marçal Rusiñol; G.A. Fink |
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Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1305 - 1309 |
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Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset. |
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Washington; USA; August 2013 |
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Admin @ si @ RRF2013 |
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2344 |
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Jose Seabra; Francesco Ciompi; Oriol Pujol; Josepa Mauri; Petia Radeva; Joao Sanchez |
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Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound |
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2011 |
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IEEE Transactions on Biomedical Engineering |
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TBME |
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58 |
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5 |
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1314-1324 |
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Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology. |
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MILAB;HuPBA |
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Admin @ si @ SCP2011 |
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1712 |
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