| |
Records |
Links |
|
Author |
Rozenn Dhayot; Fernando Vilariño; Gerard Lacey |
|
| |
Title |
Improving the Quality of Color Colonoscopy Videos |
Type |
Journal Article |
| |
Year |
2008 |
Publication |
EURASIP Journal on Image and Video Processing |
Abbreviated Journal |
EURASIP JIVP |
|
| |
Volume  |
139429 |
Issue |
1 |
Pages |
1-9 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
800 |
Expedition |
|
Conference |
|
|
| |
Notes |
MV;SIAI |
Approved |
no |
|
| |
Call Number |
fernando @ fernando @ |
Serial |
2422 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey |
|
| |
Title |
Automatic segmentation and inpainting of specular highlights for endoscopic imaging |
Type |
Journal Article |
| |
Year |
2010 |
Publication |
EURASIP Journal on Image and Video Processing |
Abbreviated Journal |
EURASIP JIVP |
|
| |
Volume |
2010 |
Issue |
9 |
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
800 |
Expedition |
|
Conference |
|
|
| |
Notes |
MV |
Approved |
no |
|
| |
Call Number |
fernando @ fernando @ |
Serial |
2423 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz |
|
| |
Title |
Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
American Journal of Physiology-Gastrointestinal and Liver Physiology |
Abbreviated Journal |
AJPGI |
|
| |
Volume |
309 |
Issue |
6 |
Pages |
G413--G419 |
|
| |
Keywords |
capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning |
|
| |
Abstract |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
American Physiological Society |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; OR;MV |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MDS2015 |
Serial |
2666 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza de Luna; Joaquin Salas |
|
| |
Title |
Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
|
| |
Volume |
175 |
Issue |
B |
Pages |
866–876 |
|
| |
Keywords |
Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices |
|
| |
Abstract |
During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR; 600.072; 600.068;MV |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TRM2016 |
Serial |
2721 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
|
| |
Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Computers & Industrial Engineering |
Abbreviated Journal |
CIE |
|
| |
Volume |
94 |
Issue |
|
Pages |
93-104 |
|
| |
Keywords |
Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
|
| |
Abstract |
In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
PERGAMON-ELSEVIER SCIENCE LTD |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
CIE |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0360-8352 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;MV; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CFG2016 |
Serial |
2749 |
|
| Permanent link to this record |
