| |
Records |
Links |
|
Author |
Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
|
| |
Title  |
Generic Feature Learning for Wireless Capsule Endoscopy Analysis |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
|
| |
Volume |
79 |
Issue |
|
Pages |
163-172 |
|
| |
Keywords |
Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis |
|
| |
Abstract |
The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase). |
|
| |
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; MILAB;MV; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SDP2016 |
Serial |
2836 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oriol Pujol; David Masip |
|
| |
Title |
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
31 |
Issue |
6 |
Pages |
1140–1146 |
|
| |
Keywords |
|
|
| |
Abstract |
This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention |
|
| |
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;HuPBA;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ PuM2009 |
Serial |
1252 |
|
| 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 |
M. Bressan; Jordi Vitria |
|
| |
Title |
Independent Component Analysis and Naïve Bayes Classification. |
Type |
Journal Article |
| |
Year |
2002 |
Publication |
Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501. |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ BrV2002a |
Serial |
288 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
M. Bressan; Jordi Vitria |
|
| |
Title |
Independent Feature Selection |
Type |
Journal |
| |
Year |
2003 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): 1312–1317 (IF: 3.823) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
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 |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ BrV2003a |
Serial |
366 |
|
| Permanent link to this record |
