| |
Records |
Links |
|
Author |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
|
| |
Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Journal of Healthcare Engineering |
Abbreviated Journal |
JHCE |
|
| |
Volume |
|
Issue |
|
Pages |
2040-2295 |
|
| |
Keywords |
Colonoscopy images; Deep Learning; Semantic Segmentation |
|
| |
Abstract |
Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. |
|
| |
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  |
ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
Approved |
no |
|
| |
Call Number |
VBS2017b |
Serial |
2940 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez |
|
| |
Title |
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” |
Abbreviated Journal |
SENS |
|
| |
Volume |
23 |
Issue |
2 |
Pages |
621 |
|
| |
Keywords |
Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving |
|
| |
Abstract |
Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall
procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our
procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results. |
|
| |
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 |
ADAS; no proj |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GVL2023 |
Serial |
3705 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
|
| |
Title |
Feature Point Descriptors: Infrared and Visible Spectra |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
14 |
Issue |
2 |
Pages |
3690-3701 |
|
| |
Keywords |
|
|
| |
Abstract |
This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. |
|
| |
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 |
ADAS;600.055; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RCA2014a |
Serial |
2474 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
|
| |
Title |
Monocular visual odometry: A cross-spectral image fusion based approach |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
|
| |
Volume |
85 |
Issue |
|
Pages |
26-36 |
|
| |
Keywords |
Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion |
|
| |
Abstract |
This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier B.V. |
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 |
ADAS;600.086; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @SAC2016 |
Serial |
2811 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
|
| |
Title |
Real-time quality control of surgical material packaging by artificial vision |
Type |
Journal Article |
| |
Year |
2005 |
Publication |
Assembly Automation |
Abbreviated Journal |
|
|
| |
Volume |
25 |
Issue |
3 |
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
IF: 0.061) |
|
| |
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 |
ADAS;DAG |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ LVV2005 |
Serial |
552 |
|
| Permanent link to this record |
