| |
Records |
Links |
|
Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
|
| |
Title |
Camera pose estimation in multi-view environments: From virtual scenarios to the real world |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IVC |
|
| |
Volume  |
110 |
Issue |
|
Pages |
104182 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios. |
|
| |
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 |
MSIAU; 600.130; 600.122 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CSV2021 |
Serial |
3577 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
|
| |
Title |
myStone: A system for automatic kidney stone classification |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
ESA |
|
| |
Volume |
89 |
Issue |
|
Pages |
41-51 |
|
| |
Keywords |
Kidney stone; Optical device; Computer vision; Image classification |
|
| |
Abstract |
Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
|
| |
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; MSIAU; 603.046; 600.122; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SLB2017 |
Serial |
3026 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |
|
| |
Title |
Segmentation of aerial images for plausible detail synthesis |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
|
| |
Volume |
71 |
Issue |
|
Pages |
23-34 |
|
| |
Keywords |
Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
|
| |
Abstract |
The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
|
| |
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 |
0097-8493 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MSIAU; 600.086; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ACC2018 |
Serial |
3147 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Daniela Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa |
|
| |
Title |
A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Journal of Manufacturing Systems |
Abbreviated Journal |
JMANUFSYST |
|
| |
Volume |
64 |
Issue |
|
Pages |
497-507 |
|
| |
Keywords |
Calibration; Collaborative cell; Multi-modal; Multi-sensor |
|
| |
Abstract |
Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Science Direct |
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 |
MSIAU; MACO |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ROS2022 |
Serial |
3750 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
|
| |
Title |
Detailed 3D face reconstruction from a single RGB image |
Type |
Journal |
| |
Year |
2019 |
Publication |
Journal of WSCG |
Abbreviated Journal |
JWSCG |
|
| |
Volume |
27 |
Issue |
2 |
Pages |
103-112 |
|
| |
Keywords |
3D Wrinkle Reconstruction; Face Analysis, Optimization. |
|
| |
Abstract |
This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles. |
|
| |
Address |
2019/11 |
|
| |
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 |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3708 |
|
| Permanent link to this record |
