|
Records |
Links |
|
Author |
Daniel 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 |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey |
|
|
Title |
Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Journal of Archaeological Science |
Abbreviated Journal |
JArchSci |
|
|
Volume |
148 |
Issue |
|
Pages |
105654 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. |
|
|
Address |
December 2022 |
|
|
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; MACO; 600.167 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BOL2022 |
Serial |
3753 |
|
Permanent link to this record |
|
|
|
|
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 |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
|
|
Title |
LDC: Lightweight Dense CNN for Edge Detection |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
10 |
Issue |
|
Pages |
68281-68290 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
|
|
Address |
27 June 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE |
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; 600.160; 600.167 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SPS2022 |
Serial |
3751 |
|
Permanent link to this record |
|
|
|
|
Author |
Armin Mehri; Parichehr Behjati; Angel Sappa |
|
|
Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution |
Type |
Journal Article |
|
Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
11 |
Issue |
|
Pages |
11529-11540 |
|
|
Keywords |
|
|
|
Abstract |
Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient. |
|
|
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 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MBS2023 |
Serial |
3876 |
|
Permanent link to this record |